model.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "view-in-github"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/sorayutmild/loan-default-prediction/blob/main/mini_project.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "WZfjTaNXgKP6"
      },
      "outputs": [],
      "source": [
        "import warnings\n",
        "warnings.filterwarnings(\"ignore\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "nqqcZ_VObK2z"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
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        },
        "id": "QJFhDSTV5Kih",
        "outputId": "281feb21-3c7a-4bc6-cbe8-d3fa8a41072a"
      },
      "outputs": [
        {
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>account_id</th>\n",
              "      <th>loan_amount</th>\n",
              "      <th>loan_duration</th>\n",
              "      <th>loan_payments</th>\n",
              "      <th>loan_status</th>\n",
              "      <th>acc_frequency</th>\n",
              "      <th>day_before_loan</th>\n",
              "      <th>sum_monthly_payment_amount</th>\n",
              "      <th>sum_monthly_payment_amount_in</th>\n",
              "      <th>sum_monthly_payment_amount_out</th>\n",
              "      <th>...</th>\n",
              "      <th>amount_last_mount_out_per_loan_amount</th>\n",
              "      <th>amount_last_3months_avg</th>\n",
              "      <th>amount_last_3months_in_avg</th>\n",
              "      <th>amount_last_3months_out_avg</th>\n",
              "      <th>amount_last_3months_per_loan_payments</th>\n",
              "      <th>amount_last_3months_in_per_loan_payments</th>\n",
              "      <th>amount_last_3months_out_per_loan_payments</th>\n",
              "      <th>amount_last_3months_per_loan_amount</th>\n",
              "      <th>amount_last_3months_in_per_loan_amount</th>\n",
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              "      <th>0</th>\n",
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              "      <td>24</td>\n",
              "      <td>3373</td>\n",
              "      <td>A</td>\n",
              "      <td>monthly</td>\n",
              "      <td>313</td>\n",
              "      <td>120.482428</td>\n",
              "      <td>4.124601</td>\n",
              "      <td>116.357827</td>\n",
              "      <td>...</td>\n",
              "      <td>0.425894</td>\n",
              "      <td>44114.333333</td>\n",
              "      <td>23734.666667</td>\n",
              "      <td>20379.666667</td>\n",
              "      <td>13.078664</td>\n",
              "      <td>7.036664</td>\n",
              "      <td>6.042000</td>\n",
              "      <td>0.544944</td>\n",
              "      <td>0.293194</td>\n",
              "      <td>0.251750</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>19</td>\n",
              "      <td>30276</td>\n",
              "      <td>12</td>\n",
              "      <td>2523</td>\n",
              "      <td>B</td>\n",
              "      <td>monthly</td>\n",
              "      <td>388</td>\n",
              "      <td>335.966495</td>\n",
              "      <td>7.306701</td>\n",
              "      <td>328.659794</td>\n",
              "      <td>...</td>\n",
              "      <td>0.601632</td>\n",
              "      <td>36373.333333</td>\n",
              "      <td>17858.333333</td>\n",
              "      <td>18515.000000</td>\n",
              "      <td>14.416700</td>\n",
              "      <td>7.078214</td>\n",
              "      <td>7.338486</td>\n",
              "      <td>1.201392</td>\n",
              "      <td>0.589851</td>\n",
              "      <td>0.611540</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>25</td>\n",
              "      <td>30276</td>\n",
              "      <td>12</td>\n",
              "      <td>2523</td>\n",
              "      <td>A</td>\n",
              "      <td>monthly</td>\n",
              "      <td>498</td>\n",
              "      <td>204.881526</td>\n",
              "      <td>8.206827</td>\n",
              "      <td>196.674699</td>\n",
              "      <td>...</td>\n",
              "      <td>3.150482</td>\n",
              "      <td>115321.666667</td>\n",
              "      <td>53753.000000</td>\n",
              "      <td>61568.666667</td>\n",
              "      <td>45.708152</td>\n",
              "      <td>21.305192</td>\n",
              "      <td>24.402959</td>\n",
              "      <td>3.809013</td>\n",
              "      <td>1.775433</td>\n",
              "      <td>2.033580</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>37</td>\n",
              "      <td>318480</td>\n",
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              "      <td>5308</td>\n",
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              "      <td>422</td>\n",
              "      <td>74.492891</td>\n",
              "      <td>5.457346</td>\n",
              "      <td>69.035545</td>\n",
              "      <td>...</td>\n",
              "      <td>0.103344</td>\n",
              "      <td>60867.000000</td>\n",
              "      <td>30319.333333</td>\n",
              "      <td>30547.666667</td>\n",
              "      <td>11.467031</td>\n",
              "      <td>5.712007</td>\n",
              "      <td>5.755024</td>\n",
              "      <td>0.191117</td>\n",
              "      <td>0.095200</td>\n",
              "      <td>0.095917</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>38</td>\n",
              "      <td>110736</td>\n",
              "      <td>48</td>\n",
              "      <td>2307</td>\n",
              "      <td>C</td>\n",
              "      <td>weekly</td>\n",
              "      <td>254</td>\n",
              "      <td>117.125984</td>\n",
              "      <td>4.385827</td>\n",
              "      <td>112.740157</td>\n",
              "      <td>...</td>\n",
              "      <td>0.089312</td>\n",
              "      <td>29212.333333</td>\n",
              "      <td>16590.333333</td>\n",
              "      <td>12622.000000</td>\n",
              "      <td>12.662477</td>\n",
              "      <td>7.191302</td>\n",
              "      <td>5.471175</td>\n",
              "      <td>0.263802</td>\n",
              "      <td>0.149819</td>\n",
              "      <td>0.113983</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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              "<p>5 rows × 72 columns</p>\n",
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              "                                                     [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",
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            "text/plain": [
              "   account_id  loan_amount  loan_duration  loan_payments loan_status  \\\n",
              "0           2        80952             24           3373           A   \n",
              "1          19        30276             12           2523           B   \n",
              "2          25        30276             12           2523           A   \n",
              "3          37       318480             60           5308           D   \n",
              "4          38       110736             48           2307           C   \n",
              "\n",
              "  acc_frequency  day_before_loan  sum_monthly_payment_amount  \\\n",
              "0       monthly              313                  120.482428   \n",
              "1       monthly              388                  335.966495   \n",
              "2       monthly              498                  204.881526   \n",
              "3       monthly              422                   74.492891   \n",
              "4        weekly              254                  117.125984   \n",
              "\n",
              "   sum_monthly_payment_amount_in  sum_monthly_payment_amount_out  ...  \\\n",
              "0                       4.124601                      116.357827  ...   \n",
              "1                       7.306701                      328.659794  ...   \n",
              "2                       8.206827                      196.674699  ...   \n",
              "3                       5.457346                       69.035545  ...   \n",
              "4                       4.385827                      112.740157  ...   \n",
              "\n",
              "   amount_last_mount_out_per_loan_amount  amount_last_3months_avg  \\\n",
              "0                               0.425894             44114.333333   \n",
              "1                               0.601632             36373.333333   \n",
              "2                               3.150482            115321.666667   \n",
              "3                               0.103344             60867.000000   \n",
              "4                               0.089312             29212.333333   \n",
              "\n",
              "   amount_last_3months_in_avg  amount_last_3months_out_avg  \\\n",
              "0                23734.666667                 20379.666667   \n",
              "1                17858.333333                 18515.000000   \n",
              "2                53753.000000                 61568.666667   \n",
              "3                30319.333333                 30547.666667   \n",
              "4                16590.333333                 12622.000000   \n",
              "\n",
              "   amount_last_3months_per_loan_payments  \\\n",
              "0                              13.078664   \n",
              "1                              14.416700   \n",
              "2                              45.708152   \n",
              "3                              11.467031   \n",
              "4                              12.662477   \n",
              "\n",
              "   amount_last_3months_in_per_loan_payments  \\\n",
              "0                                  7.036664   \n",
              "1                                  7.078214   \n",
              "2                                 21.305192   \n",
              "3                                  5.712007   \n",
              "4                                  7.191302   \n",
              "\n",
              "   amount_last_3months_out_per_loan_payments  \\\n",
              "0                                   6.042000   \n",
              "1                                   7.338486   \n",
              "2                                  24.402959   \n",
              "3                                   5.755024   \n",
              "4                                   5.471175   \n",
              "\n",
              "   amount_last_3months_per_loan_amount  \\\n",
              "0                             0.544944   \n",
              "1                             1.201392   \n",
              "2                             3.809013   \n",
              "3                             0.191117   \n",
              "4                             0.263802   \n",
              "\n",
              "   amount_last_3months_in_per_loan_amount  \\\n",
              "0                                0.293194   \n",
              "1                                0.589851   \n",
              "2                                1.775433   \n",
              "3                                0.095200   \n",
              "4                                0.149819   \n",
              "\n",
              "   amount_last_3months_out_per_loan_amount  \n",
              "0                                 0.251750  \n",
              "1                                 0.611540  \n",
              "2                                 2.033580  \n",
              "3                                 0.095917  \n",
              "4                                 0.113983  \n",
              "\n",
              "[5 rows x 72 columns]"
            ]
          },
          "execution_count": 3,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "url ='https://github.com/sorayutmild/loan-default-prediction/blob/main/transformed_data/final_transformed_data.csv?raw=true'\n",
        "raw_df = pd.read_csv(url)\n",
        "raw_df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "_PI1yfWxlMsO"
      },
      "outputs": [],
      "source": [
        "#ref = https://www.in2013dollars.com/czechia/inflation/1999?amount=100\n",
        "rate = 1.44724 # 1 CZK = 1.44724 THB\n",
        "name_chang_rate = ['loan_amount', 'loan_payments', 'sum_monthly_payment_amount',\n",
        "       'sum_monthly_payment_amount_in', 'sum_monthly_payment_amount_out',\n",
        "       'sum_monthly_payment_out_household',\n",
        "       'sum_monthly_payment_out_insurrance',\n",
        "       'sum_monthly_payment_in_interest_credited',\n",
        "       'sum_monthly_payment_out_payment_for_statement',\n",
        "       'sum_monthly_payment_out_sanction_interest_neg_bal',\n",
        "        'current_monthly_payment_amount_out',\n",
        "       'current_monthly_payment_out_household',\n",
        "       'current_monthly_payment_out_insurrance',\n",
        "       'current_monthly_payment_out_payment_for_statement',\n",
        "       'current_monthly_payment_in_interest_credited',\n",
        "        'sum_other_amount', 'sum_other_amount_in', 'sum_other_amount_out',\n",
        "       'avg_salary', 'balance_latest', \n",
        "       ]\n",
        "\n",
        "#Czechia Inflation Calculator\n",
        "CPI_1999\t= 69.750\n",
        "CPI_2022\t= 126.367\n",
        "#raw_df[name_chang_rate] = raw_df[name_chang_rate]*(CPI_2022/CPI_1999)\n",
        "\n",
        "# CZK -> THB\n",
        "#raw_df[name_chang_rate] = raw_df[name_chang_rate]*rate\n",
        "\n",
        "#raw_df.head(5)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
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        "id": "PNaF3uBX6N2j",
        "outputId": "153cb25a-a498-4429-ea79-4441168916d6"
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>loan_amount</th>\n",
              "      <th>loan_duration</th>\n",
              "      <th>loan_payments</th>\n",
              "      <th>acc_frequency</th>\n",
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              "      <th>sum_monthly_payment_out_household</th>\n",
              "      <th>sum_monthly_payment_out_insurrance</th>\n",
              "      <th>...</th>\n",
              "      <th>amount_last_3months_avg</th>\n",
              "      <th>amount_last_3months_in_avg</th>\n",
              "      <th>amount_last_3months_out_avg</th>\n",
              "      <th>amount_last_3months_per_loan_payments</th>\n",
              "      <th>amount_last_3months_in_per_loan_payments</th>\n",
              "      <th>amount_last_3months_out_per_loan_payments</th>\n",
              "      <th>amount_last_3months_per_loan_amount</th>\n",
              "      <th>amount_last_3months_in_per_loan_amount</th>\n",
              "      <th>amount_last_3months_out_per_loan_amount</th>\n",
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              "      <td>7.036664</td>\n",
              "      <td>6.042000</td>\n",
              "      <td>0.544944</td>\n",
              "      <td>0.293194</td>\n",
              "      <td>0.251750</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>30276</td>\n",
              "      <td>12</td>\n",
              "      <td>2523</td>\n",
              "      <td>monthly</td>\n",
              "      <td>388</td>\n",
              "      <td>335.966495</td>\n",
              "      <td>7.306701</td>\n",
              "      <td>328.659794</td>\n",
              "      <td>328.350515</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>...</td>\n",
              "      <td>36373.333333</td>\n",
              "      <td>17858.333333</td>\n",
              "      <td>18515.000000</td>\n",
              "      <td>14.416700</td>\n",
              "      <td>7.078214</td>\n",
              "      <td>7.338486</td>\n",
              "      <td>1.201392</td>\n",
              "      <td>0.589851</td>\n",
              "      <td>0.611540</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>30276</td>\n",
              "      <td>12</td>\n",
              "      <td>2523</td>\n",
              "      <td>monthly</td>\n",
              "      <td>498</td>\n",
              "      <td>204.881526</td>\n",
              "      <td>8.206827</td>\n",
              "      <td>196.674699</td>\n",
              "      <td>191.642570</td>\n",
              "      <td>4.610442</td>\n",
              "      <td>...</td>\n",
              "      <td>115321.666667</td>\n",
              "      <td>53753.000000</td>\n",
              "      <td>61568.666667</td>\n",
              "      <td>45.708152</td>\n",
              "      <td>21.305192</td>\n",
              "      <td>24.402959</td>\n",
              "      <td>3.809013</td>\n",
              "      <td>1.775433</td>\n",
              "      <td>2.033580</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>318480</td>\n",
              "      <td>60</td>\n",
              "      <td>5308</td>\n",
              "      <td>monthly</td>\n",
              "      <td>422</td>\n",
              "      <td>74.492891</td>\n",
              "      <td>5.457346</td>\n",
              "      <td>69.035545</td>\n",
              "      <td>65.886256</td>\n",
              "      <td>2.758294</td>\n",
              "      <td>...</td>\n",
              "      <td>60867.000000</td>\n",
              "      <td>30319.333333</td>\n",
              "      <td>30547.666667</td>\n",
              "      <td>11.467031</td>\n",
              "      <td>5.712007</td>\n",
              "      <td>5.755024</td>\n",
              "      <td>0.191117</td>\n",
              "      <td>0.095200</td>\n",
              "      <td>0.095917</td>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>110736</td>\n",
              "      <td>48</td>\n",
              "      <td>2307</td>\n",
              "      <td>weekly</td>\n",
              "      <td>254</td>\n",
              "      <td>117.125984</td>\n",
              "      <td>4.385827</td>\n",
              "      <td>112.740157</td>\n",
              "      <td>108.677165</td>\n",
              "      <td>3.590551</td>\n",
              "      <td>...</td>\n",
              "      <td>29212.333333</td>\n",
              "      <td>16590.333333</td>\n",
              "      <td>12622.000000</td>\n",
              "      <td>12.662477</td>\n",
              "      <td>7.191302</td>\n",
              "      <td>5.471175</td>\n",
              "      <td>0.263802</td>\n",
              "      <td>0.149819</td>\n",
              "      <td>0.113983</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 71 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-71daa09e-0773-4738-bb82-eb9f5260be98')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\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=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert: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-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",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-71daa09e-0773-4738-bb82-eb9f5260be98 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-71daa09e-0773-4738-bb82-eb9f5260be98');\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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              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "   loan_amount  loan_duration  loan_payments acc_frequency  day_before_loan  \\\n",
              "0        80952             24           3373       monthly              313   \n",
              "1        30276             12           2523       monthly              388   \n",
              "2        30276             12           2523       monthly              498   \n",
              "3       318480             60           5308       monthly              422   \n",
              "4       110736             48           2307        weekly              254   \n",
              "\n",
              "   sum_monthly_payment_amount  sum_monthly_payment_amount_in  \\\n",
              "0                  120.482428                       4.124601   \n",
              "1                  335.966495                       7.306701   \n",
              "2                  204.881526                       8.206827   \n",
              "3                   74.492891                       5.457346   \n",
              "4                  117.125984                       4.385827   \n",
              "\n",
              "   sum_monthly_payment_amount_out  sum_monthly_payment_out_household  \\\n",
              "0                      116.357827                         116.070288   \n",
              "1                      328.659794                         328.350515   \n",
              "2                      196.674699                         191.642570   \n",
              "3                       69.035545                          65.886256   \n",
              "4                      112.740157                         108.677165   \n",
              "\n",
              "   sum_monthly_payment_out_insurrance  ...  amount_last_3months_avg  \\\n",
              "0                            0.000000  ...             44114.333333   \n",
              "1                            0.000000  ...             36373.333333   \n",
              "2                            4.610442  ...            115321.666667   \n",
              "3                            2.758294  ...             60867.000000   \n",
              "4                            3.590551  ...             29212.333333   \n",
              "\n",
              "   amount_last_3months_in_avg  amount_last_3months_out_avg  \\\n",
              "0                23734.666667                 20379.666667   \n",
              "1                17858.333333                 18515.000000   \n",
              "2                53753.000000                 61568.666667   \n",
              "3                30319.333333                 30547.666667   \n",
              "4                16590.333333                 12622.000000   \n",
              "\n",
              "   amount_last_3months_per_loan_payments  \\\n",
              "0                              13.078664   \n",
              "1                              14.416700   \n",
              "2                              45.708152   \n",
              "3                              11.467031   \n",
              "4                              12.662477   \n",
              "\n",
              "   amount_last_3months_in_per_loan_payments  \\\n",
              "0                                  7.036664   \n",
              "1                                  7.078214   \n",
              "2                                 21.305192   \n",
              "3                                  5.712007   \n",
              "4                                  7.191302   \n",
              "\n",
              "   amount_last_3months_out_per_loan_payments  \\\n",
              "0                                   6.042000   \n",
              "1                                   7.338486   \n",
              "2                                  24.402959   \n",
              "3                                   5.755024   \n",
              "4                                   5.471175   \n",
              "\n",
              "   amount_last_3months_per_loan_amount  \\\n",
              "0                             0.544944   \n",
              "1                             1.201392   \n",
              "2                             3.809013   \n",
              "3                             0.191117   \n",
              "4                             0.263802   \n",
              "\n",
              "   amount_last_3months_in_per_loan_amount  \\\n",
              "0                                0.293194   \n",
              "1                                0.589851   \n",
              "2                                1.775433   \n",
              "3                                0.095200   \n",
              "4                                0.149819   \n",
              "\n",
              "   amount_last_3months_out_per_loan_amount  default  \n",
              "0                                 0.251750    False  \n",
              "1                                 0.611540     True  \n",
              "2                                 2.033580    False  \n",
              "3                                 0.095917     True  \n",
              "4                                 0.113983    False  \n",
              "\n",
              "[5 rows x 71 columns]"
            ]
          },
          "execution_count": 5,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "raw_df['default'] = (raw_df['loan_status'] == 'B') | (raw_df['loan_status'] == 'D') # y => bad '1' , good '0'\n",
        "#aw_df['default']  = raw_df['default'].astype('float')\n",
        "\n",
        "raw_df['client_gender'] =  (raw_df['client_gender'] == 'M')\n",
        "raw_df['client_gender'] = raw_df['client_gender'].astype('float')\n",
        "\n",
        "account_id = pd.DataFrame(raw_df['account_id'])\n",
        "raw_df = raw_df.drop(['loan_status', 'account_id'], axis=1)\n",
        "raw_df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EdC3Z03z14kj",
        "outputId": "44fd2ebc-5aaf-4e79-92ef-ed4c536096e1"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Index(['loan_amount', 'loan_duration', 'loan_payments', 'acc_frequency',\n",
              "       'day_before_loan', 'sum_monthly_payment_amount',\n",
              "       'sum_monthly_payment_amount_in', 'sum_monthly_payment_amount_out',\n",
              "       'sum_monthly_payment_out_household',\n",
              "       'sum_monthly_payment_out_insurrance',\n",
              "       'sum_monthly_payment_in_interest_credited',\n",
              "       'sum_monthly_payment_out_payment_for_statement',\n",
              "       'sum_monthly_payment_out_sanction_interest_neg_bal',\n",
              "       'count_out_sanction_interest_neg_bal', 'count_monthly_payment_trans',\n",
              "       'current_monthly_payment_amount_out',\n",
              "       'current_monthly_payment_out_household',\n",
              "       'current_monthly_payment_out_insurrance',\n",
              "       'current_monthly_payment_out_payment_for_statement',\n",
              "       'current_monthly_payment_in_interest_credited', 'sum_other_amount',\n",
              "       'sum_other_amount_in', 'sum_other_amount_out', 'count_other_trans',\n",
              "       'balance_latest', 'client_gender', 'client_age', 'all_client_mean_age',\n",
              "       'all_client_count', 'card_type_classic', 'card_type_gold',\n",
              "       'card_type_junior', 'num_inhabitants', 'urban_rate', 'avg_salary',\n",
              "       'num_enterpreneurs_per1000inhabitants', 'unemploy_rate',\n",
              "       'number_crimes', 'balance_mean', 'balance_min', 'balance_max',\n",
              "       'balance_min_last_month', 'balance_max_last_month',\n",
              "       'balance_mean_last_month', 'balance_min_last_3months',\n",
              "       'balance_max_last_3months', 'balance_mean_last_3months',\n",
              "       'balance_mean_per_loan_payment',\n",
              "       'balance_mean_last_month_per_loan_payment',\n",
              "       'balance_mean_per_loan_amount',\n",
              "       'balance_mean_last_month_per_loan_amount', 'growth_balance',\n",
              "       'amount_last_month', 'amount_last_month_in', 'amount_last_month_out',\n",
              "       'amount_last_month_per_loan_payments',\n",
              "       'amount_last_mount_in_per_loan_payments',\n",
              "       'amount_last_mount_out_per_loan_payments',\n",
              "       'amount_last_month_per_loan_amount',\n",
              "       'amount_last_mount_in_per_loan_amount',\n",
              "       'amount_last_mount_out_per_loan_amount', 'amount_last_3months_avg',\n",
              "       'amount_last_3months_in_avg', 'amount_last_3months_out_avg',\n",
              "       'amount_last_3months_per_loan_payments',\n",
              "       'amount_last_3months_in_per_loan_payments',\n",
              "       'amount_last_3months_out_per_loan_payments',\n",
              "       'amount_last_3months_per_loan_amount',\n",
              "       'amount_last_3months_in_per_loan_amount',\n",
              "       'amount_last_3months_out_per_loan_amount', 'default'],\n",
              "      dtype='object')"
            ]
          },
          "execution_count": 6,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "raw_df.columns"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WXXZ8-Ky6QJP",
        "outputId": "eb8044cc-bf4c-454f-8d9a-ab10d12d1028"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "float64    65\n",
              "bool        5\n",
              "object      1\n",
              "dtype: int64"
            ]
          },
          "execution_count": 7,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "filteredColumns = raw_df.dtypes[raw_df.dtypes == np.int64]\n",
        "raw_df[list(filteredColumns.index)] = raw_df[list(filteredColumns.index)].astype(float)\n",
        "\n",
        "name_onehot_already = ['card_type_junior','card_type_classic', 'card_type_gold','client_gender']\n",
        "\n",
        "raw_df[name_onehot_already] = raw_df[name_onehot_already].astype(np.bool)\n",
        "raw_df.dtypes.value_counts()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "3IOt4JNVms3a"
      },
      "outputs": [],
      "source": [
        "from sklearn.preprocessing import OneHotEncoder, StandardScaler, MinMaxScaler\n",
        "from sklearn.compose import ColumnTransformer\n",
        "\n",
        "filteredColumns = raw_df.dtypes[((raw_df.dtypes == np.object)  | (raw_df.dtypes == np.bool))  ]\n",
        "cat_cols = list(filteredColumns.index) \n",
        "\n",
        "col_trans = ColumnTransformer([\n",
        "    ('cat', OneHotEncoder(drop='if_binary'), cat_cols)\n",
        "], remainder='passthrough')\n",
        "\n",
        "df_transformed = col_trans.fit_transform(raw_df)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1aBzxP0S8BFC",
        "outputId": "239b0063-c8f4-444b-e3c8-857f7e743b9c"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array(['cat__acc_frequency_after_transaction',\n",
              "       'cat__acc_frequency_monthly', 'cat__acc_frequency_weekly',\n",
              "       'cat__client_gender_True', 'cat__card_type_classic_True',\n",
              "       'cat__card_type_gold_True', 'cat__card_type_junior_True',\n",
              "       'cat__default_True', 'remainder__loan_amount',\n",
              "       'remainder__loan_duration', 'remainder__loan_payments',\n",
              "       'remainder__day_before_loan',\n",
              "       'remainder__sum_monthly_payment_amount',\n",
              "       'remainder__sum_monthly_payment_amount_in',\n",
              "       'remainder__sum_monthly_payment_amount_out',\n",
              "       'remainder__sum_monthly_payment_out_household',\n",
              "       'remainder__sum_monthly_payment_out_insurrance',\n",
              "       'remainder__sum_monthly_payment_in_interest_credited',\n",
              "       'remainder__sum_monthly_payment_out_payment_for_statement',\n",
              "       'remainder__sum_monthly_payment_out_sanction_interest_neg_bal',\n",
              "       'remainder__count_out_sanction_interest_neg_bal',\n",
              "       'remainder__count_monthly_payment_trans',\n",
              "       'remainder__current_monthly_payment_amount_out',\n",
              "       'remainder__current_monthly_payment_out_household',\n",
              "       'remainder__current_monthly_payment_out_insurrance',\n",
              "       'remainder__current_monthly_payment_out_payment_for_statement',\n",
              "       'remainder__current_monthly_payment_in_interest_credited',\n",
              "       'remainder__sum_other_amount', 'remainder__sum_other_amount_in',\n",
              "       'remainder__sum_other_amount_out', 'remainder__count_other_trans',\n",
              "       'remainder__balance_latest', 'remainder__client_age',\n",
              "       'remainder__all_client_mean_age', 'remainder__all_client_count',\n",
              "       'remainder__num_inhabitants', 'remainder__urban_rate',\n",
              "       'remainder__avg_salary',\n",
              "       'remainder__num_enterpreneurs_per1000inhabitants',\n",
              "       'remainder__unemploy_rate', 'remainder__number_crimes',\n",
              "       'remainder__balance_mean', 'remainder__balance_min',\n",
              "       'remainder__balance_max', 'remainder__balance_min_last_month',\n",
              "       'remainder__balance_max_last_month',\n",
              "       'remainder__balance_mean_last_month',\n",
              "       'remainder__balance_min_last_3months',\n",
              "       'remainder__balance_max_last_3months',\n",
              "       'remainder__balance_mean_last_3months',\n",
              "       'remainder__balance_mean_per_loan_payment',\n",
              "       'remainder__balance_mean_last_month_per_loan_payment',\n",
              "       'remainder__balance_mean_per_loan_amount',\n",
              "       'remainder__balance_mean_last_month_per_loan_amount',\n",
              "       'remainder__growth_balance', 'remainder__amount_last_month',\n",
              "       'remainder__amount_last_month_in',\n",
              "       'remainder__amount_last_month_out',\n",
              "       'remainder__amount_last_month_per_loan_payments',\n",
              "       'remainder__amount_last_mount_in_per_loan_payments',\n",
              "       'remainder__amount_last_mount_out_per_loan_payments',\n",
              "       'remainder__amount_last_month_per_loan_amount',\n",
              "       'remainder__amount_last_mount_in_per_loan_amount',\n",
              "       'remainder__amount_last_mount_out_per_loan_amount',\n",
              "       'remainder__amount_last_3months_avg',\n",
              "       'remainder__amount_last_3months_in_avg',\n",
              "       'remainder__amount_last_3months_out_avg',\n",
              "       'remainder__amount_last_3months_per_loan_payments',\n",
              "       'remainder__amount_last_3months_in_per_loan_payments',\n",
              "       'remainder__amount_last_3months_out_per_loan_payments',\n",
              "       'remainder__amount_last_3months_per_loan_amount',\n",
              "       'remainder__amount_last_3months_in_per_loan_amount',\n",
              "       'remainder__amount_last_3months_out_per_loan_amount'], dtype=object)"
            ]
          },
          "execution_count": 9,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "col_trans.get_feature_names_out(raw_df.columns)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 488
        },
        "id": "Ji5GQKTA76ms",
        "outputId": "badd9f12-e084-450f-f824-9d20c84a42f6"
      },
      "outputs": [
        {
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            "text/plain": [
              "     cat__acc_frequency_after_transaction  cat__acc_frequency_monthly  \\\n",
              "0                                     0.0                         1.0   \n",
              "1                                     0.0                         1.0   \n",
              "2                                     0.0                         1.0   \n",
              "3                                     0.0                         1.0   \n",
              "4                                     0.0                         0.0   \n",
              "..                                    ...                         ...   \n",
              "677                                   0.0                         1.0   \n",
              "678                                   0.0                         1.0   \n",
              "679                                   0.0                         0.0   \n",
              "680                                   0.0                         1.0   \n",
              "681                                   0.0                         1.0   \n",
              "\n",
              "     cat__acc_frequency_weekly  cat__client_gender_True  \\\n",
              "0                          0.0                      1.0   \n",
              "1                          0.0                      0.0   \n",
              "2                          0.0                      1.0   \n",
              "3                          0.0                      1.0   \n",
              "4                          1.0                      0.0   \n",
              "..                         ...                      ...   \n",
              "677                        0.0                      0.0   \n",
              "678                        0.0                      0.0   \n",
              "679                        1.0                      0.0   \n",
              "680                        0.0                      1.0   \n",
              "681                        0.0                      0.0   \n",
              "\n",
              "     cat__card_type_classic_True  cat__card_type_gold_True  \\\n",
              "0                            0.0                       0.0   \n",
              "1                            0.0                       0.0   \n",
              "2                            0.0                       0.0   \n",
              "3                            0.0                       0.0   \n",
              "4                            0.0                       0.0   \n",
              "..                           ...                       ...   \n",
              "677                          0.0                       0.0   \n",
              "678                          0.0                       0.0   \n",
              "679                          0.0                       0.0   \n",
              "680                          1.0                       0.0   \n",
              "681                          0.0                       0.0   \n",
              "\n",
              "     cat__card_type_junior_True  default  remainder__loan_amount  \\\n",
              "0                           0.0      0.0                 80952.0   \n",
              "1                           0.0      1.0                 30276.0   \n",
              "2                           0.0      0.0                 30276.0   \n",
              "3                           0.0      1.0                318480.0   \n",
              "4                           0.0      0.0                110736.0   \n",
              "..                          ...      ...                     ...   \n",
              "677                         0.0      0.0                 39168.0   \n",
              "678                         0.0      0.0                280440.0   \n",
              "679                         0.0      0.0                419880.0   \n",
              "680                         0.0      0.0                 54024.0   \n",
              "681                         0.0      0.0                129408.0   \n",
              "\n",
              "     remainder__loan_duration  ...  \\\n",
              "0                        24.0  ...   \n",
              "1                        12.0  ...   \n",
              "2                        12.0  ...   \n",
              "3                        60.0  ...   \n",
              "4                        48.0  ...   \n",
              "..                        ...  ...   \n",
              "677                      24.0  ...   \n",
              "678                      60.0  ...   \n",
              "679                      60.0  ...   \n",
              "680                      12.0  ...   \n",
              "681                      24.0  ...   \n",
              "\n",
              "     remainder__amount_last_mount_out_per_loan_amount  \\\n",
              "0                                            0.425894   \n",
              "1                                            0.601632   \n",
              "2                                            3.150482   \n",
              "3                                            0.103344   \n",
              "4                                            0.089312   \n",
              "..                                                ...   \n",
              "677                                          0.656352   \n",
              "678                                          0.076412   \n",
              "679                                          0.200500   \n",
              "680                                          0.390327   \n",
              "681                                          0.170546   \n",
              "\n",
              "     remainder__amount_last_3months_avg  \\\n",
              "0                          44114.333333   \n",
              "1                          36373.333333   \n",
              "2                         115321.666667   \n",
              "3                          60867.000000   \n",
              "4                          29212.333333   \n",
              "..                                  ...   \n",
              "677                        38777.333333   \n",
              "678                        42196.000000   \n",
              "679                        96056.000000   \n",
              "680                        65997.333333   \n",
              "681                        35072.666667   \n",
              "\n",
              "     remainder__amount_last_3months_in_avg  \\\n",
              "0                             23734.666667   \n",
              "1                             17858.333333   \n",
              "2                             53753.000000   \n",
              "3                             30319.333333   \n",
              "4                             16590.333333   \n",
              "..                                     ...   \n",
              "677                           20569.333333   \n",
              "678                           20453.333333   \n",
              "679                           42327.333333   \n",
              "680                           31656.000000   \n",
              "681                           18916.000000   \n",
              "\n",
              "     remainder__amount_last_3months_out_avg  \\\n",
              "0                              20379.666667   \n",
              "1                              18515.000000   \n",
              "2                              61568.666667   \n",
              "3                              30547.666667   \n",
              "4                              12622.000000   \n",
              "..                                      ...   \n",
              "677                            18208.000000   \n",
              "678                            21742.666667   \n",
              "679                            53728.666667   \n",
              "680                            34341.333333   \n",
              "681                            16156.666667   \n",
              "\n",
              "     remainder__amount_last_3months_per_loan_payments  \\\n",
              "0                                           13.078664   \n",
              "1                                           14.416700   \n",
              "2                                           45.708152   \n",
              "3                                           11.467031   \n",
              "4                                           12.662477   \n",
              "..                                                ...   \n",
              "677                                         23.760621   \n",
              "678                                          9.027813   \n",
              "679                                         13.726207   \n",
              "680                                         14.659559   \n",
              "681                                          6.504575   \n",
              "\n",
              "     remainder__amount_last_3months_in_per_loan_payments  \\\n",
              "0                                             7.036664     \n",
              "1                                             7.078214     \n",
              "2                                            21.305192     \n",
              "3                                             5.712007     \n",
              "4                                             7.191302     \n",
              "..                                                 ...     \n",
              "677                                          12.603758     \n",
              "678                                           4.375981     \n",
              "679                                           6.048490     \n",
              "680                                           7.031542     \n",
              "681                                           3.508160     \n",
              "\n",
              "     remainder__amount_last_3months_out_per_loan_payments  \\\n",
              "0                                             6.042000      \n",
              "1                                             7.338486      \n",
              "2                                            24.402959      \n",
              "3                                             5.755024      \n",
              "4                                             5.471175      \n",
              "..                                                 ...      \n",
              "677                                          11.156863      \n",
              "678                                           4.651833      \n",
              "679                                           7.677717      \n",
              "680                                           7.628017      \n",
              "681                                           2.996414      \n",
              "\n",
              "     remainder__amount_last_3months_per_loan_amount  \\\n",
              "0                                          0.544944   \n",
              "1                                          1.201392   \n",
              "2                                          3.809013   \n",
              "3                                          0.191117   \n",
              "4                                          0.263802   \n",
              "..                                              ...   \n",
              "677                                        0.990026   \n",
              "678                                        0.150464   \n",
              "679                                        0.228770   \n",
              "680                                        1.221630   \n",
              "681                                        0.271024   \n",
              "\n",
              "     remainder__amount_last_3months_in_per_loan_amount  \\\n",
              "0                                             0.293194   \n",
              "1                                             0.589851   \n",
              "2                                             1.775433   \n",
              "3                                             0.095200   \n",
              "4                                             0.149819   \n",
              "..                                                 ...   \n",
              "677                                           0.525157   \n",
              "678                                           0.072933   \n",
              "679                                           0.100808   \n",
              "680                                           0.585962   \n",
              "681                                           0.146173   \n",
              "\n",
              "     remainder__amount_last_3months_out_per_loan_amount  \n",
              "0                                             0.251750   \n",
              "1                                             0.611540   \n",
              "2                                             2.033580   \n",
              "3                                             0.095917   \n",
              "4                                             0.113983   \n",
              "..                                                 ...   \n",
              "677                                           0.464869   \n",
              "678                                           0.077531   \n",
              "679                                           0.127962   \n",
              "680                                           0.635668   \n",
              "681                                           0.124851   \n",
              "\n",
              "[682 rows x 73 columns]"
            ]
          },
          "execution_count": 10,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df = pd.DataFrame(df_transformed, columns= col_trans.get_feature_names_out(raw_df.columns) , index= raw_df.index)\n",
        "df.rename(columns = {'cat__default_True':'default'}, inplace = True)\n",
        "df.rename(columns = {'remainder__default':'default'}, inplace = True)\n",
        "df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "id": "iceyyYWa270x"
      },
      "outputs": [],
      "source": [
        "df.mean()\n",
        "df.fillna(df.mean(), inplace=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vlYC9NdYRKSG",
        "outputId": "52b3b51c-6052-44b2-fe73-e987345fefde"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Feature(X):  (682, 72)\n",
            "Target(y):  (682,)\n"
          ]
        }
      ],
      "source": [
        "X = df.drop(['default'], axis=1) #df_transformed[:, :-1]\n",
        "y = df['default']  #df_transformed[:, -1]\n",
        "print('Feature(X): ', np.shape(X))\n",
        "print('Target(y): ', np.shape(y))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "id": "8kvEhjJLftJl"
      },
      "outputs": [],
      "source": [
        "\n",
        "#from sklearn.ensemble import RandomForestClassifier\n",
        "#from sklearn.ensemble import AdaBoostClassifier\n",
        "#from sklearn.tree import DecisionTreeClassifier\n",
        "#from sklearn.ensemble import BaggingClassifier\n",
        "#from sklearn.ensemble import ExtraTreesClassifier\n",
        "#from lightgbm import LGBMClassifier\n",
        "#from sklearn.linear_model import LogisticRegression\n",
        "#from sklearn.naive_bayes import GaussianNB\n",
        "\n",
        "from xgboost import XGBClassifier\n",
        "from tqdm.notebook import tqdm_notebook, trange\n",
        "from sklearn.experimental import enable_halving_search_cv  # noqa\n",
        "from sklearn.model_selection import HalvingGridSearchCV\n",
        "from imblearn.pipeline import Pipeline, make_pipeline\n",
        "from sklearn.model_selection import cross_validate\n",
        "from imblearn.over_sampling import SMOTE\n",
        "from sklearn.model_selection import *\n",
        "from sklearn.metrics import *\n",
        "import joblib"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "id": "M-D1xxLlAwm6"
      },
      "outputs": [],
      "source": [
        "\n",
        "def to_labels(pos_probs, threshold):\n",
        "\treturn (pos_probs >= threshold).astype('int')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "id": "7DoMMgM3ikrD"
      },
      "outputs": [],
      "source": [
        "\n",
        "def create_Kfold(X_train, y_train, cv):\n",
        "  K_fold ={ }\n",
        "  for i, (train_index, test_index) in enumerate(cv.split(X_train, y_train)):\n",
        "    K_fold['fold_'+str(i)] = np.array([train_index, test_index])\n",
        "  return K_fold #dict\n",
        "  "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "id": "Bhg1Fl8yooYY"
      },
      "outputs": [],
      "source": [
        "def kfold_with_smote(X, y, cv, model, smote=True, threshold=None):\n",
        "    cv_f1 = []\n",
        "    cv_acc = []\n",
        "    cv_roc_auc = []\n",
        "    tns = []\n",
        "    fps = []\n",
        "    fns = []\n",
        "    tps = []\n",
        "\n",
        "    for fold, (train_index, test_index) in enumerate(cv.split(X, y)):\n",
        "        X_train = X.iloc[train_index, :]\n",
        "        y_train = y[train_index]  # Based on your code, you might need a ravel call here, but I would look into how you're generating your y\n",
        "        X_test = X.iloc[test_index, :]\n",
        "        y_test = y[test_index\n",
        "        ]  # See comment on ravel and  y_train\n",
        "        if smote:\n",
        "            sm = SMOTE(sampling_strategy=0.5, random_state=0)\n",
        "            X_train, y_train = sm.fit_resample(X_train, y_train)\n",
        "        # model = ...  # Choose a model here\n",
        "        model.fit(X_train, y_train)  \n",
        "        y_pred = model.predict(X_test)\n",
        "\n",
        "        if threshold is not None:\n",
        "            y_pred = model.predict_proba(X_test)\n",
        "            probs = y_pred[:, 1]\n",
        "            y_pred = to_labels(probs, threshold)\n",
        "\n",
        "\n",
        "        cv_acc.append(accuracy_score(y_test, y_pred))\n",
        "        cv_f1.append(f1_score(y_test, y_pred))\n",
        "        cv_roc_auc.append(roc_auc_score(y_test, y_pred))\n",
        "        tn, fp, fn, tp = confusion_matrix(y_test, y_pred).ravel()\n",
        "        tns.append(tn)\n",
        "        fps.append(fp)\n",
        "        fns.append(fn)\n",
        "        tps.append(tp)\n",
        "    \n",
        "    confuse_matrix = {'tn':np.array(tns).sum(), 'fp':np.array(fps).sum(), 'fn':np.array(fns).sum(), 'tp':np.array(tps).sum()}\n",
        "    return np.array(cv_acc), np.array(cv_f1), np.array(cv_roc_auc), confuse_matrix\n",
        "\n",
        "def custom_f1(X, y, cv, model, smote=True):\n",
        "    _, score, _, _= kfold_with_smote(X, y, cv, model, smote=True)\n",
        "    return score.mean()\n",
        "\n",
        "\n",
        "def Best_Feature_Selected(model ,X ,y ,cv):\n",
        "\n",
        " # model.fit(X, y)\n",
        "  sorted_indices = np.argsort(model.feature_importances_)[::-1]\n",
        "  importances = np.delete(sorted_indices, np.where(sorted_indices <=2))\n",
        "\n",
        "  result = {\n",
        "      'feature_indice' : [],\n",
        "      'cv_score' : [],  \n",
        "  }\n",
        "\n",
        "  for i in trange(X.shape[1], desc = 'Best_Feature_Selected', unit ='round'):\n",
        "    # feature_selected = np.array([0, 1, 2]) #fix loan \n",
        "    feature_selected = np.array([0, 2])\n",
        "    ans = np.append(feature_selected, values = importances[: i])\n",
        "    # score = cross_val_score(model, X.iloc[:,ans], y, cv = cv, scoring='f1').mean()\n",
        "    _, score, _, _ = kfold_with_smote(X.iloc[:,ans], y, cv, model, smote=True)\n",
        "    score = score.mean()\n",
        "\n",
        "    result['feature_indice'].append(ans)\n",
        "    result['cv_score'].append( score)\n",
        "\n",
        "  idx_best = np.argmax(result['cv_score'])\n",
        "\n",
        "\n",
        "  return result['feature_indice'][idx_best]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "id": "SeNpbKooAvX-"
      },
      "outputs": [],
      "source": [
        "def plot_conf(conf):\n",
        "    tn = conf['tn']\n",
        "    fp = conf['fp']\n",
        "    fn = conf['fn']\n",
        "    tp = conf['tp']\n",
        "    ax= plt.subplot()\n",
        "    cm = np.array([[tn, fp], \n",
        "                   [fn, tp]])\n",
        "    sns.heatmap(cm, annot=True, fmt='g', ax=ax);  #annot=True to annotate cells, ftm='g' to disable scientific notation\n",
        "\n",
        "    # labels, title and ticks\n",
        "    ax.set_xlabel('Predicted labels');ax.set_ylabel('True labels'); \n",
        "    ax.set_title('Confusion Matrix'); \n",
        "    ax.xaxis.set_ticklabels(['Good', 'Bad']); ax.yaxis.set_ticklabels(['Good', 'Bad']);\n",
        "    plt.show()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "id": "Izo31S0topQ1"
      },
      "outputs": [],
      "source": [
        "def train_with_smote(X, y, model, test_size=0.33):\n",
        "    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=test_size, stratify=y, random_state=10)\n",
        "\n",
        "    sm = SMOTE(sampling_strategy=0.5, random_state=0)\n",
        "    X_train, y_train = sm.fit_resample(X_train, y_train)\n",
        "    model.fit(X_train, y_train)\n",
        "    y_pred = model.predict(X_test)\n",
        "\n",
        "    acc = accuracy_score(y_test, y_pred)\n",
        "    f1 = f1_score(y_test, y_pred)\n",
        "    roc_auc = roc_auc_score(y_test, y_pred)\n",
        "    tn, fp, fn, tp = confusion_matrix(y_test, y_pred).ravel()\n",
        "    \n",
        "    confuse_matrix = {'tn':np.array(tn), 'fp':np.array(fp), 'fn':np.array(fn), 'tp':np.array(tp)}\n",
        "    return model, np.array(acc), np.array(f1), np.array(roc_auc), confuse_matrix"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "id": "8RZv34JKefLK"
      },
      "outputs": [],
      "source": [
        "def Runnnnnnnnning___(Models, X, y, k =5, threshold=None):\n",
        "\n",
        "#   X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, stratify=y, random_state=10)\n",
        "    X_train = X.copy()\n",
        "    y_train = y.copy()\n",
        "\n",
        "    disp = []\n",
        "\n",
        "    Report = {\n",
        "        '_Model_' : [],\n",
        "        'Best Parameters' : [],\n",
        "        'Num Best Features' : [],\n",
        "        'Best Features' : [],\n",
        "        'Estimator' : [],\n",
        "        'Accuracy' : [],\n",
        "        'F1-Score' : [],\n",
        "        'roc_auc' : [],   \n",
        "    }\n",
        "\n",
        "    skf = StratifiedKFold(n_splits=k, shuffle=True, random_state=0)\n",
        "    indice_Kfold = create_Kfold(X_train, y_train, cv = skf)\n",
        "    oversample = SMOTE(sampling_strategy=0.5, random_state=0)\n",
        "    my_f1 = make_scorer(custom_f1, greater_is_better=True)\n",
        "    \n",
        "    for name, model,  params  in tqdm_notebook(Models, desc = 'Runnnnnnnnning___'):\n",
        "        print('__' + name)\n",
        "        Report['_Model_'].append(name)\n",
        "        joblib_file = name + \"__model.pkl\"\n",
        "\n",
        "        #See the inital model performance\n",
        "        clf = model\n",
        "        print('See the inital model performance')\n",
        "        print('Acc:', cross_val_score(clf, X_train, y_train, cv = skf, scoring='accuracy').mean())\n",
        "        print('F1:', cross_val_score(clf, X_train, y_train, cv = skf, scoring='f1').mean())\n",
        "        print('ROC AUC:', cross_val_score(clf, X_train, y_train, cv = skf, scoring='roc_auc').mean())\n",
        "\n",
        "        print('\\t->SMOTE')\n",
        "        cv_acc, cv_f1, cv_roc_auc, conf = kfold_with_smote(X_train, y_train, skf, model, threshold=threshold)\n",
        "        print('Acc:', cv_acc.mean())\n",
        "        print('F1:', cv_f1.mean())\n",
        "        print('ROC AUC:', cv_roc_auc.mean())\n",
        "        plot_conf(conf)\n",
        "\n",
        "        print('Tuning....')\n",
        "\n",
        "        # Find Best Feature and selected\n",
        "        print('\\t->Find Best Features....')\n",
        "        # clf = model.fit(X_train, y_train)\n",
        "        BestFeatures = Best_Feature_Selected(clf, X_train, y_train, skf)\n",
        "        print('\\t\\tNum Best features', len(BestFeatures)) \n",
        "        print('\\t\\tBest features', X_train.columns[BestFeatures].array.astype(str))  \n",
        "        Report['Num Best Features'].append(len(BestFeatures))\n",
        "        Report['Best Features'].append(X_train.columns[BestFeatures].array.astype(str))\n",
        "\n",
        "        print('\\t->Best Features performance with SMOTE')\n",
        "        cv_acc, cv_f1, cv_roc_auc, conf = kfold_with_smote(X_train.iloc[:, BestFeatures], y_train, skf, model, threshold=threshold)\n",
        "        print('Acc:', cv_acc.mean())\n",
        "        print('F1:', cv_f1.mean())\n",
        "        print('ROC AUC:', cv_roc_auc.mean())\n",
        "        plot_conf(conf)\n",
        "\n",
        "\n",
        "        print('\\t->Find Best Parameters....')\n",
        "        # make pipeline (data -> SMOTE() -> model.fit)\n",
        "        search = make_pipeline(  oversample,\n",
        "                            HalvingGridSearchCV(estimator = model,\n",
        "                                                                        param_grid= params,  scoring = my_f1,  cv = 3,\n",
        "                                                                        verbose=0, refit=True,\n",
        "                                                                        random_state=0, n_jobs = -1))\n",
        "         \n",
        "        #define structure model\n",
        "        search.fit(X_train.iloc[:, BestFeatures], y_train)\n",
        "        # Return set of parameters with the best performance\n",
        "        BestParams = search.named_steps['halvinggridsearchcv'].best_params_\n",
        "        print('\\t\\tBest parameters', BestParams)\n",
        "        Report['Best Parameters'].append( BestParams)\n",
        "        \n",
        "        \n",
        "        print('Refit using best params & best feature')\n",
        "        #Combine Best pameters and Best Feature\n",
        "        estimator = model.set_params(**BestParams)\n",
        "        # estimator.fit(X_train.iloc[:, BestFeatures], y_train)\n",
        "        Report['Estimator'].append(estimator) \n",
        "\n",
        "        # SMOTE train, not test \n",
        "        print('Performance after using best params & best feature')\n",
        "        X = X_train.iloc[:, BestFeatures]\n",
        "        cv_acc, cv_f1, cv_roc_auc, conf = kfold_with_smote(X, y, skf, estimator, threshold=threshold)\n",
        "        print('Acc:', cv_acc.mean())\n",
        "        print('F1:', cv_f1.mean())\n",
        "        print('ROC AUC:', cv_roc_auc.mean())\n",
        "        plot_conf(conf)\n",
        "\n",
        "        #Save model\n",
        "        estimator = model.set_params(**BestParams)\n",
        "        model_deploy, acc, f1, roc_auc, conf = train_with_smote(X_train.iloc[:, BestFeatures], y, estimator, test_size=0.30)\n",
        "        plot_conf(conf)\n",
        "        print(acc, f1, roc_auc)\n",
        "        joblib.dump(model_deploy, joblib_file)\n",
        "\n",
        "    return Report\n",
        "\n",
        "        "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000,
          "referenced_widgets": [
            "3beafd6067e54b90bd4eb4ca471bbee0",
            "c8b6c5beb17144cdb830afaaf02b518d",
            "8d7fe9940f8342d99eda34b6445463aa",
            "c81be48440f143ff88220a705d7377cf",
            "274e24afff2644ddb50d42f24fc22274",
            "8855b81614d146f5942a715030c8cf3d",
            "7b0bf7c603f94f9fa19273c91ae1484c",
            "c9e834da380d4a04a7992bfab833bda7",
            "d3e4696967b149da81c33ca0dc4af726",
            "65148f16b5bc4661b464fac7bc96fe2b",
            "424d7b775fce4ed9b28bb0f23a354ca8",
            "21247ed93cf441348fd36125ff6d0637",
            "966331e070f34eb6a873d00139ddd21f",
            "32d5436bee8647ecb99bc640804dd076",
            "9508ad339da94ad8bbd5e7a850f51429",
            "3c6cd2790c774b48b6488096be3b47f4",
            "2356f00356b84ecf92259c735a729e29",
            "989ce91d04ff4869a70de9601b01a074",
            "32977f1920644dc1b68d185758f39603",
            "aea98d056abf496aa547b2f2c6fe2440",
            "4918aa795ad145048b11646d3d95cee3",
            "9a991c67644b4cd7ae4511c7ece6ee60"
          ]
        },
        "id": "XnRaoK7m-KBJ",
        "outputId": "c90a5bdb-edf6-400e-8184-bd50364e7e3a"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "3beafd6067e54b90bd4eb4ca471bbee0",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Runnnnnnnnning___:   0%|          | 0/1 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "__xgbclassifier\n",
            "See the inital model performance\n",
            "Acc: 0.9295822676896845\n",
            "F1: 0.5959174159174159\n",
            "ROC AUC: 0.9046906713505075\n",
            "\t->SMOTE\n",
            "Acc: 0.9163469735720374\n",
            "F1: 0.5717482517482517\n",
            "ROC AUC: 0.738002537080406\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Tuning....\n",
            "\t->Find Best Features....\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "21247ed93cf441348fd36125ff6d0637",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Best_Feature_Selected:   0%|          | 0/72 [00:00<?, ?round/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\t\tNum Best features 21\n",
            "\t\tBest features ['cat__acc_frequency_after_transaction' 'cat__acc_frequency_weekly'\n",
            " 'remainder__count_out_sanction_interest_neg_bal'\n",
            " 'remainder__sum_monthly_payment_amount' 'remainder__balance_min'\n",
            " 'remainder__amount_last_month_per_loan_payments'\n",
            " 'remainder__balance_latest'\n",
            " 'remainder__current_monthly_payment_out_payment_for_statement'\n",
            " 'remainder__balance_min_last_3months'\n",
            " 'remainder__amount_last_3months_avg'\n",
            " 'remainder__balance_mean_per_loan_payment' 'remainder__all_client_count'\n",
            " 'remainder__amount_last_3months_out_avg' 'cat__client_gender_True'\n",
            " 'remainder__day_before_loan' 'cat__card_type_classic_True'\n",
            " 'remainder__balance_max_last_month' 'remainder__avg_salary'\n",
            " 'remainder__loan_payments'\n",
            " 'remainder__current_monthly_payment_amount_out'\n",
            " 'remainder__balance_min_last_month']\n",
            "\t->Best Features performance with SMOTE\n",
            "Acc: 0.9266410912190963\n",
            "F1: 0.6448772795831619\n",
            "ROC AUC: 0.7843901249024199\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\t->Find Best Parameters....\n",
            "\t\tBest parameters {'max_depth': 30}\n",
            "Refit using best params & best feature\n",
            "Performance after using best params & best feature\n",
            "Acc: 0.9207800511508951\n",
            "F1: 0.5969816131580837\n",
            "ROC AUC: 0.7576180718188914\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "0.9219512195121952 0.5789473684210527 0.7281414237935977\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "{'Accuracy': [],\n",
              " 'Best Features': [array(['cat__acc_frequency_after_transaction',\n",
              "         'cat__acc_frequency_weekly',\n",
              "         'remainder__count_out_sanction_interest_neg_bal',\n",
              "         'remainder__sum_monthly_payment_amount', 'remainder__balance_min',\n",
              "         'remainder__amount_last_month_per_loan_payments',\n",
              "         'remainder__balance_latest',\n",
              "         'remainder__current_monthly_payment_out_payment_for_statement',\n",
              "         'remainder__balance_min_last_3months',\n",
              "         'remainder__amount_last_3months_avg',\n",
              "         'remainder__balance_mean_per_loan_payment',\n",
              "         'remainder__all_client_count',\n",
              "         'remainder__amount_last_3months_out_avg',\n",
              "         'cat__client_gender_True', 'remainder__day_before_loan',\n",
              "         'cat__card_type_classic_True', 'remainder__balance_max_last_month',\n",
              "         'remainder__avg_salary', 'remainder__loan_payments',\n",
              "         'remainder__current_monthly_payment_amount_out',\n",
              "         'remainder__balance_min_last_month'], dtype='<U60')],\n",
              " 'Best Parameters': [{'max_depth': 30}],\n",
              " 'Estimator': [XGBClassifier(eval_metric='logloss', max_depth=30, n_jobs=-1)],\n",
              " 'F1-Score': [],\n",
              " 'Num Best Features': [21],\n",
              " '_Model_': ['xgbclassifier'],\n",
              " 'roc_auc': []}"
            ]
          },
          "execution_count": 20,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "xgb_model =  ('xgbclassifier', \n",
        "           XGBClassifier(objective=\"binary:logistic\", random_state = 0, n_jobs = -1, eval_metric='logloss').fit(X.values, y),\n",
        "           { \n",
        "               \"max_depth\": [6, 10, 20, 30, 40],\n",
        "            #   \"gamma\": np.arange(0, 0.1, 0.025).tolist(),\n",
        "            #   \"min_child_weight\": np.arange(0, 3, 1).tolist(),\n",
        "            #   \"reg_lambda\": np.arange(20, 70, 10).tolist() + [1],\n",
        "            #   \"scale_pos_weight\": np.arange(2, 14, 2).tolist() + [1],\n",
        "               \n",
        "            #    \"max_depth\": [7],\n",
        "            #   \"gamma\": np.arange(0.4, 0.45, 0.01).tolist(),\n",
        "            #   \"min_child_weight\": [1, 2],\n",
        "            #   \"reg_lambda\": [4, 5, 6],\n",
        "            #   \"scale_pos_weight\": [1, 2, 3, 4],\n",
        "            # \"eta\": [5, 10, 20, 1],\n",
        "            # \"max_delta_step\": [-1, 0, 1, 2],\n",
        "            # \"subsample\":np.arange(0.0, 1, 0.5).tolist(), \n",
        "            # \"colsample_bytree\":np.arange(0.5, 1, 0.5).tolist(),\n",
        "            # \"alpha\":np.arange(0.0, 5, 1).tolist(),\n",
        "            # \"lambda\":np.arange(0.0, 2, 1).tolist(),\n",
        "            })\n",
        "\n",
        "Report = Runnnnnnnnning___([xgb_model], X, y, k=10)\n",
        "Report"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "y0d8KFvS0oHF"
      },
      "source": [
        "# Profit"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "id": "b-tKPiEniEsa"
      },
      "outputs": [],
      "source": [
        "\n",
        "xgb_params = {'max_depth': 30, 'gamma': 0.0,\n",
        "   'max_depth': 6,\n",
        "   'min_child_weight': 1,\n",
        "   'reg_lambda': 40,\n",
        "   'scale_pos_weight': 8}\n",
        "\n",
        "xgb_feature = np.array(['cat__acc_frequency_after_transaction',\n",
        "         'cat__acc_frequency_weekly',\n",
        "         'remainder__count_out_sanction_interest_neg_bal',\n",
        "         'remainder__sum_monthly_payment_amount', 'remainder__balance_min',\n",
        "         'remainder__amount_last_month_per_loan_payments',\n",
        "         'remainder__balance_latest',\n",
        "         'remainder__current_monthly_payment_out_payment_for_statement',\n",
        "         'remainder__balance_min_last_3months',\n",
        "         'remainder__amount_last_3months_avg',\n",
        "         'remainder__balance_mean_per_loan_payment',\n",
        "         'remainder__all_client_count',\n",
        "         'remainder__amount_last_3months_out_avg',\n",
        "         'cat__client_gender_True', 'remainder__day_before_loan',\n",
        "         'cat__card_type_classic_True', 'remainder__balance_max_last_month',\n",
        "         'remainder__avg_salary', 'remainder__loan_payments',\n",
        "         'remainder__current_monthly_payment_amount_out',\n",
        "         'remainder__balance_min_last_month'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "id": "jyDJcXa2I5Fj"
      },
      "outputs": [],
      "source": [
        "selected_feature = xgb_feature"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1Uj2w2nFhjNF",
        "outputId": "97d69d52-06f9-4f52-bf85-dd2061cbdf18"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "XGBClassifier(eval_metric='logloss', gamma=0.0, max_depth=6, n_jobs=-1,\n",
              "              reg_lambda=40, scale_pos_weight=8)"
            ]
          },
          "execution_count": 23,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "selected_feature_X = X[selected_feature]\n",
        "selected_model = XGBClassifier(objective=\"binary:logistic\", random_state = 0, n_jobs = -1, eval_metric='logloss', **xgb_params)\n",
        "selected_model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 299
        },
        "id": "yJFvPPxb0Agn",
        "outputId": "2fbd32e5-7b61-476d-9bf9-0a9e25070305"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-0ec49228-db83-449f-97ad-a35ccb320c52\">\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>account_id</th>\n",
              "      <th>loan_amount</th>\n",
              "      <th>loan_duration</th>\n",
              "      <th>loan_payments</th>\n",
              "      <th>loan_status</th>\n",
              "      <th>acc_frequency</th>\n",
              "      <th>day_before_loan</th>\n",
              "      <th>sum_monthly_payment_amount</th>\n",
              "      <th>sum_monthly_payment_amount_in</th>\n",
              "      <th>sum_monthly_payment_amount_out</th>\n",
              "      <th>...</th>\n",
              "      <th>amount_last_mount_out_per_loan_amount</th>\n",
              "      <th>amount_last_3months_avg</th>\n",
              "      <th>amount_last_3months_in_avg</th>\n",
              "      <th>amount_last_3months_out_avg</th>\n",
              "      <th>amount_last_3months_per_loan_payments</th>\n",
              "      <th>amount_last_3months_in_per_loan_payments</th>\n",
              "      <th>amount_last_3months_out_per_loan_payments</th>\n",
              "      <th>amount_last_3months_per_loan_amount</th>\n",
              "      <th>amount_last_3months_in_per_loan_amount</th>\n",
              "      <th>amount_last_3months_out_per_loan_amount</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2</td>\n",
              "      <td>80952</td>\n",
              "      <td>24</td>\n",
              "      <td>3373</td>\n",
              "      <td>A</td>\n",
              "      <td>monthly</td>\n",
              "      <td>313</td>\n",
              "      <td>120.482428</td>\n",
              "      <td>4.124601</td>\n",
              "      <td>116.357827</td>\n",
              "      <td>...</td>\n",
              "      <td>0.425894</td>\n",
              "      <td>44114.333333</td>\n",
              "      <td>23734.666667</td>\n",
              "      <td>20379.666667</td>\n",
              "      <td>13.078664</td>\n",
              "      <td>7.036664</td>\n",
              "      <td>6.042000</td>\n",
              "      <td>0.544944</td>\n",
              "      <td>0.293194</td>\n",
              "      <td>0.251750</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>19</td>\n",
              "      <td>30276</td>\n",
              "      <td>12</td>\n",
              "      <td>2523</td>\n",
              "      <td>B</td>\n",
              "      <td>monthly</td>\n",
              "      <td>388</td>\n",
              "      <td>335.966495</td>\n",
              "      <td>7.306701</td>\n",
              "      <td>328.659794</td>\n",
              "      <td>...</td>\n",
              "      <td>0.601632</td>\n",
              "      <td>36373.333333</td>\n",
              "      <td>17858.333333</td>\n",
              "      <td>18515.000000</td>\n",
              "      <td>14.416700</td>\n",
              "      <td>7.078214</td>\n",
              "      <td>7.338486</td>\n",
              "      <td>1.201392</td>\n",
              "      <td>0.589851</td>\n",
              "      <td>0.611540</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>25</td>\n",
              "      <td>30276</td>\n",
              "      <td>12</td>\n",
              "      <td>2523</td>\n",
              "      <td>A</td>\n",
              "      <td>monthly</td>\n",
              "      <td>498</td>\n",
              "      <td>204.881526</td>\n",
              "      <td>8.206827</td>\n",
              "      <td>196.674699</td>\n",
              "      <td>...</td>\n",
              "      <td>3.150482</td>\n",
              "      <td>115321.666667</td>\n",
              "      <td>53753.000000</td>\n",
              "      <td>61568.666667</td>\n",
              "      <td>45.708152</td>\n",
              "      <td>21.305192</td>\n",
              "      <td>24.402959</td>\n",
              "      <td>3.809013</td>\n",
              "      <td>1.775433</td>\n",
              "      <td>2.033580</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>37</td>\n",
              "      <td>318480</td>\n",
              "      <td>60</td>\n",
              "      <td>5308</td>\n",
              "      <td>D</td>\n",
              "      <td>monthly</td>\n",
              "      <td>422</td>\n",
              "      <td>74.492891</td>\n",
              "      <td>5.457346</td>\n",
              "      <td>69.035545</td>\n",
              "      <td>...</td>\n",
              "      <td>0.103344</td>\n",
              "      <td>60867.000000</td>\n",
              "      <td>30319.333333</td>\n",
              "      <td>30547.666667</td>\n",
              "      <td>11.467031</td>\n",
              "      <td>5.712007</td>\n",
              "      <td>5.755024</td>\n",
              "      <td>0.191117</td>\n",
              "      <td>0.095200</td>\n",
              "      <td>0.095917</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>38</td>\n",
              "      <td>110736</td>\n",
              "      <td>48</td>\n",
              "      <td>2307</td>\n",
              "      <td>C</td>\n",
              "      <td>weekly</td>\n",
              "      <td>254</td>\n",
              "      <td>117.125984</td>\n",
              "      <td>4.385827</td>\n",
              "      <td>112.740157</td>\n",
              "      <td>...</td>\n",
              "      <td>0.089312</td>\n",
              "      <td>29212.333333</td>\n",
              "      <td>16590.333333</td>\n",
              "      <td>12622.000000</td>\n",
              "      <td>12.662477</td>\n",
              "      <td>7.191302</td>\n",
              "      <td>5.471175</td>\n",
              "      <td>0.263802</td>\n",
              "      <td>0.149819</td>\n",
              "      <td>0.113983</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 72 columns</p>\n",
              "</div>\n",
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            ],
            "text/plain": [
              "   account_id  loan_amount  loan_duration  loan_payments loan_status  \\\n",
              "0           2        80952             24           3373           A   \n",
              "1          19        30276             12           2523           B   \n",
              "2          25        30276             12           2523           A   \n",
              "3          37       318480             60           5308           D   \n",
              "4          38       110736             48           2307           C   \n",
              "\n",
              "  acc_frequency  day_before_loan  sum_monthly_payment_amount  \\\n",
              "0       monthly              313                  120.482428   \n",
              "1       monthly              388                  335.966495   \n",
              "2       monthly              498                  204.881526   \n",
              "3       monthly              422                   74.492891   \n",
              "4        weekly              254                  117.125984   \n",
              "\n",
              "   sum_monthly_payment_amount_in  sum_monthly_payment_amount_out  ...  \\\n",
              "0                       4.124601                      116.357827  ...   \n",
              "1                       7.306701                      328.659794  ...   \n",
              "2                       8.206827                      196.674699  ...   \n",
              "3                       5.457346                       69.035545  ...   \n",
              "4                       4.385827                      112.740157  ...   \n",
              "\n",
              "   amount_last_mount_out_per_loan_amount  amount_last_3months_avg  \\\n",
              "0                               0.425894             44114.333333   \n",
              "1                               0.601632             36373.333333   \n",
              "2                               3.150482            115321.666667   \n",
              "3                               0.103344             60867.000000   \n",
              "4                               0.089312             29212.333333   \n",
              "\n",
              "   amount_last_3months_in_avg  amount_last_3months_out_avg  \\\n",
              "0                23734.666667                 20379.666667   \n",
              "1                17858.333333                 18515.000000   \n",
              "2                53753.000000                 61568.666667   \n",
              "3                30319.333333                 30547.666667   \n",
              "4                16590.333333                 12622.000000   \n",
              "\n",
              "   amount_last_3months_per_loan_payments  \\\n",
              "0                              13.078664   \n",
              "1                              14.416700   \n",
              "2                              45.708152   \n",
              "3                              11.467031   \n",
              "4                              12.662477   \n",
              "\n",
              "   amount_last_3months_in_per_loan_payments  \\\n",
              "0                                  7.036664   \n",
              "1                                  7.078214   \n",
              "2                                 21.305192   \n",
              "3                                  5.712007   \n",
              "4                                  7.191302   \n",
              "\n",
              "   amount_last_3months_out_per_loan_payments  \\\n",
              "0                                   6.042000   \n",
              "1                                   7.338486   \n",
              "2                                  24.402959   \n",
              "3                                   5.755024   \n",
              "4                                   5.471175   \n",
              "\n",
              "   amount_last_3months_per_loan_amount  \\\n",
              "0                             0.544944   \n",
              "1                             1.201392   \n",
              "2                             3.809013   \n",
              "3                             0.191117   \n",
              "4                             0.263802   \n",
              "\n",
              "   amount_last_3months_in_per_loan_amount  \\\n",
              "0                                0.293194   \n",
              "1                                0.589851   \n",
              "2                                1.775433   \n",
              "3                                0.095200   \n",
              "4                                0.149819   \n",
              "\n",
              "   amount_last_3months_out_per_loan_amount  \n",
              "0                                 0.251750  \n",
              "1                                 0.611540  \n",
              "2                                 2.033580  \n",
              "3                                 0.095917  \n",
              "4                                 0.113983  \n",
              "\n",
              "[5 rows x 72 columns]"
            ]
          },
          "execution_count": 24,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "\n",
        "raw_df = pd.read_csv(url)\n",
        "raw_df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "id": "Fde-blrg0AeV"
      },
      "outputs": [],
      "source": [
        "def cal_revenue(total_amount, loan_duration_month, paid_month, interest_rate_peryear):\n",
        "    '''\n",
        "    Interest (Compound) = P(1+i)^t - P\n",
        "    P = all / (1+i)^t\n",
        "    '''\n",
        "    principle = total_amount / (1+interest_rate_peryear)**(loan_duration_month//12)\n",
        "    expected_revenue = total_amount - principle\n",
        "    revenue = paid_month * expected_revenue / loan_duration_month\n",
        "    return revenue\n",
        "\n",
        "def cal_cost(loan_payment, loan_duration_month, paid_month):\n",
        "    not_paid_month = loan_duration_month - paid_month\n",
        "    cost = not_paid_month * loan_payment\n",
        "    return cost\n",
        "\n",
        "def kfold_test_smote(X, y, model, account_id,  k=5, smote=True, threshold=None):\n",
        "    cv = StratifiedKFold(n_splits=k, shuffle=True, random_state=0)\n",
        "    df = pd.DataFrame({'test_idx' : np.array([]),\n",
        "                       'y_true' : np.array([]),\n",
        "                       'probs' : np.array([]),\n",
        "                       'account_id' : np.array([])})\n",
        "    \n",
        "   \n",
        "\n",
        "    for fold, (train_index, test_index) in enumerate(cv.split(X, y)):\n",
        "        new_df = pd.DataFrame({'test_idx' : np.array([]),\n",
        "                       'y_true' : np.array([]),\n",
        "                       'probs' : np.array([]),\n",
        "                       'account_id' : np.array([])})\n",
        "        X_train = X.iloc[train_index, :]\n",
        "        y_train = y[train_index] \n",
        "        X_test = X.iloc[test_index, :]\n",
        "        y_test = y[test_index]  # See comment on ravel and  y_train\n",
        "        # profit_test = profit[test_index]\n",
        "        account_id_test = account_id[test_index]\n",
        "\n",
        "        if smote:\n",
        "            sm = SMOTE(sampling_strategy=0.5, random_state=0)\n",
        "            X_train, y_train = sm.fit_resample(X_train, y_train)\n",
        "        # model = ...  # Choose a model here\n",
        "        model.fit(X_train, y_train)  \n",
        "        y_pred = model.predict_proba(X_test)\n",
        "        probs = y_pred[:, 1]\n",
        "        new_df['test_idx'] = test_index\n",
        "        new_df['y_true'] = y_test\n",
        "        new_df['probs'] = probs\n",
        "        new_df['account_id'] = account_id_test\n",
        "        df = pd.concat([df.reset_index(drop = True), new_df.reset_index(drop = True)], axis =0)\n",
        "        \n",
        "\n",
        "\n",
        "    return df\n",
        "\n",
        "def profit_maximize(y_true, y_pred, profit):\n",
        "    '''\n",
        "        Actual  predict\n",
        "    TN: Good 0   Good 0   -> get money from profit\n",
        "    TP: Bad 1    Bad 1  -> we will not issue profit (-) = 0 \n",
        "    FN: Good 0    Bad 1   -> we will not issue profit (+) = 0\n",
        "    FP: Bad 1    Good 0  -> we lost, cost > revenue profit (-)\n",
        "    '''\n",
        "    df = pd.DataFrame({'actual': y_true,\n",
        "                        'predict': y_pred,\n",
        "                       'profit':profit})\n",
        "    # TN\n",
        "    df.loc[(df['actual']==0) & (df['predict']==0), ['profit']] = profit\n",
        "\n",
        "    # TP \n",
        "    df.loc[(df['actual']==1) & (df['predict']==1), ['profit']] = 0\n",
        "\n",
        "    # FN\n",
        "    df.loc[(df['actual']==0) & (df['predict']==1), ['profit']] = 0\n",
        "\n",
        "    # FP\n",
        "    df.loc[(df['actual']==1) & (df['predict']==0), ['profit']] = profit\n",
        "    return df['profit'].sum()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "W7YI-n_D05TR",
        "outputId": "4cffaab8-802e-483a-f211-4e7015ccc2a1"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "13039536.921046285"
            ]
          },
          "execution_count": 26,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# url = 'https://raw.githubusercontent.com/sorayutmild/loan-default-prediction/main/after_trans_payment_for_loan.csv?raw=true'\n",
        "# after_trans_for_loan = pd.read_csv(url)\n",
        "\n",
        "# interest_rate_peryear = 0.05\n",
        "\n",
        "# revenue = cal_revenue(total_amount=X['remainder__loan_amount'], \n",
        "#                 loan_duration_month=X['remainder__loan_duration'], \n",
        "#                 paid_month=after_trans_for_loan['count_loan_trans'], \n",
        "#                 interest_rate_peryear=interest_rate_peryear)\n",
        "\n",
        "# cost = cal_cost(loan_payment=X['remainder__loan_payments'], \n",
        "#                 loan_duration_month=X['remainder__loan_duration'],\n",
        "#                 paid_month=after_trans_for_loan['count_loan_trans'])\n",
        "\n",
        "# profit = revenue - cost\n",
        "# profit.sum()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "id": "0o3hQDn7CH41"
      },
      "outputs": [],
      "source": [
        "res =  kfold_test_smote(selected_feature_X, y, selected_model, account_id=raw_df['account_id'].values, k=5, threshold=0.8534)\n",
        "report  = res.sort_values('account_id').set_index('test_idx')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "metadata": {
        "id": "I8WdAM6kBqfx"
      },
      "outputs": [],
      "source": [
        "report['y_true'] = y"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "metadata": {
        "id": "4RvMoLc-mNqo"
      },
      "outputs": [],
      "source": [
        "report[['y_true', 'y_pred', 'probs', 'account_id']].to_csv('report/report_xgb.csv',index=False)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Done"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 455
        },
        "id": "7ODDokvyEklB",
        "outputId": "c097edd4-537e-431e-da5c-824a4a29f6f0"
      },
      "outputs": [
        {
          "data": {
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              "      <th>y_true</th>\n",
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              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0.0</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.017466</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1.0</th>\n",
              "      <td>1.0</td>\n",
              "      <td>0.639199</td>\n",
              "      <td>19.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2.0</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.030918</td>\n",
              "      <td>25.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3.0</th>\n",
              "      <td>1.0</td>\n",
              "      <td>0.992415</td>\n",
              "      <td>37.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4.0</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.054533</td>\n",
              "      <td>38.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>677.0</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.009702</td>\n",
              "      <td>11327.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>678.0</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.053159</td>\n",
              "      <td>11328.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>679.0</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.362870</td>\n",
              "      <td>11349.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>680.0</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.213898</td>\n",
              "      <td>11359.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>681.0</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.130839</td>\n",
              "      <td>11362.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>682 rows × 8 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-9a564bbb-75e9-44ab-965f-5a3eea69632e')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\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=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <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",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert: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-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",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-9a564bbb-75e9-44ab-965f-5a3eea69632e 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-9a564bbb-75e9-44ab-965f-5a3eea69632e');\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_true     probs  account_id  y_pred  case_TP  case_FN  case_FP  \\\n",
              "test_idx                                                                    \n",
              "0.0          0.0  0.017466         2.0       0        0        0        0   \n",
              "1.0          1.0  0.639199        19.0       0        0        1        0   \n",
              "2.0          0.0  0.030918        25.0       0        0        0        0   \n",
              "3.0          1.0  0.992415        37.0       1        1        0        0   \n",
              "4.0          0.0  0.054533        38.0       0        0        0        0   \n",
              "...          ...       ...         ...     ...      ...      ...      ...   \n",
              "677.0        0.0  0.009702     11327.0       0        0        0        0   \n",
              "678.0        0.0  0.053159     11328.0       0        0        0        0   \n",
              "679.0        0.0  0.362870     11349.0       0        0        0        0   \n",
              "680.0        0.0  0.213898     11359.0       0        0        0        0   \n",
              "681.0        0.0  0.130839     11362.0       0        0        0        0   \n",
              "\n",
              "          case_TN  \n",
              "test_idx           \n",
              "0.0             1  \n",
              "1.0             0  \n",
              "2.0             1  \n",
              "3.0             0  \n",
              "4.0             1  \n",
              "...           ...  \n",
              "677.0           1  \n",
              "678.0           1  \n",
              "679.0           1  \n",
              "680.0           1  \n",
              "681.0           1  \n",
              "\n",
              "[682 rows x 8 columns]"
            ]
          },
          "execution_count": 30,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "report['y_pred'] = to_labels(report['probs'],0.85)\n",
        "report['case_TP'] = report['y_true'].astype(int) & report['y_pred'].astype(int) \n",
        "report['case_FN'] = ((report['y_true'].astype(int) == 1) & (report['y_pred'].astype(int) == 0)).astype(int)\n",
        "report['case_FP'] = ((report['y_true'].astype(int) == 0) & (report['y_pred'].astype(int) == 1)).astype(int)\n",
        "report['case_TN'] = ( report['y_true'].astype(int) | report['y_pred'].astype(int) == 0).astype(int)\n",
        "report"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 31,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "aTvnfOHxixMl",
        "outputId": "9ab3ed99-a01b-4f28-d73c-34a640b0f18f"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "\n",
        "ax= plt.subplot()\n",
        "cf_matrix = [[report['case_TN'].sum(),  report['case_FP'].sum()],\n",
        "       [ report['case_FN'].sum(), report['case_TP'].sum() ]]\n",
        "sns.heatmap(cf_matrix, annot=True, ax=ax)\n",
        "    # labels, title and ticks\n",
        "ax.set_xlabel('Predicted labels');ax.set_ylabel('True labels'); \n",
        "ax.set_title('Confusion Matrix'); \n",
        "ax.xaxis.set_ticklabels(['Good', 'Bad']); ax.yaxis.set_ticklabels(['Good', 'Bad']);\n",
        "plt.show()\n"
      ]
    }
  ],
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