Financial_distress_prediction.ipynb

{
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      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
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      "source": [
        "<a href=\"https://colab.research.google.com/github/rdemarqui/financial_distress_prediction/blob/main/Financial_distress_prediction.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "09xPYRvMK1qj"
      },
      "source": [
        "# Financial Distress Prediction"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZNIxKljhk1tn"
      },
      "source": [
        "Source: https://www.kaggle.com/competitions/GiveMeSomeCredit/"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "5YcLxGRMzd9I"
      },
      "outputs": [],
      "source": [
        "!pip install -q scikit-optimize\n",
        "!pip install -q shap"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "cgHV9TNsKr74",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a30b6a55-6e85-4cee-cc91-965d559ea712"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Using `tqdm.autonotebook.tqdm` in notebook mode. Use `tqdm.tqdm` instead to force console mode (e.g. in jupyter console)\n"
          ]
        }
      ],
      "source": [
        "import os\n",
        "import zipfile\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "from sklearn.model_selection import GridSearchCV, RandomizedSearchCV, StratifiedKFold, cross_val_score\n",
        "from sklearn import metrics\n",
        "from skopt import BayesSearchCV\n",
        "from imblearn.over_sampling import SMOTE\n",
        "import shap"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "hpX4kfbMcOGf"
      },
      "outputs": [],
      "source": [
        "from sklearn.ensemble import AdaBoostClassifier\n",
        "from sklearn.ensemble import HistGradientBoostingClassifier\n",
        "from sklearn.ensemble import ExtraTreesClassifier\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.naive_bayes import GaussianNB\n",
        "from sklearn.naive_bayes import BernoulliNB\n",
        "from sklearn.naive_bayes import MultinomialNB\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from xgboost import XGBClassifier\n",
        "from lightgbm import LGBMClassifier"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Openning Files"
      ],
      "metadata": {
        "id": "WxZt97GaGiTT"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "p8b0_7BFK0lP"
      },
      "outputs": [],
      "source": [
        "# Getting current working directory\n",
        "current_dir = os.getcwd()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "xSD9yINyMyPd"
      },
      "outputs": [],
      "source": [
        "# Downloading file\n",
        "!wget -q https://github.com/rdemarqui/financial_distress_prediction/raw/main/datasets/GiveMeSomeCredit.zip"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "MF7Q-7SZNNsV"
      },
      "outputs": [],
      "source": [
        "# Opening datasets\n",
        "zf = zipfile.ZipFile(os.path.join(current_dir, 'GiveMeSomeCredit.zip'))\n",
        "df_train = pd.read_csv(zf.open('cs-training.csv'), index_col=0, low_memory=False)\n",
        "df_test = pd.read_csv(zf.open('cs-test.csv'), index_col=0, low_memory=False)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-okzye9NQn-n"
      },
      "source": [
        "## Exploratory Data Analysis (EDA)"
      ]
    },
    {
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      "execution_count": null,
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        "colab": {
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        "id": "EBSfp8ktBgVp",
        "outputId": "88a5a15b-c7b8-4972-be02-44a700093bd9"
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      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(150000, 11)"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ],
      "source": [
        "# Checking null values and data types\n",
        "df_train.shape"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_train.info()"
      ],
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        "id": "OFf4K4Z5jgn1",
        "outputId": "2db0ceab-9b5c-4c19-bf7a-c99defddd2ba",
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      "outputs": [
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          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 150000 entries, 1 to 150000\n",
            "Data columns (total 11 columns):\n",
            " #   Column                                Non-Null Count   Dtype  \n",
            "---  ------                                --------------   -----  \n",
            " 0   SeriousDlqin2yrs                      150000 non-null  int64  \n",
            " 1   RevolvingUtilizationOfUnsecuredLines  150000 non-null  float64\n",
            " 2   age                                   150000 non-null  int64  \n",
            " 3   NumberOfTime30-59DaysPastDueNotWorse  150000 non-null  int64  \n",
            " 4   DebtRatio                             150000 non-null  float64\n",
            " 5   MonthlyIncome                         120269 non-null  float64\n",
            " 6   NumberOfOpenCreditLinesAndLoans       150000 non-null  int64  \n",
            " 7   NumberOfTimes90DaysLate               150000 non-null  int64  \n",
            " 8   NumberRealEstateLoansOrLines          150000 non-null  int64  \n",
            " 9   NumberOfTime60-89DaysPastDueNotWorse  150000 non-null  int64  \n",
            " 10  NumberOfDependents                    146076 non-null  float64\n",
            "dtypes: float64(4), int64(7)\n",
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            "text/plain": [
              "                                         count         mean           std  \\\n",
              "SeriousDlqin2yrs                      150000.0     0.066840      0.249746   \n",
              "RevolvingUtilizationOfUnsecuredLines  150000.0     6.048438    249.755371   \n",
              "age                                   150000.0    52.295207     14.771866   \n",
              "NumberOfTime30-59DaysPastDueNotWorse  150000.0     0.421033      4.192781   \n",
              "DebtRatio                             150000.0   353.005076   2037.818523   \n",
              "MonthlyIncome                         120269.0  6670.221237  14384.674215   \n",
              "NumberOfOpenCreditLinesAndLoans       150000.0     8.452760      5.145951   \n",
              "NumberOfTimes90DaysLate               150000.0     0.265973      4.169304   \n",
              "NumberRealEstateLoansOrLines          150000.0     1.018240      1.129771   \n",
              "NumberOfTime60-89DaysPastDueNotWorse  150000.0     0.240387      4.155179   \n",
              "NumberOfDependents                    146076.0     0.757222      1.115086   \n",
              "\n",
              "                                      min          25%          50%  \\\n",
              "SeriousDlqin2yrs                      0.0     0.000000     0.000000   \n",
              "RevolvingUtilizationOfUnsecuredLines  0.0     0.029867     0.154181   \n",
              "age                                   0.0    41.000000    52.000000   \n",
              "NumberOfTime30-59DaysPastDueNotWorse  0.0     0.000000     0.000000   \n",
              "DebtRatio                             0.0     0.175074     0.366508   \n",
              "MonthlyIncome                         0.0  3400.000000  5400.000000   \n",
              "NumberOfOpenCreditLinesAndLoans       0.0     5.000000     8.000000   \n",
              "NumberOfTimes90DaysLate               0.0     0.000000     0.000000   \n",
              "NumberRealEstateLoansOrLines          0.0     0.000000     1.000000   \n",
              "NumberOfTime60-89DaysPastDueNotWorse  0.0     0.000000     0.000000   \n",
              "NumberOfDependents                    0.0     0.000000     0.000000   \n",
              "\n",
              "                                              75%        max  \n",
              "SeriousDlqin2yrs                         0.000000        1.0  \n",
              "RevolvingUtilizationOfUnsecuredLines     0.559046    50708.0  \n",
              "age                                     63.000000      109.0  \n",
              "NumberOfTime30-59DaysPastDueNotWorse     0.000000       98.0  \n",
              "DebtRatio                                0.868254   329664.0  \n",
              "MonthlyIncome                         8249.000000  3008750.0  \n",
              "NumberOfOpenCreditLinesAndLoans         11.000000       58.0  \n",
              "NumberOfTimes90DaysLate                  0.000000       98.0  \n",
              "NumberRealEstateLoansOrLines             2.000000       54.0  \n",
              "NumberOfTime60-89DaysPastDueNotWorse     0.000000       98.0  \n",
              "NumberOfDependents                       1.000000       20.0  "
            ],
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              "      <th></th>\n",
              "      <th>count</th>\n",
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              "    <tr>\n",
              "      <th>SeriousDlqin2yrs</th>\n",
              "      <td>150000.0</td>\n",
              "      <td>0.066840</td>\n",
              "      <td>0.249746</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
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              "      <td>1.0</td>\n",
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              "      <th>RevolvingUtilizationOfUnsecuredLines</th>\n",
              "      <td>150000.0</td>\n",
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              "      <td>0.029867</td>\n",
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              "      <td>0.559046</td>\n",
              "      <td>50708.0</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>41.000000</td>\n",
              "      <td>52.000000</td>\n",
              "      <td>63.000000</td>\n",
              "      <td>109.0</td>\n",
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              "    <tr>\n",
              "      <th>NumberOfTime30-59DaysPastDueNotWorse</th>\n",
              "      <td>150000.0</td>\n",
              "      <td>0.421033</td>\n",
              "      <td>4.192781</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>98.0</td>\n",
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              "    <tr>\n",
              "      <th>DebtRatio</th>\n",
              "      <td>150000.0</td>\n",
              "      <td>353.005076</td>\n",
              "      <td>2037.818523</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.175074</td>\n",
              "      <td>0.366508</td>\n",
              "      <td>0.868254</td>\n",
              "      <td>329664.0</td>\n",
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              "    <tr>\n",
              "      <th>MonthlyIncome</th>\n",
              "      <td>120269.0</td>\n",
              "      <td>6670.221237</td>\n",
              "      <td>14384.674215</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3400.000000</td>\n",
              "      <td>5400.000000</td>\n",
              "      <td>8249.000000</td>\n",
              "      <td>3008750.0</td>\n",
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              "    <tr>\n",
              "      <th>NumberOfOpenCreditLinesAndLoans</th>\n",
              "      <td>150000.0</td>\n",
              "      <td>8.452760</td>\n",
              "      <td>5.145951</td>\n",
              "      <td>0.0</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>11.000000</td>\n",
              "      <td>58.0</td>\n",
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              "    <tr>\n",
              "      <th>NumberOfTimes90DaysLate</th>\n",
              "      <td>150000.0</td>\n",
              "      <td>0.265973</td>\n",
              "      <td>4.169304</td>\n",
              "      <td>0.0</td>\n",
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              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>98.0</td>\n",
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              "    <tr>\n",
              "      <th>NumberRealEstateLoansOrLines</th>\n",
              "      <td>150000.0</td>\n",
              "      <td>1.018240</td>\n",
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              "      <td>150000.0</td>\n",
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              "      <td>0.000000</td>\n",
              "      <td>98.0</td>\n",
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              "      <th>NumberOfDependents</th>\n",
              "      <td>146076.0</td>\n",
              "      <td>0.757222</td>\n",
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              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-4a6a51fc-babc-4abe-9cca-e7ffdb1a0868 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ],
      "source": [
        "df_train.describe().T"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9O9uCn0ND91b"
      },
      "source": [
        "* Dataset has only numeric features.\n",
        "* Null values on *MonthlyIncome* and *NumberOfDependents*\n",
        "* *Age* has some values filled with 0.\n",
        "\n",
        "Unfortunately few estimators can natively handle missing data. Due to this, we'll fill null values with median, since some features have outliers."
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Correlation Analysis**\n",
        "\n",
        "At the end of this study we will check which features were most important for the algorithm. Therefore, we need to check whether there is a high correlation between the those features, since collinearity / multicollinearity impacts the study of feature contribution."
      ],
      "metadata": {
        "id": "4k1_OPMtz4Sc"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "correlation_matrix = df_train.iloc[:, 1:].corr(method='pearson')\n",
        "mask = np.triu(np.ones_like(correlation_matrix, dtype=bool))\n",
        "plt.figure(figsize=(7, 4))\n",
        "sns.heatmap(correlation_matrix, annot=True, cmap='coolwarm', fmt=\".2f\", linewidths=0.5, mask=mask)\n",
        "plt.title('Pearson Correlation', fontsize=16)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 683
        },
        "id": "LDK2vRRT6OGL",
        "outputId": "48b6d318-b33f-406f-9052-52b51984640f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 700x400 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "As we can see above, we have a high correlation between `NumberOfTime30-59DaysPastDueNotWorse`, `NumberOfTime60-89DaysPastDueNotWorse` and `NumberOfTimes90DaysLate`. In this case, we could maintain only `NumberOfTime30-59DaysPastDueNotWorse`, whereas borrowers who had been late for longer periods were late first in this shorter interval, but in previous tests we've seen that it costs some accuracy points. As accuracy is a sensitive theme in this type of case, we decided to maintain all features."
      ],
      "metadata": {
        "id": "oG_goRjP8n_M"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#df_train_final = df_train.drop(columns=['NumberOfTime60-89DaysPastDueNotWorse',\n",
        "#                                        'NumberOfTimes90DaysLate']).copy()"
      ],
      "metadata": {
        "id": "4upCfqul8vyr"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Filling missing data**"
      ],
      "metadata": {
        "id": "9Y-34Vs53q7c"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "hdF0tMHZKYQh"
      },
      "outputs": [],
      "source": [
        "df_train_final = df_train.copy()\n",
        "\n",
        "fill_MonthlyIncome = df_train_final['MonthlyIncome'].median()\n",
        "fill_NumberOfDependents = df_train_final['NumberOfDependents'].median()\n",
        "\n",
        "df_train_final['MonthlyIncome'].fillna(fill_MonthlyIncome, inplace=True)\n",
        "df_train_final['NumberOfDependents'].fillna(fill_NumberOfDependents, inplace=True)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Checking y proportion\n",
        "df_train_final['SeriousDlqin2yrs'].value_counts()/df_train.shape[0]"
      ],
      "metadata": {
        "id": "ODJzCEg4VRgs",
        "outputId": "e9c73cf0-9e69-47c6-e06c-f7a2707321e5",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    0.93316\n",
              "1    0.06684\n",
              "Name: SeriousDlqin2yrs, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Dataset have inbalance label. We'll tackle this problem applying oversampling in the training step."
      ],
      "metadata": {
        "id": "3hKGLiB6WUFX"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "YWwEfE5GJ4EJ"
      },
      "outputs": [],
      "source": [
        "# Splitting data\n",
        "X_train = df_train_final.drop(columns=['SeriousDlqin2yrs'])\n",
        "y_train = df_train_final['SeriousDlqin2yrs']"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "14cNlM04VN6p"
      },
      "source": [
        "##Training"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FIbiTbUZw-W9"
      },
      "source": [
        "We're going to use cross validation to train all models. In this case we will split in 1/5, where 80% for training and 20% for validation."
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def cross_validation(model=None, X=None, y=None, n_splits=5, shuffle=True, oversampling=False, random_state=42):\n",
        "  kfold = StratifiedKFold(n_splits=n_splits, shuffle=shuffle, random_state=42)\n",
        "  scores = []\n",
        "\n",
        "  for idx, (idx_train, idx_validation) in enumerate(kfold.split(X, y)):\n",
        "    X_train_split = X.iloc[idx_train, :]\n",
        "    y_train_split = y.iloc[idx_train]\n",
        "\n",
        "    if oversampling:\n",
        "      sm = SMOTE(random_state=random_state)\n",
        "      X_train_split, y_train_split = sm.fit_resample(X_train_split, y_train_split)\n",
        "\n",
        "    model.fit(X_train_split, y_train_split)\n",
        "\n",
        "    X_val_split = X.iloc[idx_validation, :]\n",
        "    y_val_split = y.iloc[idx_validation]\n",
        "\n",
        "    val_predict = model.predict_proba(X_val_split)[:, 1]\n",
        "\n",
        "    split_score = round(metrics.roc_auc_score(y_val_split, val_predict), 6)\n",
        "    scores.append(split_score)\n",
        "\n",
        "  return scores"
      ],
      "metadata": {
        "id": "jdCX7lLIAvUQ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def train_models(all_models, oversampling=None):\n",
        "  all_results = {}\n",
        "  for model in all_models:\n",
        "    model_name = str(model).split('(')[0]\n",
        "    print(f'Training: {model_name}')\n",
        "    result = cross_validation(model, X_train, y_train, oversampling=oversampling)\n",
        "    all_results[model_name] = result\n",
        "\n",
        "  df_results = (pd.DataFrame.from_dict(all_results).T)\n",
        "  df_results['mean'] = df_results.mean(axis=1)\n",
        "  df_results = df_results.sort_values(by=['mean'], ascending=False)\n",
        "  return df_results"
      ],
      "metadata": {
        "id": "5OTYShUN09iO"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "s6O_qno9Jm_a"
      },
      "source": [
        "### Choosing Best Model"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "First, we'll try many algorithms to see which one does better."
      ],
      "metadata": {
        "id": "T7s4LqciByJ-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Listing all models\n",
        "mRandState = {'random_state':42}\n",
        "all_models = [\n",
        "    AdaBoostClassifier(**mRandState), HistGradientBoostingClassifier(**mRandState),\n",
        "    ExtraTreesClassifier(**mRandState), RandomForestClassifier(**mRandState),\n",
        "    LogisticRegression(max_iter=3000, **mRandState), GaussianNB(),\n",
        "    BernoulliNB(), MultinomialNB(), DecisionTreeClassifier(**mRandState),\n",
        "    XGBClassifier(**mRandState), LGBMClassifier(verbose=-1, **mRandState)\n",
        "    ]"
      ],
      "metadata": {
        "id": "mNHDXEsLYES5"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Oversampling"
      ],
      "metadata": {
        "id": "9MozcXiVZJm2"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "As seen before, this dataset have inbalance label (93% and 7%). Let's first apply oversampling."
      ],
      "metadata": {
        "id": "8EG_11hjXKEF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "results_oversampling = train_models(all_models, oversampling=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vOsY1CTn18WP",
        "outputId": "7e4dcd6b-05b2-4afb-9f72-c092f81bb487"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training: AdaBoostClassifier\n",
            "Training: HistGradientBoostingClassifier\n",
            "Training: ExtraTreesClassifier\n",
            "Training: RandomForestClassifier\n",
            "Training: LogisticRegression\n",
            "Training: GaussianNB\n",
            "Training: BernoulliNB\n",
            "Training: MultinomialNB\n",
            "Training: DecisionTreeClassifier\n",
            "Training: XGBClassifier\n",
            "Training: LGBMClassifier\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Checking all results using oversampling\n",
        "results_oversampling"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 394
        },
        "id": "jLgdlA1iV5BP",
        "outputId": "a1f39138-fd5d-4818-d6fd-4c80e5a98da4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                       0         1         2         3  \\\n",
              "HistGradientBoostingClassifier  0.828963  0.835007  0.839808  0.830350   \n",
              "LGBMClassifier                  0.828483  0.831316  0.837434  0.829035   \n",
              "ExtraTreesClassifier            0.819238  0.826719  0.831449  0.819978   \n",
              "RandomForestClassifier          0.814953  0.824280  0.828224  0.819164   \n",
              "AdaBoostClassifier              0.816548  0.817743  0.829322  0.816280   \n",
              "XGBClassifier                   0.815077  0.817124  0.821349  0.814809   \n",
              "BernoulliNB                     0.784014  0.789299  0.796908  0.779174   \n",
              "LogisticRegression              0.693786  0.813415  0.729233  0.795217   \n",
              "GaussianNB                      0.685544  0.696976  0.682401  0.667848   \n",
              "DecisionTreeClassifier          0.626428  0.635741  0.630230  0.638566   \n",
              "MultinomialNB                   0.755333  0.551972  0.563029  0.548038   \n",
              "\n",
              "                                       4      mean  \n",
              "HistGradientBoostingClassifier  0.832577  0.833341  \n",
              "LGBMClassifier                  0.830404  0.831334  \n",
              "ExtraTreesClassifier            0.824305  0.824338  \n",
              "RandomForestClassifier          0.820845  0.821493  \n",
              "AdaBoostClassifier              0.826966  0.821372  \n",
              "XGBClassifier                   0.814398  0.816551  \n",
              "BernoulliNB                     0.794267  0.788732  \n",
              "LogisticRegression              0.713678  0.749066  \n",
              "GaussianNB                      0.689921  0.684538  \n",
              "DecisionTreeClassifier          0.634739  0.633141  \n",
              "MultinomialNB                   0.682871  0.620249  "
            ],
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              "      <th>0</th>\n",
              "      <th>1</th>\n",
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              "      <th>mean</th>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>HistGradientBoostingClassifier</th>\n",
              "      <td>0.828963</td>\n",
              "      <td>0.835007</td>\n",
              "      <td>0.839808</td>\n",
              "      <td>0.830350</td>\n",
              "      <td>0.832577</td>\n",
              "      <td>0.833341</td>\n",
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              "      <th>LGBMClassifier</th>\n",
              "      <td>0.828483</td>\n",
              "      <td>0.831316</td>\n",
              "      <td>0.837434</td>\n",
              "      <td>0.829035</td>\n",
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              "      <td>0.819238</td>\n",
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              "      <td>0.819978</td>\n",
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              "      <th>RandomForestClassifier</th>\n",
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              "      <td>0.824280</td>\n",
              "      <td>0.828224</td>\n",
              "      <td>0.819164</td>\n",
              "      <td>0.820845</td>\n",
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              "      <th>AdaBoostClassifier</th>\n",
              "      <td>0.816548</td>\n",
              "      <td>0.817743</td>\n",
              "      <td>0.829322</td>\n",
              "      <td>0.816280</td>\n",
              "      <td>0.826966</td>\n",
              "      <td>0.821372</td>\n",
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              "      <th>XGBClassifier</th>\n",
              "      <td>0.815077</td>\n",
              "      <td>0.817124</td>\n",
              "      <td>0.821349</td>\n",
              "      <td>0.814809</td>\n",
              "      <td>0.814398</td>\n",
              "      <td>0.816551</td>\n",
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              "    <tr>\n",
              "      <th>BernoulliNB</th>\n",
              "      <td>0.784014</td>\n",
              "      <td>0.789299</td>\n",
              "      <td>0.796908</td>\n",
              "      <td>0.779174</td>\n",
              "      <td>0.794267</td>\n",
              "      <td>0.788732</td>\n",
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              "      <th>LogisticRegression</th>\n",
              "      <td>0.693786</td>\n",
              "      <td>0.813415</td>\n",
              "      <td>0.729233</td>\n",
              "      <td>0.795217</td>\n",
              "      <td>0.713678</td>\n",
              "      <td>0.749066</td>\n",
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              "      <th>GaussianNB</th>\n",
              "      <td>0.685544</td>\n",
              "      <td>0.696976</td>\n",
              "      <td>0.682401</td>\n",
              "      <td>0.667848</td>\n",
              "      <td>0.689921</td>\n",
              "      <td>0.684538</td>\n",
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              "    <tr>\n",
              "      <th>DecisionTreeClassifier</th>\n",
              "      <td>0.626428</td>\n",
              "      <td>0.635741</td>\n",
              "      <td>0.630230</td>\n",
              "      <td>0.638566</td>\n",
              "      <td>0.634739</td>\n",
              "      <td>0.633141</td>\n",
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              "      <th>MultinomialNB</th>\n",
              "      <td>0.755333</td>\n",
              "      <td>0.551972</td>\n",
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              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-eeda793d-0e08-42b8-a549-137d3279d16f button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Unbalanced"
      ],
      "metadata": {
        "id": "gWCGCj34ZbSD"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "results_unbalanced = train_models(all_models, oversampling=False)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "71d9ecbb-c20a-48b5-a790-a3fe4ef8408f",
        "id": "dkjMaiHwZlrs"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training: AdaBoostClassifier\n",
            "Training: HistGradientBoostingClassifier\n",
            "Training: ExtraTreesClassifier\n",
            "Training: RandomForestClassifier\n",
            "Training: LogisticRegression\n",
            "Training: GaussianNB\n",
            "Training: BernoulliNB\n",
            "Training: MultinomialNB\n",
            "Training: DecisionTreeClassifier\n",
            "Training: XGBClassifier\n",
            "Training: LGBMClassifier\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Checking all results using oversampling\n",
        "results_unbalanced"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 394
        },
        "id": "RpmY-PF1Zlrt",
        "outputId": "d990334e-1d37-4863-ce8a-18f802643489"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                       0         1         2         3  \\\n",
              "LGBMClassifier                  0.862495  0.864936  0.869228  0.859360   \n",
              "HistGradientBoostingClassifier  0.864703  0.863581  0.869639  0.859239   \n",
              "AdaBoostClassifier              0.858279  0.860829  0.864372  0.853150   \n",
              "XGBClassifier                   0.854276  0.856128  0.862880  0.851565   \n",
              "RandomForestClassifier          0.835511  0.839263  0.843631  0.836495   \n",
              "ExtraTreesClassifier            0.821132  0.821706  0.825852  0.819680   \n",
              "BernoulliNB                     0.793406  0.796862  0.800310  0.782885   \n",
              "GaussianNB                      0.707258  0.711964  0.709642  0.690416   \n",
              "LogisticRegression              0.690640  0.697300  0.694940  0.694456   \n",
              "MultinomialNB                   0.730615  0.567083  0.597694  0.557186   \n",
              "DecisionTreeClassifier          0.612381  0.608981  0.615917  0.615825   \n",
              "\n",
              "                                       4      mean  \n",
              "LGBMClassifier                  0.868900  0.864984  \n",
              "HistGradientBoostingClassifier  0.867612  0.864955  \n",
              "AdaBoostClassifier              0.860769  0.859480  \n",
              "XGBClassifier                   0.860235  0.857017  \n",
              "RandomForestClassifier          0.846033  0.840187  \n",
              "ExtraTreesClassifier            0.828793  0.823433  \n",
              "BernoulliNB                     0.805937  0.795880  \n",
              "GaussianNB                      0.707686  0.705393  \n",
              "LogisticRegression              0.702804  0.696028  \n",
              "MultinomialNB                   0.714243  0.633364  \n",
              "DecisionTreeClassifier          0.614898  0.613600  "
            ],
            "text/html": [
              "\n",
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              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
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              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>0</th>\n",
              "      <th>1</th>\n",
              "      <th>2</th>\n",
              "      <th>3</th>\n",
              "      <th>4</th>\n",
              "      <th>mean</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>LGBMClassifier</th>\n",
              "      <td>0.862495</td>\n",
              "      <td>0.864936</td>\n",
              "      <td>0.869228</td>\n",
              "      <td>0.859360</td>\n",
              "      <td>0.868900</td>\n",
              "      <td>0.864984</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>HistGradientBoostingClassifier</th>\n",
              "      <td>0.864703</td>\n",
              "      <td>0.863581</td>\n",
              "      <td>0.869639</td>\n",
              "      <td>0.859239</td>\n",
              "      <td>0.867612</td>\n",
              "      <td>0.864955</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>AdaBoostClassifier</th>\n",
              "      <td>0.858279</td>\n",
              "      <td>0.860829</td>\n",
              "      <td>0.864372</td>\n",
              "      <td>0.853150</td>\n",
              "      <td>0.860769</td>\n",
              "      <td>0.859480</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>XGBClassifier</th>\n",
              "      <td>0.854276</td>\n",
              "      <td>0.856128</td>\n",
              "      <td>0.862880</td>\n",
              "      <td>0.851565</td>\n",
              "      <td>0.860235</td>\n",
              "      <td>0.857017</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>RandomForestClassifier</th>\n",
              "      <td>0.835511</td>\n",
              "      <td>0.839263</td>\n",
              "      <td>0.843631</td>\n",
              "      <td>0.836495</td>\n",
              "      <td>0.846033</td>\n",
              "      <td>0.840187</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ExtraTreesClassifier</th>\n",
              "      <td>0.821132</td>\n",
              "      <td>0.821706</td>\n",
              "      <td>0.825852</td>\n",
              "      <td>0.819680</td>\n",
              "      <td>0.828793</td>\n",
              "      <td>0.823433</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>BernoulliNB</th>\n",
              "      <td>0.793406</td>\n",
              "      <td>0.796862</td>\n",
              "      <td>0.800310</td>\n",
              "      <td>0.782885</td>\n",
              "      <td>0.805937</td>\n",
              "      <td>0.795880</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>GaussianNB</th>\n",
              "      <td>0.707258</td>\n",
              "      <td>0.711964</td>\n",
              "      <td>0.709642</td>\n",
              "      <td>0.690416</td>\n",
              "      <td>0.707686</td>\n",
              "      <td>0.705393</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LogisticRegression</th>\n",
              "      <td>0.690640</td>\n",
              "      <td>0.697300</td>\n",
              "      <td>0.694940</td>\n",
              "      <td>0.694456</td>\n",
              "      <td>0.702804</td>\n",
              "      <td>0.696028</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>MultinomialNB</th>\n",
              "      <td>0.730615</td>\n",
              "      <td>0.567083</td>\n",
              "      <td>0.597694</td>\n",
              "      <td>0.557186</td>\n",
              "      <td>0.714243</td>\n",
              "      <td>0.633364</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>DecisionTreeClassifier</th>\n",
              "      <td>0.612381</td>\n",
              "      <td>0.608981</td>\n",
              "      <td>0.615917</td>\n",
              "      <td>0.615825</td>\n",
              "      <td>0.614898</td>\n",
              "      <td>0.613600</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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              "\n",
              "  <div class=\"colab-df-container\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\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",
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              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
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              "\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-cf9aac04-d7fa-49b9-9f98-7fb9868692f6 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-cf9aac04-d7fa-49b9-9f98-7fb9868692f6');\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",
              "\n",
              "\n",
              "<div id=\"df-264c4d73-550c-4108-8a5f-3e33f96806c3\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-264c4d73-550c-4108-8a5f-3e33f96806c3')\"\n",
              "            title=\"Suggest charts.\"\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",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-264c4d73-550c-4108-8a5f-3e33f96806c3 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Comparing Results"
      ],
      "metadata": {
        "id": "hOf-2_ODg-xP"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df_compare = pd.DataFrame(results_oversampling['mean']).rename(columns={'mean':'mean_over'})\n",
        "df_compare = df_compare.join(results_unbalanced['mean'])\n",
        "df_compare = df_compare.sort_values(by=['mean'], ascending=True)\n",
        "ax = df_compare.plot.barh()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 430
        },
        "id": "kA_lDad-g8Ke",
        "outputId": "528f9b20-08aa-461d-fcfe-24aec2480353"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_compare = df_compare.sort_values(by=['mean'], ascending=False)\n",
        "df_compare['difference'] = df_compare['mean_over'] - df_compare['mean']\n",
        "df_compare"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 394
        },
        "id": "pbzzrDW2v-LR",
        "outputId": "7d169678-5e3c-45ae-dc31-ae40a538ad6c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                mean_over      mean  difference\n",
              "LGBMClassifier                   0.831334  0.864984   -0.033649\n",
              "HistGradientBoostingClassifier   0.833341  0.864955   -0.031614\n",
              "AdaBoostClassifier               0.821372  0.859480   -0.038108\n",
              "XGBClassifier                    0.816551  0.857017   -0.040465\n",
              "RandomForestClassifier           0.821493  0.840187   -0.018693\n",
              "ExtraTreesClassifier             0.824338  0.823433    0.000905\n",
              "BernoulliNB                      0.788732  0.795880   -0.007148\n",
              "GaussianNB                       0.684538  0.705393   -0.020855\n",
              "LogisticRegression               0.749066  0.696028    0.053038\n",
              "MultinomialNB                    0.620249  0.633364   -0.013116\n",
              "DecisionTreeClassifier           0.633141  0.613600    0.019540"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-e9446dba-e933-471e-ae34-60f2839931f2\" 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>mean_over</th>\n",
              "      <th>mean</th>\n",
              "      <th>difference</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>LGBMClassifier</th>\n",
              "      <td>0.831334</td>\n",
              "      <td>0.864984</td>\n",
              "      <td>-0.033649</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>HistGradientBoostingClassifier</th>\n",
              "      <td>0.833341</td>\n",
              "      <td>0.864955</td>\n",
              "      <td>-0.031614</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>AdaBoostClassifier</th>\n",
              "      <td>0.821372</td>\n",
              "      <td>0.859480</td>\n",
              "      <td>-0.038108</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>XGBClassifier</th>\n",
              "      <td>0.816551</td>\n",
              "      <td>0.857017</td>\n",
              "      <td>-0.040465</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>RandomForestClassifier</th>\n",
              "      <td>0.821493</td>\n",
              "      <td>0.840187</td>\n",
              "      <td>-0.018693</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ExtraTreesClassifier</th>\n",
              "      <td>0.824338</td>\n",
              "      <td>0.823433</td>\n",
              "      <td>0.000905</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>BernoulliNB</th>\n",
              "      <td>0.788732</td>\n",
              "      <td>0.795880</td>\n",
              "      <td>-0.007148</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>GaussianNB</th>\n",
              "      <td>0.684538</td>\n",
              "      <td>0.705393</td>\n",
              "      <td>-0.020855</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LogisticRegression</th>\n",
              "      <td>0.749066</td>\n",
              "      <td>0.696028</td>\n",
              "      <td>0.053038</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>MultinomialNB</th>\n",
              "      <td>0.620249</td>\n",
              "      <td>0.633364</td>\n",
              "      <td>-0.013116</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>DecisionTreeClassifier</th>\n",
              "      <td>0.633141</td>\n",
              "      <td>0.613600</td>\n",
              "      <td>0.019540</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-e9446dba-e933-471e-ae34-60f2839931f2')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
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              "      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",
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              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
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              "\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",
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              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-e9446dba-e933-471e-ae34-60f2839931f2 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-e9446dba-e933-471e-ae34-60f2839931f2');\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",
              "\n",
              "\n",
              "<div id=\"df-e0f3dcba-325f-496c-a2e6-98206c6a68dc\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-e0f3dcba-325f-496c-a2e6-98206c6a68dc')\"\n",
              "            title=\"Suggest charts.\"\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",
              "    <g>\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-e0f3dcba-325f-496c-a2e6-98206c6a68dc button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "As we can see, oversampling made more harm than good in most of our models.\n",
        "\n",
        "The best result was achieved by **LGBMClassifier**. Let's fine tuning it's parameters then.\n",
        "\n",
        "https://lightgbm.readthedocs.io/en/latest/pythonapi/lightgbm.LGBMClassifier.html"
      ],
      "metadata": {
        "id": "qxSdl-70xlRt"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mhY8D5b-yfwX"
      },
      "source": [
        "### Hyperparameter Tuning"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Since we're no longer using oversampling, we can perform optimization directly with cross_validation."
      ],
      "metadata": {
        "id": "MXW2QBKLoBdl"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "D5BygeQvw5ZF"
      },
      "outputs": [],
      "source": [
        "def param_opt(model, params, X, y, opt_method='', n_iter=10, score='roc_auc', n_splits=5, shuffle=False, n_jobs=4):\n",
        "  \"\"\"\n",
        "  Function to optimize algorithm hyperparameters\n",
        "     If opt_method = 'Grid' then all combinations of hyperparameters will be tested for GridSearchCV\n",
        "     If opt_method = 'Random', 10 hyperparameters will be tested randomly\n",
        "     If opt_method = 'Bayes', 10 hyperparameters will be randomly tested and then Bayesian optimization will be performed (default)\n",
        "  \"\"\"\n",
        "\n",
        "  # Defining kfold\n",
        "  if shuffle == False:\n",
        "    val_random = None\n",
        "  kfold = StratifiedKFold(n_splits=n_splits, shuffle=shuffle, random_state=val_random)\n",
        "\n",
        "  if opt_method.upper() == 'GRID':\n",
        "    grid = GridSearchCV(model, params, cv=kfold, n_jobs=n_jobs, scoring=score)\n",
        "  elif opt_method.upper() == 'RANDOM':\n",
        "    grid = RandomizedSearchCV(model, params, cv=kfold, n_iter=n_iter, n_jobs=n_jobs, random_state=val_random, scoring=score)\n",
        "  elif opt_method.upper() == 'BAYES':\n",
        "    # Optimizers['GP', 'RF', 'ET', 'GBRT']: https://scikit-optimize.github.io/stable/modules/generated/skopt.Optimizer.html#skopt.Optimizer\n",
        "    grid = BayesSearchCV(model, params, cv=kfold, n_iter=n_iter, n_jobs=n_jobs, optimizer_kwargs={'base_estimator':'GP'}, random_state=val_random, scoring=score)\n",
        "\n",
        "  # Training model\n",
        "  grid.fit(X, y)\n",
        "\n",
        "  # Best hiperparameters\n",
        "  best_params = dict(grid.best_params_)\n",
        "  print(\"Best params:\", best_params)\n",
        "\n",
        "  # Best score\n",
        "  best_score = grid.best_score_\n",
        "  print(f\"Best score({score}): {best_score:.5}\\n\")\n",
        "\n",
        "  return best_params"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Listing hyperparameters\n",
        "params = {}\n",
        "params['num_leaves'] = [10, 20, 31, 50]                         # (default=31)\n",
        "params['max_depth'] = [-1, 1]                                   # (default=-1)\n",
        "params['learning_rate'] = [0.0001, 0.001, 0.01, 0.1, 0.3, 0.5]  # (default=0.1)\n",
        "params['n_estimators'] = [50, 100, 150, 200, 250, 300]          # (default=100)\n",
        "params['reg_alpha'] = [0., 0.0001, 0.001, 0.01, 0.1, 0.3]       # (default=0.)\n",
        "params['reg_lambda'] = [0., 0.0001, 0.001, 0.01, 0.1, 0.3]      # (default=0.)\n",
        "params['random_state'] = [42]                                   # (default=None)\n",
        "params['n_jobs'] = [-1]                                         # (default=None)\n",
        "\n",
        "n_comb = np.prod([len(params[key]) for key in params.keys()])\n",
        "print(f'Possible matches: {n_comb}\\n')"
      ],
      "metadata": {
        "id": "5hiB4BoiT9re",
        "outputId": "86af0268-dee4-40ee-cf60-2af3f39d9d7d",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Possible matches: 10368\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Tuning hyperparameters Bayesian method\n",
        "best_param_b = param_opt(model=LGBMClassifier(verbose=-1), params=params,\n",
        "                       X=X_train, y=y_train, opt_method='bayes',\n",
        "                       score='roc_auc', n_iter=50, n_splits=5)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sk8eFKa19hA4",
        "outputId": "72210f10-8088-42ac-ec62-f2d058e0cab2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[LightGBM] [Info] Number of positive: 10026, number of negative: 139974\n",
            "[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.019397 seconds.\n",
            "You can set `force_col_wise=true` to remove the overhead.\n",
            "[LightGBM] [Info] Total Bins 978\n",
            "[LightGBM] [Info] Number of data points in the train set: 150000, number of used features: 10\n",
            "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.066840 -> initscore=-2.636275\n",
            "[LightGBM] [Info] Start training from score -2.636275\n",
            "Best params: {'learning_rate': 0.1, 'max_depth': 0, 'n_estimators': 50, 'n_jobs': -1, 'num_leaves': 50, 'random_state': 42, 'reg_alpha': 0.0, 'reg_lambda': 0.3}\n",
            "Best score(roc_auc): 0.86516\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Tuning hyperparameters random method\n",
        "best_param_r = param_opt(model=LGBMClassifier(verbose=-1), params=params,\n",
        "                       X=X_train, y=y_train, opt_method='random',\n",
        "                       score='roc_auc', n_iter=50, n_splits=5)"
      ],
      "metadata": {
        "id": "cvqEFGMqWs3w",
        "outputId": "c36bbfd9-0321-4e00-c453-573d961a0b4e",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[LightGBM] [Info] Number of positive: 10026, number of negative: 139974\n",
            "[LightGBM] [Warning] Auto-choosing col-wise multi-threading, the overhead of testing was 0.019938 seconds.\n",
            "You can set `force_col_wise=true` to remove the overhead.\n",
            "[LightGBM] [Info] Total Bins 978\n",
            "[LightGBM] [Info] Number of data points in the train set: 150000, number of used features: 10\n",
            "[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.066840 -> initscore=-2.636275\n",
            "[LightGBM] [Info] Start training from score -2.636275\n",
            "Best params: {'reg_lambda': 0.0001, 'reg_alpha': 0.0, 'random_state': 42, 'num_leaves': 10, 'n_jobs': -1, 'n_estimators': 100, 'max_depth': -1, 'learning_rate': 0.1}\n",
            "Best score(roc_auc): 0.86516\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "best_param = best_param_b\n",
        "best_param"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4kdfg8ZRJnnz",
        "outputId": "21309049-ff07-47a0-fa6b-fda20a459322"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'learning_rate': 0.1,\n",
              " 'max_depth': 0,\n",
              " 'n_estimators': 50,\n",
              " 'n_jobs': -1,\n",
              " 'num_leaves': 50,\n",
              " 'random_state': 42,\n",
              " 'reg_alpha': 0.0,\n",
              " 'reg_lambda': 0.3}"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We have a tinny improvement compared with vanilla model, but it's better than nothing."
      ],
      "metadata": {
        "id": "SqdZeaJWF2bC"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ivp_5DMpVGYR"
      },
      "source": [
        "## Testing"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "As we're working with a kaggle challenge dataset, the test dataset don't have  labels. To verify the performance achieved by the model, we'll have to upload the prediction directly to the platform."
      ],
      "metadata": {
        "id": "MxdAAEtxFjtD"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "g0fnpsjoHjoK"
      },
      "outputs": [],
      "source": [
        "# Filling null values with median of df_train\n",
        "X_test = df_test.drop(columns=['SeriousDlqin2yrs'])\n",
        "X_test['MonthlyIncome'].fillna(fill_MonthlyIncome, inplace=True)\n",
        "X_test['NumberOfDependents'].fillna(fill_NumberOfDependents, inplace=True)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_test.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 243
        },
        "id": "qdQDPdkRKkdF",
        "outputId": "54bb42b2-23d7-4352-a633-97bf0cd2341c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   RevolvingUtilizationOfUnsecuredLines  age  \\\n",
              "1                              0.885519   43   \n",
              "2                              0.463295   57   \n",
              "3                              0.043275   59   \n",
              "4                              0.280308   38   \n",
              "5                              1.000000   27   \n",
              "\n",
              "   NumberOfTime30-59DaysPastDueNotWorse  DebtRatio  MonthlyIncome  \\\n",
              "1                                     0   0.177513         5700.0   \n",
              "2                                     0   0.527237         9141.0   \n",
              "3                                     0   0.687648         5083.0   \n",
              "4                                     1   0.925961         3200.0   \n",
              "5                                     0   0.019917         3865.0   \n",
              "\n",
              "   NumberOfOpenCreditLinesAndLoans  NumberOfTimes90DaysLate  \\\n",
              "1                                4                        0   \n",
              "2                               15                        0   \n",
              "3                               12                        0   \n",
              "4                                7                        0   \n",
              "5                                4                        0   \n",
              "\n",
              "   NumberRealEstateLoansOrLines  NumberOfTime60-89DaysPastDueNotWorse  \\\n",
              "1                             0                                     0   \n",
              "2                             4                                     0   \n",
              "3                             1                                     0   \n",
              "4                             2                                     0   \n",
              "5                             0                                     0   \n",
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            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "CqW7-N9gbnS2"
      },
      "outputs": [],
      "source": [
        "final_model = LGBMClassifier(verbose=-1, **best_param).fit(X_train, y_train)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "predicted_values = pd.DataFrame(final_model.predict_proba(X_test)[:, 1]).reset_index()\n",
        "predicted_values.rename(columns={'index':'Id', 0:'Probability'}, inplace=True)\n",
        "predicted_values['Id'] = predicted_values['Id'] + 1\n",
        "predicted_values['Id'] = predicted_values['Id'].astype(int)\n",
        "predicted_values"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "HCdnHVR-KRxc",
        "outputId": "e4347f57-78f8-4fe2-dfba-647931248406"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            Id  Probability\n",
              "0            1     0.070235\n",
              "1            2     0.053903\n",
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              "3            4     0.069995\n",
              "4            5     0.132212\n",
              "...        ...          ...\n",
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              "\n",
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              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        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",
              "\n",
              "\n",
              "<div id=\"df-19a23e7d-b7e9-4107-9c11-df6f462c5f5a\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-19a23e7d-b7e9-4107-9c11-df6f462c5f5a')\"\n",
              "            title=\"Suggest charts.\"\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",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-19a23e7d-b7e9-4107-9c11-df6f462c5f5a button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Exporting results\n",
        "predicted_values.to_csv('predicted_values.csv', index=False, sep=',', decimal='.')"
      ],
      "metadata": {
        "id": "IcJBW-KVMTZP"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Kaggle returned scores **~0.867** for private score and **~0.861** for public score. It's a good result, considering that winer got **0.86955**.\n",
        "\n",
        "Maybe we could improve that creating a stack with the top best performers, but if we would do that we will lost the ability of explainability, seen in next topic."
      ],
      "metadata": {
        "id": "irBBe3VFCt9D"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "![image.png](data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAABLEAAADDCAYAAAB9GoONAAAgAElEQVR4nOzdf1xUVf748ddGzaghJoYrLogKagvaBroafDRxSROjZtUwW4jWH5RZUqal0ZqyFVqJtP5KQ20lKJS0SPIHpuCPwFyBWoVvypAhFCwTGOOEzhh7v3/MAAMMvxQT2/fz8fDR3Lnnnvu+955rc96ee+5vFEVREEIIIYQQQgghhBCiE7vpegcghBBCCCGEEEIIIURrJIklhBBCCCGEEEIIITo9SWIJIYQQQgghhBBCiE5PklhCCCGEEEIIIYQQotOTJJYQQgghhBBCCCGE6PQkiSWEEEIIIYQQQgghOj1JYgkhhBBCCCGEEEKITu/m6x2AEEIIIYQQQgjRXkZdIXmny7lkWe7i7IFXfyfUdtc1LHEjMurQntRSXteYejNomDtO6usalbBBklhCCCGEEEIIIW4Y+vxkol+IYlu+0cZadx5+YxWRkz1xkGSWaI0+n20rXmRpUj62WpPHQ9G89bdgvBx+8chEM+RxQiGEEEIIIYQQNwAj2sSnGBMUybZSH8Lf2M7hL09x9psCzhac4vD2aMJH69j2goaRcxLQVl/veEVnZjyTwJPjNCxOKmH449HsSM/m64ICzn5TwNfp21nxuC/lH0YS5BdO/BlbKS5xPfxGURTlaiownN7D1o2JZJdDd7cAHps3A58+rW2VT1LoFwxNmMHQq9m5NpGZTxXw2PZljO3R3o0NZEVNY12/1STM8LiaKDqebg+vr4fwpYE4NlpVuWcZCxO1liVnhvmPZnyQhqGtnvOO0o7zVmPCcBHs7VXm5au6XkIIIYQQQtwgdHt4fX4ipy2L3d3GMP6hYIK8G/+6NzNkLEOz3o0t22fgdqX7vGjApLJHdQWjj66sT/fLK/4wnPEvZEDAUnbEhDYzOsaIduvTBEVlYPSP5nBcMK6/whFZpzaFsiqjdsmZYYGBTNf8CWf7VjZsoa9ZW++pUQlMH6Yl6ZEIimZvZ1FAa5VaqSkjJ34NWw8UcQFnhoc8wWMTPLDvbNegJJmZEyNJx5+o7TGEedoeamU8k8CTU6JIr/ZnxeE4Hnb5heO0uNJ7tP56Xvm+TQYDdL2yv1s6Yv+NXd1IrPyNzFmSi8+zG9gQt4GlISqSZr7EocrWNjRS8Z3hqnYNwAANS9fNucKEiD2+T6zmtSmdLIEFUGOiotxke93FSgq8ZrJ63QZWr1vAFE8Tn0ZMY92JDjifbdKO86bbw98X7aGuOVzV9RJCCCGEEOIGUWOi4jtPwtdtYPW6Dbz2xF2Urg8lNsP2b3b7MXPY8nrwlSewgFMb/0xc7hVseMV9ul/Y+TRilmVgdJ/FFusEli6NxRoNi3frLF+o8XhsLe8/7g4ZkUTv1l+viK8tQxndA5fV9QuDuh7m+YhEimpa2a6lvqal3gojgAdB0asJ929HAotKDr00kyS7EJbGbWHDunn4fBnJnE357ajjl6Bn75tRpFe7Ex5nncDSsTdSQ1BkGnWtaXAob/9zFh5ksHjFLq5La7qae7Tuel6pMtIWRZKma73ktdl/U3bLli1bdqUbVx6J56uhEcy82xG7W1R0dbqT4SPs6XarC7d1hdK0NeTcNIr+Pc3l65fL+NfWc/R/qBdfxSdyMKcMdd876G25Pwy5yWT95MblLxLZuTef37jdiXONlv0JiRzMqcTBfRC9ugA3lfFV4mnUowbQHaAsl9SkDzn0uRaT80Bce1hGAFWdI2tHPHsP5VOqcmOwc1fzfgoOcPS8G793NpcznD1IakIqR0+2FM+XDepoQqdl/8eJHDyUT6mdM4P72retjrIv2PnuRxzV/kTfXiaOfQGj77+Dxnu5+P/2kFTtyxx/N1Rd7LnN1YvRAb3YO28H3aeMwfkWoMZAUXoyyZ8eouA7FX0HO9O1Nl3ZzLlo/vjPcSjpG9S3fMmnH3wJd3jRrbj+vJWmJVPU05mSnZtIPZRP5W2DcL9dBVW57Nyyk6P5Z6nQaTH1HkV/x0bXq6aMUx8nk/LZUYoMzri69UB1U/0+HW4/x57aczJwAN1VbW+bQgghhBBCXDeG0+zfcYmRT4zBpYsKVQ8Xhvet5G+fXGbqvQMw5SaT9ZMzlXs2kVrmzPDe5RzMqMR1aC+KUteT87M37k6WYQ81Wg6t+4LL3oPoZWegNHMPn368nyztT9zuNoDbupj7WYlpp/nm+ypKdSqGDHVGRfP9G2ut9eloqZ5m+x1N+xC9u7QtnuYU73yRxWnnCYt5l7AhVlM7Vxfy2Sf/4pL3g0wccqvly5tx9nHh/D93seNbZ4IeudPmqKMbWfmxePJun8793o6outjT6/d3oEp7g68HTucPTuc4tCa7vt+F1bLhNPsPwx9HVrF/i7mvVduOaust6hfGyL5Q8a9E/t/Nlv58S33MOl/z6WtqNLFBuN1ih12XHrh6j2JwV3tuc7bHDlroA9KwP997IK49Lf30xveLe4+W62lNUTKLIg9QGfIG8WFDrCYKr0Z74BOOG//An+8bQl1r6vtHXH/YTMrOb3EOeoQ//MKN6WruUevr2eI1tLnOwKmkd/kk6yRFlTq+/an23LdcT8G++pxHt6JkSmr330GuaiSWvVMfTu5JJue7+kyu45BRuFkuakV+Cqcq6ss3XM4l7q0D2PsHE3SXiU/nR5D6nXmN6ewR4lZu5KRTAEHDK4mbM4dn3jqC8wQNvuxhzqt7MP8bRgWnduZRAVB5kL/PTwG/YB7ROHNsYQQ7zwE1+cTPiebUAA2PBI/C9G4osUdMdfs5dtZck+nEGiKi83AObCaeJSvJcgpgamAfCpbPZN0JG9nrshReeGwl3/YJZGrwKPgwgr+nVbZeR/5GQiP2gK+GIO8L7IxN5Nv2XAjH0Yz3O0r21wAGTq2Zw4p8ZyYEaxha9T4z5qeYR0O1dC4aHH8l8TMj2F9mOcdJy1mRZMBtuAe9VA3PW0V+IusiV3NqiIapgW58uzKU2CMGUDkz2NMNe3s3hg4fSf8eja5XTRmp8xfyqekugoIDcc6PJmJNLibq9xkVX8nwYA2+Fz8mdFEKne0fg4QQQgghhGizbip6WT6a+wYxZDuOZPhAe7h4jmNHzmFChVuPC6xKOU5dbyN3DysM3XFTmTi1ZibP7zUxVDODoAF5rAjfSEENdB84ksG9odeQkQwfYk5gNf59nzSn9vd9Q6316ZrvJzTqd5hSWDg/hdIasNmHaGM8tunIycgFgrlvTKPXxTlNYEVKCm894NTofPtz33Tg5CGy27yfG5k93bvXfrbqd9la/m4PcfHnGKwJZkKfPFbMWcMpG4ME6/vvja515buEzLfVP+uF84AjfLozH0PtiDD7fgz17oMKmvQBux9fyJz1lj7gdym8MD8F010apgY6cyp6Tt3TRk3ul8b1ZEcSUVtPG+hyDpEDPHyfPw1bkxMTo1NIjXmAhq1JzbhJoUA+GblXOiTpyrX3HrV9b7WQJ2j2+qroNcQTN3t73Dwt577Feio59NI0VmndmKAZQ9/s1azNvAYnRLlKFce2KFEzNMpETYjy0sqPlZOl9etOxgYoa/9lazlHWesfonzwdf06Y/pryp9ic8x17pinRGyvrahU2TU3RNnxTW3JHGWt/2rlZOPPOauVPy05oFz42VKs9r/lHyvPh2xRzjT+3rKf53eUKopSpOyYMUPZUWQVz+HXlInRRxRjk3gU5cK+SCV4Q57N82E0Wi0cW6mMtnlM1nUYlcxXAhucJ+XfG5TguR8rFTbqr9gxr65Oa3Xntmi7MmfhbuVC/Z6UzCUaZWteS+fCxvEX5SgnS4yK+RyvVLJtnjfzfl/abRVp0XZlzoztyveKoiilHyvPNziO+utlfX6bxtB4n3nKVk2kkvGjjRMihBBCCCFEZ1P6sfK8/2tK5o8XlAs/XlAulOYpO+YFKlH7zL+MG/cNGvxu/jlHWRv4mpJdrSiKYlSyowOVVYctv5p/NirGut/Ipcqu2fOUXZZqGva9ipQdMxYqaVY/042fLW22D9N8n66FfoL1736lcayNf8+3L56mcpTXBngo/f+yXTnXeFX5PmXRgw8qiz4tb7JVefJspf8AD+W1zDbu5gZyMjZAiUgoMLevHy8o33/2mhISvMHS17PuMzdaLv1Yed5/qZJZ32FUvt0a0qB/V9uOGvQxG1zrC8r3OTnK99U2ArtQoKStXKiEBAYqIU9EKh98VlTXR2/SB/y5QjnzeYFyoXE7Vxrus/H9Yjz8mhKyzqrt/FygfBDS9v5i9nIPpf+AR5Wk4sZrypU9Lz6o3P/iPqVJayrdrswY4KH0f+X6NKaW79Hm76025QlavL6lyq659X/PtFjPNwnKjAb9/wolbWHDnFBHuLn1NFfLHEfN4OVRM8BUSdGRRFbMjGDKltWMb3WSMW8GW02rpLrDi+HJ56jEGwB1g0nDHFE18/RenTuDefmThQRP3cLwEQHcfb/GPHGiUwDhAQuJeGAPg71HMfZPwQQF9KPhk2kVlJ71Zmw/q3gGeTEsqYLahLR1PPaOfSi1+TpXAxVHUth58AjZRQYwVIL/n+rW2q6jkopSD9ysJ4hz6kP/Vg63MVNtUra8jFN5R4gITaxfV2nA90FaOBc2jr+ft9Wk+6oWJnHrg88dVuMp+3kw9OxRKgDnFuI16MoYNsTD6jr0Y/Cd5zhUDvRrvE9HHH93gQsXAZlPSwghhBBC3BCOsu6pfPPvXXs3JoRt4WW/+t/N6uZ+X9t5MyEoitQvTPiMOs6hXA1BL1h+NVfmkZayh2MZ+XxfY6Kisg/hNiupoPRsHjsjQvmg9quLlZSO0dgs3XyfroV+wokyTt052uo3v4r+QzxI0lXWLdf/nm9fPO1SU0X5yXyM+v+9t8edSl5GxB7z577eISyNC2RQWybf9vCiv9WjnG6eo8g6Wgo004kvb3yt7XH29rZd1t6D8QveZPwCMJ3LJXXTQubkv8iWed5N+4B2jgzycwTK+L7Eg0GDrHrpVv3KvjS8Xwy6MkrToplpNcqnQudIeAf0F6vK8skzVnHp6qrpcC3fo224t1rKE1S34/q2WE8lBYM8rR7ddcT5d3Dm6g69iatKYjWYpV7liFvAPF6unEPsF5WM17T2oGglFwzUN7KLBn7obhn6eiXB2PVh7NIExtaYqDx7lA9eCSV29sfMH2PPoBkb2BNmwvBdHmnrI3hGt5q3p/drVIEJUw1Qe3PUmDDatW8SJsOBlTyfdQ9vvriap+xVcGINY462ZUsTWE/AV2PCSDv2fTGXrMxRDH0CKAD8F7DlhVE2CjZ3LkClanT8bVZGaQUwoDaWnzC1MXRjo0kHTaaWkmVCCCGEEELcSAJZnDDvit7GPsg/kKydxwm3O0y2/5+ZbwegJempd2FZJC/P6IPKrozUp6JbqCWA57YuwKeV39ct9+la6SeYGvbcjDUmUDXXGWhbPLa54BUAHMinuIY2v22w4OsMwB/3Aa0WvSEND1vJG1Ou4DWSukqsnx40GS7g3FXdbHHsQGVqQy+90dvpVf28mbJ0AaWTjnJmnjd97cDYbDVN+8QtdYmHhqzkH8FX9gpN1yH+QAb53wJtfdvg2ULSgXF3/PIvhmvtHm3zvdVcniC3jde3tXpO0OTvhPZU21ZXNSdWUXwoz8Rr67+oOUdWRhlDB1oSWHYqirTnzJ+rvuBQhvXWX/BpWu2DmgZykpPpO/Yu2vPuA2uG3HeJTT4HdiocPf5EkL8jpZUGOLeH19cfxWSnwr6fN0Hj7+KMrqLR1l74TjhAUkp9PKeSkzH5ebdrAkBTVSW9hnjibLlpC44facNWfRg+xkRScv0zvEVpe8hp604r80ldvpyswBDzW/+8vJmScYCsKsv6mjL2x6whp4wWzoUXw/0bHn/Wq5Pq5stqzaGDX9TFXrpnO2n+3gwG8//oqiq5YOMNGY4jxmBKTq5/9vq7FJIyRjP8jrYeuBBCCCGEEL9SnoFM/2Y7f0/KZ0qgp+XLC1To3Bg8pI+5M6vLJceqK4adCkNV7Y9rc//mkNXbEEtTYojLbDo5VMt9uhb6CV7eTMlIrJtHGEMuO5NN3O1tqwfV9nhsc8LH3xtIYN+RNo64qs5gXxLgPZbhTq2W/tVRqQo4Y+mKGzIPcsh6ZdUe0r6wXIuaMtKSjxPk59m4inp3eBPU4FofJXZSNDkXG5WrOsCKqSvr+6KA4YujHLrTg/6Ao3ejPuDZRJ6cmUypjT5xaUoiqbX9ykYcR4zBtGdP/ZsYDbkkvZpMQRsTJk4+Y/EB4vdl0LbWZCR9dwLgjb/3L9+YWrtH23RvtZQnaPH6qlDbVWKoakM9jf9OqDrIoQMddhrqXNVIrEFhKxn/6kIC/2yPsz1UVBoYHraSxcPM64dOWUivhXMIiAd7rycI94Oiuq0DuUe1hpmPFIHJAHdG8NqEK5/m337QKJw3RqDZaU8vTJjcgnltqSOovLlbt5DgqRvo1dWEyW4Ub8Y2HhqnwmfeSs4snIMm2Z5etfHMazxaq2WOE2YwNHwOoSn2qGpU+I5q4S8CK87B0Tz20hyCp9rTCxVD5mqYfqKFDVKeZ0yK5bgdvQmaHc2WQEtGuOtonnq9gOcf+zNx9vZgMNBdE82bfYCa5s9Fk+MfFclKv7YNqRquOs4zj6zBiIGKHhpWrxxtTpo7jWHynRHMmXqEx6K3MN36dPQL5uXZy3gpdBrYq6gw2PPI6yvxae2xUSGEEEIIIX71+uE7wUBsmob5daOIvJny7PvMmRpKL3vAZRTDrborQwNnEBcxDU3WQhL/9ifL7/tQNJvs6WX5nb5yZdORKy336Wz0k2r7CXaWfsdTf2anvT0mAwybu5IpNrtQqjbH0xzXSbPRLH+K+JdjuW/vYvy6WVbY9aD3ME8u3Wo9kshIzuoVxFer0fz1Pjz+55728CZoQQovhU8izk7F8CdCGItVUsNTg9vxCGauMdX3F4e1UF3Xptfa94XVTftujoE892I+Lz0yiXWOjqguVlJqH8DLKwPNg1Wa9AGdeez1pebH2IKjCX91IcFToZeqUb+ysX7BvDx9GS9NNddT2/amt/VhJrcHCX9gBU8mRhEzyZdI3/q206OPJ17GLnSxKm48EUt0ohH1A48S2N55fzpA2+7RVu6tlvIEtHR9HfEN9iZi/p85ND2at8NaqefveTxTW499AOEhnpzq4PPxG0VRlKuuxWTAcBFU9vbtfxzMZMCAPfbte3KveRcNGOxs1GcyYKixx761JElz27dDg+F+7XHRgEl1Bdu1N46WzkU7j//UW/dyaPRnPOVtwmBStX5+G6sxYTCAfY+OagBCCCGEEEL8ijV6ZKtVbe1vtdana6GfYKoyQFv7glfR/yv+MJzxL2Rg9F/M/rWz8Ohmq5QR7danCYrKgIBo9m8IbvPjh/9zrqD/2dZrbaoy122zf9hSH7Ct/fa6/ZhQXUlfsiSZmRMjSa/2J3LvWsIH236c0ngmgSenRJGOPys+jeNht/bvqsO0do+28d5qKV/Rnnu52Xra+3dUO3VMEkv8z6pLYo243pEIIYQQQgghft2MaBOfY+qSNPTdvAlf9iKPTvLGtRtQY6Q49xPeW72CuKN6HCYt5f3oULwcrnfMorMynkng2Uei2Htejc/Mpbz06IP4uJmTWcaiXD7+IJbod7LQ95xA1HvLCfOUxtQZSBJLXBWD9gsqHEfhduVPggohhBBCCCFEm+nzk4l+IYpttt4Y382Th5ctJ3KyJw4yAku0Rp/PthUvsjQp38b8WGq8pi/l9eeD8ep5HWITNkkSSwghhBBCCCHEDceoKyTvdDmXLMtdnD3w6u+EWpJXor2MOrQntZTXNabeDPJ0x8nmI6viepIklhBCCCGEEEIIIYTo9G663gEIIYQQQgghhBBCCNEaSWIJIYQQQgghhBBCiE5PklhCCCGEEEIIIYQQotOTJJYQQgghhBBCCCGE6PQkiSWEEEIIIYQQQgghOj1JYgkhhBBCCCGEEEKITk+SWEIIIYQQQgghhBCi05MklhBCCCGEEEIIIYTo9CSJJYQQQgghhBBCCCE6PUliCSGEEEIIIYQQQohOT5JYQgghhBBCCCGEEKLTkySWEEIIIYQQQgghhOj0JIklhBBCCCGEEEIIITo9SWIJIYQQQgghhBBCiE5PklhCCCGEEEIIIYQQotOTJJYQQgghhBBCCCGE6PQkiSWEEEIIIYQQQgghOr2bCwqLrncMQgghhBBCCCGEEELYNMjdDYDfKIqiXOdYhBBCCCGEEEIIIYRokTxOKIQQQgghhBBCCCE6PUliCSGEEEIIIYQQQohOT5JYQgghhBBCCCGEEKLTkySWEEIIIYQQQgghhOj0JIklhBBCCCGEEEIIITo9SWIJIYQQQgghhBBCiE5PklhCCCGEEEIIIYQQotOTJJYQQgghhBBCCCGE6PQkiSWEEEIIIYQQQgghOj1JYgkhhBBCCCGEEEKITk+SWEIIIYQQQgghhBCi05MklhBCCCGEEEIIIYTo9CSJJYQQQgghhBBCCCE6PUliCSGEEEIIIYQQQohOT5JYQgghhBBCCCGEEKLTkySWEEIIIYQQQgghhOj0JIklhBBCCCGEEEIIITo9SWIJIYQQQgghhBBCiE5PklhCCCGEEEIIIYQQotOTJJYQQgghhBBCCCGE6PQkiXUjqTGir77eQYjGij8M546BQ3l2l65d2xlPrGD8wEGMj83FeI1iE0IIIYQQQgghfi0kidVZ6XOJj40lJjaNYgBjLjETh/KHkWFsK+pksf0iStgbG0tMbAI5+l9sp607n0bMsgyM3ot5epJTg1W6E8nELAgjSKMh6Mko4o82THKpR8zl6UmgXbOcbYW/ZNBCCCGEEEIIIcSNR5JYnVW1low161m7JgcdwM1qHNRqULvQw6E9FenYG6khSBPJ3vYNFGp7bL8IHTlr1rN2zSEKOtFotOLdm0ipBp8p/njY1X5rJCdWw5hpkaz9KIu8k/nk7UtgaZgf41dYj7pyYOJDoUAu0QlZMhpLCCGEEEIIIYRogSSxbhR2noSnnuJsdjQTe7Zv06qyfPJO6qiquTah/e/KJ2VjLuCJ5m4Xq6838+yafIzus0jMLuDsNwWc/TKecHfQvvMccfn1RdU+vmgA49Y0MiWLJYQQQgghhBBCNOuXTWIZS8k7/inxSUt4ZEMYo2PH4mb5M3rtbB7Z+hbx6Tlof/xFo2qWbnckQRoNi3frMJ5JZmmYhiBNGM++k4Wuxla5EooPrOBJyzYA1OjJ2xXLs2EagjQaQhZsJr2w6fNw+vxdRD9pLvPkmix0PzcukUucxsaIqhodmYlRPKmxbLskgcyy2sDSWKyZTcxxgCxiZmsI0mwmp27bjoqtOUZy3p5GkEbD0n0N6y3+8CmCNBpmJhbWxaI9sLkuFluP37XpfORuNm//Tm6TY6h7tC/sOeIOFKJvlNRrUEbzFEsTG17nJnSF5JQA3fwZ6m5VT7UDmnlziVo+F7/ahKODLxMDAEoazmvm4ImvL0AyOVbJreY0Po6YXfkNj6PRNW14HttxPYQQQgghhBBCiE7mF0liGUtziE+Yy+j105n0+RssKT1M5sWiBvMpFV8uILPyI5Z8OZ+Ad8cy+p1X2JZn+CXCa161jryT+RTnbObJKVHsqYTywixSVoQxZk4yxTWNyu1bxczwzew9mU95NYCOlGfGEfTMevZqzUUL9q1g5vhpRJ+oH3ZjPBrFmKDniNuXT3E1aP8Zxl+i0ihvFI7uZKMRVcZc1mrGEbIkgb0lRqjRkZ4YRci9mgajfWzr2NhsU+M1zAvtyXzidx+jPm1SSPp7aeSd1OPj7Q4YyXlzGuPDV9TFYjyewNKwcYS0kFRpcj4AaszXIq/SqtyupxgT9Bxr91kq16YRHT6RqW/WP9pnPLGCqZYyag93XKuPEb8kjDFzdjX/yGRRHukAI11wtfraYUQoC+bPJ2yE1XOf1bnsPQDgjUuDqbNccP09gJG8b1t+OLPuOD7KobwGjHm7WPuMhjEvplnOrfk8mq+pGg8Pl7rzOPMjHW2/HkIIIYQQQgghROdzbZNYl0vZ+/5s/i9pPkt0ee2aBLz4p894Ie1+RmxYT+YP1yzCNsncUoj/p6c4nprC8ePbCXcH44FIYhqNZsk8cIFHU7M5+00BWx5ywnh0PYt261FPXsWRIymkpqRwfG8047oVEvfcZvJqgJp84iIT0KNm3BvpfLU/hf3Hs4ly05LXSlzFHy4nJt+IevIqjh/fS2pqJsfjZ+FanU/0xjT0ThNYkbKJBSMBfFmwKYXUlFn4wDWPrZba90HCugG79pJZe7qKskg5CQybxURPQJ9PwU/ueE1exX5LLPs/W8VEjGRu3NfmfdlkzGLti2nouz3AW+mZpKakkHpkLysC1OZH+06ai+V9thktEB6XyY6YVby9P5PEx32Z6FZOQZntqvXlllTeQBecbBch5x3ziKiRI6cR/5M/CxLXEebWsEyXmy0fWhr1VXsceLMgJZvjqSnsP55OlD/oP1xOfD5APnvfKQRmseXIdt6KWcf+I/GEj56Ax3ktOtp4PYQQQgghhBBCiE7o2iWxSg+zJG46T/ynwGokiz0+jpN5Y+R6DoR+yplnDlE03/znTPhHHJiwhFecvPCwikp3cRuPvBfGqznXcVRWSAgP1yYeunnz9PwHAEjJaZRemT6TMM/60TfZGQkYcSJ88ljUej3683r0t/ozbTJQcoxTpUBpHlklgMtcFjxkmVfJzgG/R2bh1WJQJWTuNs/HFDn3AZwsk4o7jF7M/uxsvlo2lpbmf7+2sVmx82ZiqBrYRVaOedyT7l+HyAF8pvnjAeDgzcOvrCM15gFcMaI/r6e4sNg8SqpEf3UTnuccIr4anB4LZtzNluPUO/Cnh4KBErLySszl1GoA0j9KILNQh96oxm9xPG/9bRZ+fWxXbay+YP5wc5eWY6jRoa8Go7GYvBPalh9RbOU4CJlL+DBzrM4JmHkAACAASURBVNi5EPZ2Nl9lpxBmGUCl7gaQwSdJWWjL9BjVvkTGryJypq850daW6yGEEEIIIYQQQnRCN7de5AqUfkrE9jdI+W/tFyr8bp/NvHsn4+essrmJ2t4RD6978fC6lzBjKZl73+CFb3Iso7eKiDs0GeOlj3jFz/6ahNwSjwGuqK2WHXr3Nn/4pqTho2YNkhk6ir8x/3dt2HDWNqk1i8IygEIyATyc6G292tmFodDCKCQdhVkAvVHf2nCNuqdDg3htbXttY2vI5/65uL4Ty7ZDOUT6e5GZkQF4oxldPxm67uh6Xo7azN7aObm6qVs5hrbRfWd+HFH3dhh/eLvp+sxvdYALPk+8x4Kjs4n5MIqQD6MAcHCfQPjfl/G0r+1xVk793YEMKNWhB5tJQ5/HU0h9HKgpYduciSyODaPcMZ0dIS42Srd+HNzao+F5UTvgUPeFN0//cz6ZT8SybUkY25YAOOBx3yyi/j4XP8thtOV6CCGEEEIIIYQQnU3HJ7EqD7PEOoF1kxfzRixi3v+5tT0poXbGTxPLgW8+5dW9bxBvBDAR/8VcevfYxDwv24mwa0Vb3miy88uW/3Y3JxRsjxRS06M7gDsLtm+vGynToIQDcNoBV6DYCJesV+p1rcw7pcbBBSgxQpsnWv+lYmvE059pLrHEJB8iL+IC6bsB/2ACa0e3nVzP1LBYit2DiYqfRaCXEw4OhcQMmkZcew/tcsNFdbfuAHg8v50d05s7UMwj7HZkE64rJO/fOWRn7GJtYhoxIYWQupenbT1m181yfr4tpxyrJFa1Hr3RvL4uwWTnwsNPhLL4wGZyDmShCwm2PIKop7zUXMT19ubHztUeBz9farYMgHrEXHYcn4Xu23xO5eaQ8cl64vfFEqKF1L1z8bKj9eshhBBCCCGEEEJ0Qh37OOF/S0lJeYV4qwTW3ya8wcL2JLCsqAfezyuhsSys27iIlZ+9TnplS1tdA7v3kVn3Rjkj6bsTAPAZ6dnCI3sOeI30BgpJz9Ph0NPB8kdN8dF9ZOcVUv4z4O7NuG5AVjLpVnOYF2ekmScNb5Y7PgFqIIvtB6w2LExg6sBBDAhJbjQHmXWy61rH1oidJ+MecYHqBOKXpZACTJx8X908UrrTuRQD456YT9hod5x6OqAuySO7xUodcBkGoKXwbN307GQe2NewlJcPPoD2szzKHRzqjlVdcog9OXloK42AHu3RDLa9swutgzs+AcGEvxLP2zMBCsnJb2bCdXdPxgGczEFrlefM26rhD8OHE/RmVoMEpzbP8l7I3zlZtZtC8g4A+ON5h9VdYiwh72T9fmuPg6Q00q3b4pKhDBg4iOgs4HwhmQeSifukEAd3b8Y9NIuod9cSDlCYy6na6lq5HkIIIYQQQgghfj3O/6gn83gOBw9nXdGfzOM5nP9R3/qOfgEdOhJLf2wTr/xosiy5EfbHJYT//iof/3PwYd7kFyioHd313894/rPxfD7t7g553KxNKhN4clo5Dwe4wrcZxO8yQrcHCJ/U8uNXrpNfJPyf04iLmkZI6WJmj+6CNnkTMbvyMXov5fAOb8CXaU+5E/9mLkunhVEY6o1DZS7xx8rxALTN1q7Gb9ZSxiVHkh41jRBtKD6OenISEshBzbiH/C1vzKsddZVFdGQUJfc9SHiI9zWOrSmvCbPwejOKlF1pwAQmjq5P4zj0cUENpG+MJb7nA3hQyCfrY1t5XNGd4RPd4WQhceHjyL7vbnr/+xDlDu5ASX0xt2BeejyBqe9EMXVGCZGPjaVLYTJxq3eRV+1NVPp2fIDyA0+zeKsRj9xyng7xxKHsEGuTALzx/2Mz6R21D74PQPyuNHLOwMQRlmOd/Bzj1j1H+pYwRuY+wMOjXTHmJ7PtgA5w5+kpvvVttzDPnJTyHms195aObU+OY3EGjIvJZMtkJ6vjSODJaVWEWbdF97lM9AEoZ98zkcRXu5Oje4pHPbtTnLGeeBrX3/L1EEIIIYQQQgjx6/H/zmi5dOnKZ5u+dMnI/zujxW+kTwdGdYWUjlJzRnlnzT1Kv1XmP3/ecUq5dFX1VSjZR08pVZbFS/+KVYavqq1/lpJ0tgNibkV58myl/wAPpf/ydOXUxkeVOwd4KP0HeChDfOcrW09fslEux0YlmcqaR33M6wd4KP0HeCn3R7ynnPrJqszP5crB5cH19d//orKnOFN5bYCH0n/AciVbURRFybEsz1aSSus3vXR6u7Lofq/6+r0eVBYlaxue++J99WW8Vlnq68jY2qJY2TrFsp+IfXXX1XIUDc5vf68HlUWfbm/9+H/SKlsjfJUhtbG/uE85l7m86bX4uVz5fLVV/QM8lCH3z1e2/tvqLP2kVZL+FtywjO9s5Z2chpE2dunIMmXIAA9lyLLMhuf823Tltdm1sZn/3DllmZJ0uuFdUfDPB5X+AzyUOZ9Y76dKOfiij9J/gI/yWqZVeRvHceej65TPy63iOb1deXlKw2v6x9mblOwmh9HS9RBCCCGEEEII8Wtx4FCmcuBQ5nXbviP9RlEUpSOSYfrMVxj2xWfmhVvu5Z/BSxj32yus7L+VZH74LI98V4TT7a9w8NF7cKCSlM2Tiagdwfa71zlzjUdj6T4MZ+QLGfD4ds4u9gajHr1RjYPDFezVqEdfDWoHB9R2zZSpMaLXg0PPK6i/2hJbS9tW6zGqbez/KmOrO082zWLHN4tpU762xoheb2w5jma2M6JufRtL/agdcOh2FWUa0JESPo5nD4zlrex1aHrars/mMRlziR4/jTj1/Pr5qqxXG+temmg7Rus5txpryzUVQgghhBA3FN2JZOI/2EW6tooeHv48+EgoD49ow6QQNTpyPkog/pMMtFXgeueDTJwWjGaYjdH4+kLSdyWwfXsOxbgwfFowjz7gj0dt0aI0Yna28MxEb3/CQ7zrp88oy2XbB+/xSUYhVT3cGffgo4RN9q57u3p9jHq0GZ+wfXcame09PnF1WrvmLbBuk/T1QTMpmIcneeJgow/S5vbbajwl7I1NbuHJHSfGzQrF53w72+qv2MHDWQD86R7f67J9R+qgJJaB9Pfu568/mJd8Bq4nSeN1ZQkmqwRWLT+PTfzzgUGQ8xb/d+gjyxsBJ/PB08/id8vVR9+cJkksYZP+RAJxR5qZNwovps2fYHm08VdKl0/m6Spw9GC4p1Ob272xKJfs4kt06e+Dj8sv9nCsEEIIIYS44RjJWaFh6juFTdZ4PL6d1MXezf8Grc4lWjONuCabqvGa9x475lttW7KLJzXPsfd8o6I9J/BWyjo0LsCJFQyYtrn5UP2jOb7F/BIj44kVBE3b3HQaEvdZ7EhZjE/tPxrXlJDyjIZndzedc8fjsXh2LPX9n0g0XBclycycGGk1765FN39W7I3j4WZn0Gm+Tao95/L+9vn11xcjObHT+Mua/EYvRbPVBtsSTy7RA1t6CZg/KzLjeLik7W311+7XlMTqmDmxjKf4/IfaBTcm/b7jElg+v32FN+4fZK7vrvE8duQjVv4X4COyv34Wv2FXGbu4ag4jQlkw4npHcR05eeJ3BX/zqd288ZM3AgohhBBCiFYYM1bwl3cKATXjnn+PqOlO6JKi+MubGWjfeZRFd2by1iTbaZ6c1Y+aE1guobz9/mImOhvJS1jA1KgM8tYsZ9uU7YS5AejY9rI5gaX2DCZyvmWu2hVRbCtM49mXk/HbEoyTezCJ8WMb7eUH9r36HPFnQO3mYk44VWcQ/VdzAksdMJ/3/xaKky6BpX+NJb1wM3950Yfj/5iAA6D7KMqSwHInLG4TC3wcqMqJZWZ4Atqt4bz8x+aPT1yFmkLin7EkjNxDeWvDXPw4xto5zxFfmMHiGesZauOJEQBOxFrapAthb79H5L0uGPMTePaRKNLz1/PaR8HsCDFnwIwnYnl2TT5GHPCbv4ynvG9H969YFq3JJW/Nc8SM2UvkCHU74nFnWnw8/o1C0h2I4tmthdDNBdeegLqNbVXcUDomiVVUQGbt51vuZviVdMybSWCtnn4PrrXvULxpEMN/C5SaF7P/UwrDnK887lY4PRDDVwGAWpq2EEIIIYQQ4nrQ8XF8gnkEy6TlvPWk+fEn1ydjeD1vOM/uNpLy4T5emmRrRImO4kLL2JdJDzLRRQ2o8QqdSVhUBnHkUqID3ICTyazLAPAnalM0D/cB8MWvf2+cPjE/klWsB6ee7viNdm+4m5PrWXwGwJvIv5pfYqTbnUh8NcAEXn9jLj49Abe5vLU8jz88k4ZxVzJ7XpzAw310ZO4zT03iOn8VUQHmxIdDwFLemp9BUGxJC8cnrkpuMtG5AC4sWLUUjTvAA0StKiZdE0tx4XpScufiZWPAgq640DKq6j4095lf1KUeFsrs6VGkb4GcYh3gAujZ+8/NFAOuj29iyzzLqKvRXrg6bia9EtRVlrJtjscBj9G+eFgHVJPP2kjzqDCfF2fhpwbUbWur4sZyU+tF2uDihfrnUbu64dreltCWBBYAKlwdB9UtFV80XGHAbaR2wKFnW+dGEkIIIYQQQogOZswj3zL97LiAu61GjjjgF2AZi5KRzymbLx5zwsffMi3Kv3LQ1pg/6rMOsReg2wP4WPr42px9FAP4TsDPSY/2QAIxsbHEHKhi3CPzWTA/GB+b/7avJ2VjLMWA+rH5POwGYOTUV5agAybiZzVvrMPdExlnDpr8r81BX7LE5erUcAdqB8tyRiEFNc2cH3HFtHlZ5kRUtwcbPuE0zBdNNwAjmXlNHxcEcPIZa5n3OIfsM5Yv9VlkpAGo0dxpaVjGHHJ2mz9OHOsNJblseyeWmNjNnHJ5kPCI+TxtSVxeTTz63euIKQG6hbLgoeaegbTVVsWNpkNGYukq65NPqLo3yWYWH/8Mvde9eN1qY+M2J7DMutzSCygAQPtjKXoGyRBAIYQQQgghxK/TeZ05uQR4uDYci+Tk6g5kACWUnwf6NN3cdfo6EisjeXbjCsb/YTNe/UGbr0PtPoHIVcuZaEkw6UvzzR9+r2Ovxo/o/Pqs2NrVCTz9z+0sGGFjtMLJBGJ2Q8ORLXrKv7Osd3dpOILKyQUPIB0o/kEPODFosBoyjGRuTiBz0mL8HAB9FtsTLDFRSLHO9vGJK1d3zUe6NJrD2AWXkUAG5JU2nacMALdQNiTqWbRgPdEThxLn6Q7f5qNTuzNx8Sper338s679+uNwMpKRIcnU17iemEmrSP3HA7jaXUU8NfnEv5kGWI3CssVmWxU3mo4ZidWC4oOLmPz5K0zaup6cnxqtbEMCy3T5MpXnq/ju+/9w9tsSfuw3l/0PbmXX/e+w4a6+/HS+CtPly9f6MIQQQgghhBDixvNTCXn/zkNfDVTryMvXYQT0lTpKTpfQZADXlli2/3Eth7Oz+So9nqdHqKE6n7XPbSavyWiojhnZ4hO6lHHdgMLNhPj5EaTRMNIvjGwHeblWp1Wjp/hkLqf0RsCILj8fXTVwXkd5cR7FjSdmJ4OY1ToWpGTyVXYmqW8E4wHodz9HtI0J/dtDRmH9b+mQJJaTo1ULMF2o+4vQmLeJv351zPw2QeM2Jm99j7zafFMrCSzT5cuU/ecHSr4ro6amhttuc8DVxZlB7m4Mdu+P50AP7nH/HTfX1FDyXRll//lBkllCCCGEEEKIXxe7LnUfjYZGKSer7k8XW5NvU0L8jGlEH9BBQDSHCwo4+00BX6UuZtz5XOJf0LCoSQIhlMjF/rj2dMDBzZcFyxfjBVByjFOlDUsaT6xvdmRLXTw/VTVMlNVcsjo2y39dgnl75yrCA5xQV+vQ6tQELtvLuwt9LAXcGSSjsDqe2nLFKoxUNVhhpPaiqZsZrlScNJupKzLQ4c+KdHO7OvtlCpH+enISIwl6MY3GLcvr+cWEDXPCoacTXg8tJTLE/P3er/KuPB5jLmvbMAqrpbYqbiwdMxKra3fzX2wA+qK64a5qr9n8866764ePGjcxKe498i63nMCq0hsoOvc9KtUtDOzvitPtjtzarSs331z/N/PNN9txa7euON3uyMD+rqhUt1B07nuq9Nd4niwhhBBCCCGE+KU4ueNjGVySfrLhfEB5X+4zf3DxxsPWrOdlWWTkmj+GzQzG1dKdcvB8kPv8AYyk7DuGHnDq72vZSE0P6x7+rWp6A1CFsUE2SsfH6zc3M7LFCY/aoA/l183FBcDpXPN8XLjgM8QSdLUeo9NYnv5HJl9/U8DXmduJesid4hOW43vAp+Ek3qJDeAyZYP5wMou881Yr9PlkZZk/Thzi3mQ70JF5wNKwps+sv/YOnvx5knmeNuOuvWTqqXt8FKD3rdYTAanpUTvdkNGI8Qrj0e1aT1yro7BaaqviRtMxSSy3QfjVLRyun9gNcB33Oh81TmStndxsAqvyfBU/Vunp5+KMY88ebQ7BsWcP+rk482OVnsrzVa1vIIQQQgghhBCdnifjgs2d8+KNsWwrsXxdkkzMOvOCa7B/3aACXW4ycYlZ6GpoMIor+4xVAqxaS6HW8tlBhRpw9Z1gmah7H3uP1o+h0e5OJh2gmw8eVjkC44nNlrcZ2h7Z4jU62DyvUcl6Yj6yBF1TwrbY9eZBDy7BjBti2Ufyo/xh+HD+MDGKvWXm7/T5CcRsLAHUaO67W+ZBvgYcfMcxEYA01m7MtQx2MpKzfhUpAExgnK/5zBuLMoh/Zxday2OCdSPtcgutkpRGCr6ubVg9zKOm7Hzwf8zcOtJ376O4tqw+g5QE80efOzxQtzMe86pc4labXyDQ8iisltuquLH8RlEU5eqrMZD+3v389QfzktPA9ZzQeDUoUZy+iMlfWh4ttNJ4BNaPVXp+5/zbBqOuAIy6QvJOl1M7+LSLswde/Z1QNxo2+/PPNXxX+h9u6+FADwf7qz80IYQQQgghhLieqnOJ1kwjrhDo5oSXuxPlhZY5iLqFsuW4ZU4pfRrP3vUUKYBfdDqJ053IWaFh6juFgBqngGAedqsi86Nd5JwHunkSuT2FcE8AIzmxGqausZT1dKe3sYS8Qj2gZtwbe9lSN9JFx7aZfizOAPVj8Xy11FZiwNhw357u9K4sJK/MCKgJ25JNlL+66fE1og6IZv+G+lFkomMVfxjO+BcyMAIO7p64UnvNwWfpXnY85g7ks/YeDTEloJ4Zz1d/84UTKwiathktoO7jz8PB7lQdTSYl19xevBZvJ/VxT/NOynYx897nSK8Gerrj5aKub7/us9iRshifbu2Jx0z3YTgjX8iAbqEkZi9tJonVlrb663fwsHko25/u8W2l5LXZviN10MTu9gx3v7duSffNTtIbPQDrOu51PvrD3Q3eTNF4DqxyXQV9et/eIIGlz09mcdBQ7hg1kalhYYRY/kwd78cdgyay+MN89FbDU2++2Y4+vW+nXFchc2QJIYQQQgghbnzdvIl8P54Fox3Mk7OfNCcAHLxnsSXdksACuNUFL0814G55VE+Nz/PbSX0jFJ8+oDuQwNotu8g5r8YpYBZbPq1NYGEuG2Ep29M8UXdeoR56ehP2D+sEFhiz2jKyRY3P8++RON8XB8vE33llRujpTfiW9PoElvXxBThZ1eWAT8gqSWBdY64PrSX1jWC8uoG+0HLNuzmheWUv79cljFzwuNsBUOPnZR41pR6xmB2p0YSNcIKyDOLXbCYlV4+6jz/hcXvrE1gAfR5gy2eW63u+0NJ+1TgFLGbHjvoEVtvjAaqz2jYKq01tVdxIOmgkFmDMY03cXFZa8kZObus5McWrSbHig4uY/NUxXBu9hbDsPz+gUt1i9QihEW3ic0xdkoa+py/hL87n0QmeuDqoocZIce4nvLd6BXFH9agDlpL6j1A8rBp/5fkqTKbL9Pnt7R1yeEIIIYQQQghx3VXr0RsBOzUODja65DVGjD+rbU/IbdlW7eDQ5ImWxox6PUaa2Ud71RjR680Ph7W6b0vZtsQoOlAbrpGxGtTdmn5ft203B1ptLm29vu1pM6JVv6aRWB2XxAJ0RxYReKL2kUEVYaM+4hW/po/0aY8fQz3i7roElunyZUq+K2Ngf9e6MrXDCAlYyo6YULwcAH0u8ZszYMwswkY4AEa0W58mKCoDo380h+MaZum/+bYYl9/1QXXLLR11iEIIIYQQQgghhBA3jMOZx/n555rWC7agSxc1fiN9Wi94jXXQ44RmTv/3LEtuU1mWTMR/MZe4003LeYysT2ABGAzVdLe/tf6L82nELMvA6D6LLbUJLIBqLRlr1rNPW/usohqPx9by/uPukBFJdKPXw3a3vxWDobqjDu/X76IBU1vbdY0Jg+maRnNDMlXJSRFCCCGEEEII0XkM87yDLl2ufFRlly5qfj+4c7wj9OYOre0mZzSaJZx4bwnx/wUo4tXdYZyreJ1X/Jyb3ezixUvcdlv9WwaKd28ipVpN2Evz8Wv1NRRqfCIWE5YQTvymT9A+EFr3Cs9u3bry4496aMdbDpuoMVCQtpGtibl8jz39A2bwdNgoHH/B4YynNoVyalQC04e1UvCiAZPKHtWVxHYxl3WL8gmKDaG1N45Wpj5PyPoinO8M4bUVGpyvZr+tOfkuM78YxZbZnq2XbSvdHl5fD+FLA3G0+qzKWIZmvRtbts9o9Rw0pzRlIameK3lqhKr1wo1U7lnGwkSt1TcePBa7jLG2Xpd8gzCc3sPWjYlkl0N3twAemzcDnz7XOyqzlu4rk8EAXTugTbfn3qgxYTCpsO8KYCArahrr+q0mYUbn+J+FEEIIIYQQ4sbU8zaHTjGKqiN06EgsABzv4ZVpL6Cpq7mI+C+mM3rzNnJ+tL2JyXQZtaq2068jJyMXCOa+MW3MFHbz577pwMlDZJfVf61WqTCZrmJy95oyUudPY5XWm/CVG1i9LpIpfMiM0HcpuLqReO1jKKPC2HqxUxv/TFzuFe4i431KAwNxa7WzXUbmnlLC121nywoNzle531YZDRQY2nDw7VFjoqLc1OSz/Zg5bHk9+IoTWABuQYGUrk+h9Aq2vaDT0ndKNKvXbbD8WYiv41UEc73lb2TOklx8nt3AhrgNLA1RkTTzJQ5VXu/ALJq9r8pIWxRJWuNXqV6Bdt0buRsJ3Fhb2B7fJ1bz2hRJYAkhhBBCCCFELbtly5Yt6/Bauw8m0M2dyoJ0vrIke/TGf7Hty/dJPVnEby71xfG27vSwzM72Q8V5nG7vadn4DDufTSbHdzrPPuRFgzFUhnxSNh+gJuCvTB3acIhWl/KTxO3PYMCfIhhjmVrrpptu4oeK8/RyvO2KDsP0+Wqe+yGUrc+PpZe9ClWXHvT2nsCI8ig+qApirLs58WY4e5DUhFSOnixD3fcOelumATPkJpP1kxuXv0hk5958fuN2J841WvYnJHIwpxIH90H06gJU5bLz8GXcf84k+YO9ZGlhgJcLXS2JwPJj8RT1C2NkX6DGQFF6MsmfHqLgOxV9BzvT9SYoTVtDYtppvvm+ilKdiiFDnVG1EFtDZaS9lYbLQ8EMrj2thnPkpO4k9bOjFBmccXXrgeomA6eS3uWTLC0lF3R8+5MzfQvj27Xf0rRkirqo+Wp7Il/9xovfOzcdsWQ4vYedH+zl1Hcq+tqfI6mkHzPvtozkqynj1MfJpDSIy7KhTsv+jxM5eCifUjtnBve1OtiyL9j57kcc1f5E314mjn0Bo++/g66G0+w/bPl8oYCDGZW4DnXG1ODafUmpyo3Bzl2t6sslNelDDn2uxdR7IK49LcfRtTvVaYmUDg9ksM1z3byS9PVUDJvH6IEqVF3Mf+xusjpvDr0o+fhdUo9b2s6PtcfUsL00PJm2riN15zLng3dJPaTF5DyQm79Ioeh2L3p3sWzaprbTvMoj8Xw1NIKZdztid4uKrk53MnyEPd1udeG2rjTbls2xtbSuuTZwjkNJ3+Bw+zn21F7rgQPorqqvs2Bf/fXsVpRMSe19VX/CLG38JEWV5jY+3L1HC/E0v8/m7snK03vY88Hehuf17EHi3k/jqzPF1JRUor7Di27FBzh63o3f/yaXpKRv6evTj9oWaPgikZ3nejPU1b7le0IIIYQQQgghfkWuXVfH+R5eCU9i428HUf80lAntT5+x5PhsRm8ej1vsWNxix6LQxrnl7cy968xXn2Ntrr6VwlfvTPZBgsaPpnGaZdDcj3l5grlHbzqxhojoPJwDgwm6y8Sn8yNI/c5cznT2CHErN3LSKYCg4ZXEzZnDM28dwXmCBl/2MOfVPRgALp7jWGI0K1LAN1iDr90eFs5JpqjJaC8Dp9bMYUW+MxOCNQytep8Z81OoBLoPHMng3tBryEiGDzF3lhvGVknSnAj2lzWuE7iopUDrRf/f1S7nEjdzIftNdxEUpqH/yWXM2ZQPqOg1xBM3e3vcPEcyfKB9u/dbkZ/IilcSuDBgJIN720hgpb1E8EotboEafPt8SdxbR+pX1pSROn8hn5ruIig4kO7ZkUSsz8UEUJbCC4+t5Ns+gUwNHgUfRvD3NMuQn/yNhEbsAV8NQd4X2BmbyLe2LvjFcxw7cg5T7bVbspIspwCmBvahYPlM1p2wjN76LoUX5qdgukvD1EBHciLnEJdbOxdWH/rfoSXn3wZbe2hBGd8W9EOlTSQuJoZ1m/ZQUFW/tiI/kVUxKRhHafDvcZSXIubw9/hKhmsC6X8uhjkb820cT3PXEajJJ/6RCPbbjSZI401F0nL+nniEby+aV7fUrtvK3qkPJ/ckk/Nd/TxhjkNG4eYILbXlJusq3yWkdl2jNuCcH03EGksboIJTScuJiq9keLAG34sfE7qots5KDr00jVVaNyZoxtA3ezVrM21F3bSNtxxr8/u0dW9UpkQQslKLc2AwU/1M7IywjEzr0Y/hQ3pBb0+GD/egl8rcBo+dNUAfZ1SZW8g4V99WMuIPoHLp0/I9IYQQQgghhBC/Nsov4NL32crW955U/m/VPUo/G3++Ov21cvnyz5bS5crHsz2U/gOWKZ//bKOu9SyB3gAAIABJREFU09uVRfd6KP0HeCj3LktXyi1lPn/FQ+k/YLaSVFpf9vLln5VvzhZfcdy75j6h7CppqUSRsmPGDGVHUf03xsOvKROjjyhGRVEqdsxTIrbXBlSq7Jobouz4prZkjrLWf7VyUlEUpfRj5XnNBuVM3fEalezoQGXVYaOiKIpyMjZAWfsvRVGKtitzFu5WLtTt7YKSuUSjbM1TGpari22hklZhFdtnS5XgDXlND6P0Y+X5uR8rFdbfGY31n0u2KxF160uVXXPnKbusznN79nsyNkBZe6xpCHXbhjSs+8Luhcro2BxzPYdfU0LWWcX/c4HyQUikkvFj05CVYyst2xmVzFcC/397dx8XVZ33f/y1S80kISaGCwVigbUL1i7oZcFqYpZp2c6aYbaytJrsmnebd2W2pq4bWqm03laobSStSlqkeYMZmC64XgGXKfwqoSIwWAmMccJmlGt+f8wMDLeiolHX+/l48AjO+Z7v+ZxzvvN4OJ++389xi89ut3/0kj3GdT3u1+72e8NnZ7ef2j3HeQ1We05CjP21j9z6+3i9feyTe+ueS+WWKfZZW9wuwo21ONd+pPhUM3vK7Dnrl9u37Mi3f/VNsf3IP+fZx8Qstx9xNm1433LtK6OftWfVuA5t5vnVnbD552jNeNZ+p/O+2u12u/1svv01k+vetz6uGzhbaf/0X8fslc18Vu12u73y4Hr7grEm+1DTGPvTS962H3HdltbGcvFm+4Sxm+1fue37KjfX/lVNc3G4x5prXxm9xJ5TF0u+/TWTc3x8tsE+tsE9qrSnzxzccFzUaTTGW/3ctXJOe+PPht1uP2u1W93uVc5zbvv/e3ndWLfbG46jyi1T7BP+WVwfj/P+nOszISIiIiIi8mPSvoXdW2D0jyAudjVx1jLyD+eS89lBdn5TTMnpYkqA49+eINjWgyuu6AT4EhEdDns3sHv/bKKiG9bFMt4Uw+Jd0dyzaDyPrZ/MkyFprBlRwu6NQPhA+rgVwbbabBgMV15w3N38C8ktA65vqUUlZZ+HM7BH/RZDrzBu2ViJax6OsUGNKR8MnWhe3zC3elQGet4cwsaKKsCtCvaJco7m72dqbErdJluVhcjftBRbPlunxvJP16bTVZQNMLV0MQ1U5e8k/d0DpH9cBqctFPuNbdNxbTpvi3W3Kik73ouBbpfs5dsDnG+4tFSUU5aewDi3GTSVFT7Enwa6WKjcn8bW9/eTU2wBSxVE3wlUUVkWQlCA22l8/ejZhitxf3ZePn6UFViBKr4qtbAlIZZM1/5aC8U+Y9sw+8XG0ZSnmeUxj51P3NZohp8fEWOn4Cq15z96PvOqfss/s8bS2znrr+F960znlsaSm5aeo6WqnD493F624OGDT904b31cNyjTdXwvy57ey30bXmJ4M58Tn9vG8sxtY8FWRfH+FBaPm8oD65dzd2tjuaaco7f2pz46L/zDwx3XU1HOLTeHuN27Htx065fsOwH0ADC4FVH3wef6U5w6DVRWcaxXqFvsPvhfD5+e+xa24XPXwjmbe5/E6XJy0lLZsz+PL6qhshqG3HnuEHyih9Ft6n6KR4/BkLWTbjFL8Hfej5Y/E225OBERERERkR+Oy5LEqmP0J6zffYT1u484t81VJ6upqTnN1Z6Ob+WB947HtGgSyc8kcs+u2UR5NurHw5dBf0lj/8NFGHsGkP/CJJJrjJj+cA8hbl/0a2pO06nTVRcc7s2hoSQeKmBS34ZvxivbOouNXk8xbQiADVst9QmGWhtWj/N/Mx1lDRMEpyxV0KWZfqJnsP6J29rY6WCmvzaDiLa8Gc1mo67G9SevMnUtzJv7LKP9DI6legvbeMrzPW/TQJrcT3e9xyzh7zFNX29n2buEWdl38MJTy5nkZYAPVzDgQH2fuC/NrLVhbbJI9Hz0IH5J80kbAGutrYVEnYGIp3awt4VebdU2DG7P3MvXD4vFApxnMSqX1p6jB3xd3SjtZmv4R5vGdY8Y1mTENH897m/4M/gQNHgKz1RNIPHfVdx9PS2P5Tww2FpOCVobLbO12Qxte/tfoz5bOUVT5/W5a4mFfc/PJLf/EqYvn4GXAY6+eBf72nKozwDuDphAzucDIL0bA5fUp+Na+kyIiIiIiIj82HSI8r9eXp6csnxbv6HrEGbMj8ZYuo4xk9dRWNP8cb7BAZzYMJnfvVKEcfA8Ztzr22D/Kcu3eHk1zoC1nc+wsdydOZ/EvfWFpCyfpJC41kBEXx8gjMghe9mY5tpv4WhqKraocM77pXIFe8ly1Ryy5LF9u4Hbwxv1EhbOA5l7yXbVSqotZ8/SFeS6Tu9hwFLtmgPmiG1fZn1tprK0pSRlNVMUy68Hvb78kjJXcuCUheKgUHr5OZIWVXmHyGkt9gs9bxON7methew99Skfn74DsO3cWV8rzJLHxr+lcswGtuoqut0cir+XI+Zjh1y1tPzoM8DGxtT6OkHF6TvJbUM0zXP09256Yd0W24evkphaWNd/2eeFBF1/vkmFAjY+MpOtrrpHljy2ptq4ve9FJCdaeY4+tw3m2u0b2Oe81VV7U+rP3U7jujg5lj8n198nar8kO7Oc3jf6tD6Wfx7O8MyU+hpclgMk3ptA7mnnGEhN5ahreB1PY2Nmf/r8/BzBhIXzgHuf1e+zr6VsIgaMHlVYqt2PbeVz15oGnw0Lpyp96BXWAy8DUFtArntdLg8wVJ1sYUafF5H3hbJ1yRK232hioPNBtPaZEBERERER+bG5vDOxWmC48ko8O3Wi6mQ1Pl0da2ACH1zJdut0Rs5dzN39dhM//yl+f284gZ5ArZWSvHd4fflikg6Y8b53Hm8kxBDoNhuj6mQ1np06YbjywpcT0imc+FUzSJ47jgFLvehlsFDWpT+TEp9yfok0EDFlCZ/OnIAp1YtuNgvcOpVnp/Q4V89NhQRRtiKWcaVgs8AtE5fwQONuOvVn0nPHmPXIb0ny8gKLhc6mBF5w5jl6DxtL0tRRmLJnkvKXO52xxWJa60U3LFR2MbFkSXNJkTD6RC/i6CcQEQqEx/DMpgmYHvaiG9AzKpzerYR+4edtzEDElGc5Osl1P70Y8qcxRLpqlveI4ZnR83l65CjwMtTdp9EGYMhYesdPIDbNC0Otgcjb6mfP+cck8MjTE4gZ6UU3DNw80cToD9sQTgv8YxIY/beZmEZCt042KmtDmb5krHNuVwFHs+4kYsr59hrK6OcGsHjqb9nq5cWpKoh6vJkxcD5ae45+Jv62EFbNj+U1ixe3PDCKISHFzp3tM657xS3h7r/NZNhvvfD3gsoqC33iljD7FoDWxrJz3yTHvbBZIPKJ5UR0wjEGxs/n6VjHGKi0ePHwc0sc+1rTqT+T/prPn119eg0mfkwoR5tt7ENkTDhTp/2WfaMTWBPX+ueuNY0/G0PGhjFh0ii2ehnAcFvD5Nutw5j08lRMvz3I7NfmcEujvgx976DP3xJgdL/6eYStfSZERERERER+ZH5it9vb+GrAS8t25gzFX35FjwB/jMb6b2DmglQSnljApgJr04M8Q3lo/iLmjAjF2y2BZbXa+LK0jKAe111cEstdrQ1LrQGvlr4cnrZg8fBqeX9rnMu8Zq8y4XPags3gdc7lUQ2Wap2LzYKF1mOzfbiCPx8azJqJbksnL+aa2njelg9t/foaL71r03FtvLdtVmvDYjPg5Z5AOfIysdvDWP9U07datpXNYsNwwTe9GS09R/flgpSzPT4BFi5nuF8bjj0fNguW02Dwav7et/bMbNUWaO64WhsWC3g1t+S2NbU2LKfB6wIv6Lw+d63FcCGxn0NLnwkREREREZEfiw6TxAKoNlv4ptrM9f4/44orGn5LtFYUkf/JCb5z/n2VfwhhPX0bFU6Hs2drOV72H67p4k0X7wusJXS5uSexvrcgqtj3/EsYxs8h8vsL4geuiuxFy7GMnc/dHb1EUdVO/hqbhv+UPxEdBF9kvszKj4fx6vLvcwyKiIiIiIiItKxDJbHAsQzwlOVb/Lpf22BGVltYrTbKT3xNZ6+r65Yl/iCcLufox3BTuN9FlRsXOS/VX5K7dy85xy10CxlM9JBQfNprlpqIiIiIiIhIO+twSSxwzMg6UVFJN59r2pyMqjpZTWXVN3T37fbDmYElIiIiIiIiIiJt0iGTWOCokVVVVU3N6dN09roaT89OGA2GumWGZ8/WYrXZqKk5zSnLt3h26oSPT5f2q4ElIiIiIiIiIiIdRodNYrnYzpzBYqnh9OnvsNnOcLbW8S75Kzw8MBiupFOnq/Dy8lTySkRERERERETkR6zDJ7FERERERERERER++n0HICIiIiIiIiIici5KYomIiIiIiIiISIenJJaIiIiIiIiIiHR4SmKJiIiIiIiIiEiHpySWiIiIiIiIiIh0eEpiiYiIiIiIiIhIh6ckloiIiIiIiIiIdHhKYomIiIiIiIiISIenJJaIiIiIiIiIiHR4SmKJiIiIiIiIiEiHpySWiIiIiIiIiIh0eEpiiYiIiIiIiIhIh6ckloiIiIiIiIiIdHhKYomIiIiIiIiISIenJJaIiIiIiIiIiHR4SmKJiIiIiIiIiEiHpySWiIiIiIiIiIh0eEpiiYiIiIiIiIhIh6ckloiIiIiIiIiIdHhKYomIiIiIiIiISIenJJaIiIiIiIiIiHR4SmKJiIiIiIiIiEiHpySWiIiIiIiIiIh0eEpiiYiIiIiIiIhIh6ckloiIiIiIiIiIdHhKYomIiIiIiIiISIenJJaIiIiIiIiIiHR4SmKJiIiIiIiIiEiHpySWiIiIiIiIiIh0eEpiiYiIiIiIiIhIh6ckloiIiIiIiIiIdHhKYomIiIiIiIiISIenJJaIiIiIiIiIiHR4SmKJiIiIiIiIiEiHpySWiIiIiIiIiIh0eEpiiYiIiIiIiIhIh6ck1g9JrRVzzfcdhDRW8mY8P7+xN49vq2j7QbWlbIrvzQ2940krv3SxiYiIiIiIiPxYKInVUZnzSE5MZGliOiUA1jyWDu3NL/vFsam4g8V2WZSyKzGRpYkbyDVftpOe28l0ls7PxBo+m8n3+tZvrzWTvy2Rx+NMDDeZeGzuBjKK3AL3COChqRMJrMnkyVWZWC9/5CIiIiIiIiI/KEpidVQ1hWSuWM3KFblUAFxhxNtoBGMAXbzPp6MKds0xMdw0h13nMVHovGK7LCrIXbGalSv2cawDzUYr2bGWtBqIeCCaEA/nxtpSNk2IYvifV5N2oID8IwXsSlnAuLtHMS/TLZF1yz2MvwWsKau//8SkiIiIiIiISAenJNYPhUco8duP8nlOAkO7nt+h1eUF5B+poLr20oT2f1cBaS/nAaGYbg+o22resYjZe60YByfwwbFjfP7ZMQ6nPEoIRSQ/s4H8uucQTJ+hAUAemzOLvof4RURERERERH44Lm8Sy1pG/qF3Sd44l4dfiqN/4kCCnD/9V47n4ddeJDkjl8JvLmtULarYMYfhJhOzd1Rg/TSVeXEmhpviePyVbCpqm2tXSsnexTzmPAZosqxszIx1DZeVOZkLtpHwmHPp2YpsKs42bpFHkqmZGVW1FWSlLOAxU/2ytSxXjaWKdGabxrP0EEA2S8ebGG5aR27dse0VW0us5K4ZxXCTiXm7G/Zb8uYkhptMjEspqoulcO+6uliGP7aA5AOtzfNq4X7krXMc/0pek2tYOiPOsS9uOkl7izA3Suo1aGOaxLyUhs+5iYoicksBz2h6B9dvLiwrJeyWUGZMjCHQOTvLOzKW8ZFAaR5H3eIN+9U9AOQfKDj3rLbWnrVLeTbJcyc5ryGOxxO3ke+69aXbeNxkYnjcOrdEGmDNZqnJxHDTYjI60lJNERERERERETeXJYllLcslecNE+q8ezb3/ep65ZR+Qdbq4QT2lkjPHyKp6i7n/M43Brw6k/ysL2ZRvuRzhtaymgvwjBZTkruOxBxawswpOFGWTtjiOARNSKalt1G73MsbFr2PXkQJO1ABUkPbnQQz/82p2FTqaHtu9mHF3jyLhw/oqSNYDCxgwfDpJuwsoqYHCf8TxuwXpnGgUTsWRRjOqrHmsNA1izNwN7Cq1Qm0FGSkLGHOXiaSCc11c+8bWPCNht4RReKSA5B0Hqc+PFJHxejr5R8xEhAcDVnJfGMXd8YvrYrEe2sC8uEGMSWl5hlKT+wFQ63gW+VVu7bZNYsDw6azc7ey8MJ2E+KGMfCGvrhaV9cPFjHS2MYYEE1hzkOS5cQyYsK3l5FJxPhkA/QIIdNsc8cc0tqelER/uttFaQsFhwDOAQPeZdIHBRAFkF7ZeX6wtz7o0lXF3xTEv5SDW64IJMRaya8V0ho9MJNcKBIQTcUUB+Qc2kPGJW9fZ6aw8UkB+zwj6nNdSVREREREREZHL59Imsc6UseuN8fx64zTmVuSfVxHwkm/f44n0++j70mqyvr5kEbZJ1voiot89yqHtaRw6tJn4YLDuncPSRrOLsvae4vfbc/j8s2Osf9AX64HVPLnDjHHEMvbvdyQ2Du1KYJBnEUnTnbNhagtImrMBM0YGPZ/B4T1p7DmUw4KgQvLPEVfJm4tYWmDFOGIZhw7tYvv2LA4lP0pgTQEJL6dj9h3C4rS1zOgHEMmMtWlsT3uUCLjksbkYI39DnCewbRdZrttVnE3aEeCWRxkaCpgLOPZtMGEjlrHHGcue95YxFCtZL+9u87maZc1m5VPpmD3v58WMLLanpbF9/y4WDzZS+Mp0ko44muW/t45CID4piy1Ll7FmTxYpf4xkaNAJjrXw9kDzCWcq78YAfJtv4gqC3MQFJNdAyKQYooxuu64AI0BN66Xdz/msgYqD6WTUQFRCGnvWLOPFzVlsTxjC0NuhpMgKBDDs4WiglM37XJkvKzn7UgGIGzEQ5bBERERERESko7p0SayyD5ibNJo//eeY20wWLyJ8RvB8v9XsjX2XT/+8j+Jpjp9P499i75C5LPQNI8QtqorTm3j49Tj+lvs9zsoaM4aHgpy/e4Yzedr9AKTlNkqvjB5HXGh9GiAncwNWfIkfMRCj2Yz5pBnz1dGMGgGUHuRoGVCWT3YpEDCRGQ866yp5eBP18KOEtRpUKVk7HPWY5ky8H1/XsrX+s9mTk8Ph+a0nJC5tbG48whkaawS2kZ3rSNRU/Pc+coGIUdGEAHiH89DCVWxfej+BWDGfNFNSVOKYJVVqvrg39+XuI7kGfB+JYdAVzus0e3PngzFAKdn5pY52RkdmKeOtDWQVVWC2GomancyLf3mUKL/mu7bWnHL8csVVrQRgpfC1yfzulSKMgxNY/8fQC7iINj5r5/acramkHSnFXAMho1exZuE0TKGO6/PtP4RBQMmuXAoBagvITLWCZyz3RBqbnFlERERERESko7jikvRa9i5TNz9P2v+6NhiIunY8U+4aQZS/odlDjF4+hITdRUjYXcRZy8ja9TxPfJbrnL1VTNK+EVi/e4uFUV6XJOTWhNwQiPvXe+/u3R2/fFbacKlZg2RGBSWfOf67Mq4PK5v0mk1ROUARWQAhvnR33+0fQG9oZRZSBUXZAN0xXt1wj7GrN62nIy51bA1F3DeRwFcS2bQvlznRYWRlZgLhmPrXF0OvOLCaZxasY5erJpen8RzX0DYVxx3LESvWxPHLNU33Z31RAQQQ8afXmXFgPEvfXMCYNxcA4B08hPi/zmdyZPPzrHx7BgOZUFaBGZpJGlrJX/N7Rr6QB4Pnsf3v9TWyzvMq2vSsfX8zjxcz83ly22oeN6127PcL56Hpi5jzYLCjnd89mO6dQ8aOd8gqjiXEnM2uGjA+MoQ+ymGJiIiIiIhIB9b+SayqD5jrnsD6aRhT+j7JlF8HtT0pYfQnypTI3s/e5W+7nifZCmAj+d8T6d5lLVPCmk+EXSqFJxpVuz7j/G/nLhihhZlCRrp0BghmxubNxAU308Ib+MSbQKDECt+57zRXnKPulBHvAKDUCm0utH65YmskNJpRAYksTd1H/tRTZOwAomMY5prddmQ1I+MSKQmOYUHyowwL88Xbu4ilvUaRdL6Xdqbhn0bPzgCEzNrMltEtXSiOGXZbcoivKCL/o1xyMrexMiWdpWOKYPsuJjc3gcrTeX++OMEJGiexGiewYgnxbKaPqgrHjKhbAlqZOdfGZ+0RgOnvWZgWlZKfm0dOZgabN24j+QkT1Z5ZvHivN+DNoHuGwI50Mv+7gj4VqZRgJP6+yHZJGoqIiIiIiIhcKu27nPB/y0hLW0iyWwLrL0OeZ+b5JLDcGG+8j4WxicysO7iYJe89R0ZVa0ddAjt2k1Xj+sNKxo4NAET0C20l8eBNWL9woIiM/Aq8u3o7f4yUHNhNTn4RJ84CweEM8gSyU8lwq2FekpnuKBreomAiBhuBbDbvdTuwaAMjb+zFDWNSG9Ugc0+AXOrYGvEIZdDDAVCzgeT5aaQBQ0fcU1dHquKTPEqAQX+aRlz/YHy7emMszSen1U69CbgFoJCiz+vKs5O1d3fDVmERRACF7+Vzwtu77lqNpfvYmZtPYZUVMFN4IJNNr2yj0DuYiMExxC9MZs04gCJyC1oo7R4cyiCAI7kUNshzWslNHOVIYPWdxpaWElhu126MDHMsrXQyFxW49dmWZ22lJC+btJR1ZJgDCOt/P3F/Wcaav0QCVtI+qj/Oe6AJE5CxYxFJ/yyFgImY3IvQi4iIiIiIyI/GyW/MZB3K5f0Psi/oJ+tQLie/6Rivsm/XmVjmg2tZ+I3N+VcQcf81l/hfXOTyP+8Ipox4gmOu2V3/+x6z3rubf426/fLNHKnawGOjTvDQ4ED4IpPkbVbwvJ/4ewNaPSxwxFPE/2MUSQtGMaZsNuP7X0Vh6lqWbivAGj6PD7aEA5GMmhRM8gt5zBsVR1FsON5VeSQfPEEIUNhi70aiHp3HoNQ5ZCwYxZjCWCJ8zORu2EAuRgY9GO18Y55r1lU2CXMWUHrPb4gfE36JY2sqbMijhL2wgLRt6cAQhvavT/95+wVgBDJeTiS56/2EUMQ7qxPPsVwxmD5Dg+FIEUnxg8i553a6f7SPE97BQGl9s6AYnv7jBka+soCRY0uZ88hAripKJWn5NvJrwlmQsZkI4MTeycx+zUpI3gkmjwnFu3wfKzcChBP9Xy2UbTdGEHk/JG9LJ/dTGNrXsblw/Sh+t6IAK+B9cjdPPtwwsWaa73pzoZWjuZmAkYf6u031+nAx/Uatw+o5kS1HpxHRpmdtpKQgkcfn5mFMq2DBnwYSWFPA6+uyASNx7v17D8Q0BtJStpEGBE6LJuyCljmKiIiIiIhIR/f/Pi3ku+8uvNr0d99Z+X+fFhLVL6Ido7ow7ZfE+t9jbMp9r65GVETQk/wlyv8i+qsiN7uMkF+H4e1/Hy/8+hhZ+9+iAqg4vpa3v7idh3q2R+BtELuSN3zW87vF2zADRr/7WfCPRQzteo7jPMOZ80Yy3jMms/SVOWS9AmAk7P55PLco1plkgrA/vs766kk8/ko2ySuyMYbG8OKrj5F7R1zriaKAGNZshXnTF7ApZbWjfpVnKA89v4wFI1yJF2+GzlrFQ0XT2XRgAytzvRk0JpyISx1bY8HRjApfwLw84H4Tg9zunbH/bLbMLuJ3i1OZF5/qvIbZxE2e0+pywrBHVrHg49+TsK2C3LfSCRu9jDX353LHmDy3VkYiZr1OytUzeCxxHbMPrHNsDb2fBYsWERfkaBM1K43FZ58iIWUxjztzTka/aOa8vtTZpjneDIqJxbhtA8nvZjOjr2NJnvlEQd0SU3NRQZNkXFSt8xdrNpkpQMBERrkXVff2JcQTCn/p9tbDNjzrwNGrSKmaw+Mvr2N2vOM66RpO3N+XsaC/e8rXSNQ9sRhTNmAlgFGDL6TYvIiIiIiIiPwQuBJYd94ReUHHv/9B9kUlwdrTT+x2u709OjJnLeSWf7/n+OPKu/hHzFwG/ewCO/vfKrLefJyHjxfje+1C3v/9HXhTRdq6EUx1zWC7/jk+vcSzsSrejKffE5nwx818PjscrGbMViPe3hdwVqsZcw0Yvb0xtjTrpdaK2QzeXS+g/xpnbK0dW2PGamzm/BcZW919atajbPlsNm3K19ZaMZutrcfRwnFWjOc+xtk/Rm+8W1je16Y2DVSQFj+Ix/cO5MWcVZjOldh0P/LNePo9kY1ppatelRurte6NiU204VlbT5qxelzgWBURERGRjsdcRMa2DWzenEsJAfQZFcPv748mpLVXkjtVfJhK8j+3kVFYDddFYLo3hofuDcW7mX8/u7ftEhLNbx6O5aG+zaxMaGs85iIytqWStjubwuouhETfT9zDMUS08AZwAKwFpK3e7fyf5r4MejSWiDZcp1yAyzGuas0UZr7D62+mkvMVBN4aw6g//IZBwc2c5DziqfgwlbfTMkn7qBSui2DUg7H8Njq4mfNXkPvWBpLfyaSw+hzn/5F6/4Ns4OKSWBdzfHtqpySWhYzX7+MPXzv+irhxNRtNYReWYHJLYLlEhazlH/f3gtwX+fW+t5yzvUbwz8mPE3XlxUffkiZJLGmW+cMNJO1voW4UYYyaNqRuZtePUkUBWZ9Ug08IfUJ92zburRXk5xZSXdud3v2DW6mtJiIiIiL/55WmMm7oHDJqGm33jGbxriQearHKiZXcxSZGvlLUZI8xdCJvbJ5GhKdb28T6shhuLQmb8jpbpoXX/zu3dBuPmaaz62SjTrsO4cW0VZgCztGOYOKSN7Ogf/P/Cs5fM5ThL7hijmZxVhIPtZb0kgtzOcZVbSlpfzbx+I7G9ZS8Gbo0jTUj3E7S5nhaGqtgHDyP7StjCXEN1pPZzBsVR3KTUI0Men4X6x9svUTQj8WPKYnVPoXdrUf519euP4K49xftl8CK+NlCnr+vF0bA+Ku7eaQu4rfI+fhigpb24t03lhnTprXw8yNPYAH4hhLVP5KotiawAIy+hEVGEqUEloiIiIi0praI5D+P6c9RAAAgAElEQVQ7v9gHx/LiniwO7VnmeMN4TSazx64mv7aFYz9M5HevFAEBxK3J4ONjxzicNo9BnmAtWM2zb9XXkrV+mMjjKwqw4k3UtGWkJCfz4pRwjFjJXzGdpR+60gUVbHrGkZgyhsawICmZlKR5PBQMnEzn8WdSnZMO6tsRHMv6jBwO52Sw/pFgoIjkCfObSW4BRRt45oWmyRFpZ5dpXFW8tcCRwPIM5aGFSaQkJ7HgwWDAzK4ZC9hUfv7x1I9VI4PmpXEoJ4dDrvPvXcBj6wucnVrJWh7vSGA5+zyck0HKH8MBKxlPLGNXx6hVLuehfZJYxcccNXoArrydPi3WEGpFCwms5aPvINAV5U970cdtiWLOf8ouMOC28b1/KYdzcjg8VbOwRERERETke5CXSkIeQAAzls3DFOyLb/D9LFg2zfE/i4tWk5bX/KEVJUXOmSr3YLonAKMHeN8Sy/jRjv25Ja7VFGZ2/WMdJUDgH9eyfsr9RPWPxDRtLW/Mm8jkKb/Bu9rZ9kgqqzIBolmwNoG4wZFEDY5l8UurmDxlIpNvgRIzUHGQ3Zn1cQ8K8sa7awCD/rKMGQFAzTY27220mqO2lE3PLiAXGDTuUaLa5w5Kcy7HuKotYNNyR9mZQfPXsnhMNFH9o4lbtIo1UyYyeUoolJnPO578/RsoAbh3ES8+EopvV298b4nlxUVDAChc9Q65tQAVWH0eZfKUiSxYNA1TsC/eXQOImjqRhwDYRu6n7XM75fJpn8Lup0/VF6/uFETg+U7DaksCCwADgT69oOwYACWnLRcRdBsYvVFJIRERERER+b4U5mc7EgaevyHqFrcdt0Ri8kxkZY2VrPwi6Bvc5FjfiIFEkEkuueR8ChE3AeZsMtMBjJhudR5jzSV3h+PXoQPDoTSPTTsyKf0WfG/9DfGx9XWGCnN3OxIIkUOI8jVTuPcd0j6qgKsDGPTwtPpaV+XfOX8JxNe9bqyHEe+uQClkfFwI9a8youStBczLBMLnMecPRhLWX/h9k9ZdlnH1RS67SgEiued2X8xFmbz9Th4VeBMwIJYZbrXWziseq3NWoN+1DVa1GDt3d/xSU8CxMogICGDQlGkMahS/Ofeg80VZMY7Y5QelXZJYFVX1yScMnZssqSo59B7msLsIu7qZg9ucwHK46spugCOJVfhNGWZ6aTmWiIiIiIj8KJnLnEuj+gU0KtMRQEA/IBPyy1pYExUUy0spZp6csZqEob1JCg2GLwqoMAYzdPYynnO9XOhkhSMxRTTeR+bQb0wq9T2uZum9y9j+9/sJ9HCL5xcV7DJFkVBQX5Vo5fINTP7HZmb0NYJvCCGekFGTzdoN2QybFYm3B5izU3n9iPOAz0qpwJnGKt/GvPmZWAlmxsJYQq5Ivaj7Jq27POOq1DnZJZSKHSb6LXarYbUikdfdaq2dTzyBIdGODRvXs+n3kTwUBNSW8naKa8xkU1QONCh3lUeSaT5pNaXkF5nxDo9l8aLZDFUy4QenfZYTtqLk/ScZ8a+F3PvaanK/bbSzDQks25kzVJ2s5vhX/+HzL0r5psdE9vzmNbbd9wov/eo6vj1Zje3MmUt9GSIiIiIiIj8stWZKjuRx1GwFrFQUFFBRA5ys4ERJPiWNC2iTydLlFcxIy+JwThbbn48hBDDvmE5C48Lc6xPZ/F8r+SAnh8MZyUzua4SaAlZOX+eoXeQRzu/nR2MECl+Jo98AE8OHR9FvTC7eTaq1mNm16CkyaiBkyiLiQy/J3ZD2ct7jah1LUyNY46yLluJWay3pSNPuz8X33onEu2plDerD3SYTd/cbxLyqUMJaOa7iSAH5RY5xbC3Lp+Cj0iaF4aXja5cklq+PWxEs26m6gWDNX8sfDh90FPazbmLEa6+T78o3nSOBZTtzhvL/fE3p8XJqa2u55hpvAgP86RUcxE3BPQm9MYQ7gq/nitpaSo+XU/6fr5XMEhERERGRHxejc51LpZXqBjusuL54GVsogVKycTwjF2dSQTSLM47x+WfH+Px/0pgTbSY3ZQ7Dn0qn8VybsFmzibvFF++uvoQ9OI85Yxzbdx3Ob9Qyljmzowns6o13UCQzFs12JBBKD3LUWbo48MGVbP/7owzyM2ItL+KE5z0s2LWWGa4k1k0h+ALWzEQe32aFgEd5bmL4hb0kTM7PZR5XcU/PrquLFjVtEXNuASglO7/0/OPxDGfOG8nMGRGON2YKS62E/CGZ/cti6AJANMFNXjoYzpzPHLEeTn6UwPI8kp8w8WSTtyZKR9c+M7E6da7PeJqLnVNRwRg2nn/86vb6Vc7Wtdyb9Dr5Z1pPYFWbLRR/+RUGw5Xc2DMQ32t9uNqzE1dc4VHX/oorPLjasxO+1/pwY89ADIYrKf7yK6rNl7hOloiIiIiIyGUScrOjWDVHssl3f5ufuYBsx1vvGXpz07pFUEHWXmcl7NHjHEuuALxD+e290QBYt+0iywz4BhDi3N396gZVhujiKgljtWIFfHtG1u9zT3JcbcRRkai6rmSR9aSV7v0nsmb/UT7/7CiHNs/joeBSctMd+121k/IPbnDkKUrXMfIXvbjhxl7cEDWHDAAymR3VixsWt1BlXC7IZRlXfsF1xfmNXu6DxRtjN8dv1c7Bcl7xWM2YrwjjoXmvc/izY3yes4s1UyIxfpTpqHUVEE5vP6DWivmkGfNJM1a3Ny1693+U8dEAVtI+0pswf2jaJ4kV1MvtzREfkONW4T9w0HO81TiRtXJEiwmsqpPVfFNtpkeAPz5du7Q5BJ+uXegR4M831WaqTlaf+wAREREREZEOzjtyEEMBSGfly3nOSSlWclcvIw2AIQyKdCSerMWZJL+yjULncq6rXHMA8ooorPsSb+XYx4XO37s4Zrd4RBD9iCPJkLFjNyWutuZM0jY4fo34eQhGIDByCBEA7GbXgfpZLIU7Uh1JJ88IQgIAitj0hz78sk8f7p6fTkUtUGsmf0MiK0sBz/sZGqmCRN+XyzKuAiK5xznrbteubMyutp++w+ZMACN9nFOmziue7ER+2acPv/zVZJIKnGOwPJOlyx3Z0ZCHox2TbM5ms7SPYwwOf8Ht/MW7nW/OhMAumvf3Q/MTu91uv/huLGS8fh9/+Nrxl++Nq/nQ1HA1aknGk4z4H+fSQjeNZ2B9U23mev+fNZh1BWCtKCL/kxO43nFxlX8IYT19MTZsxtmztRwv+w/XdPGmi7fXxV+aiIiIiIjI96jkzXjufiITK+AdHEogpXW1fSLm7WLLI8FAASvvMLG0FIzjkjn8l0j4cDHDR62jEDD6RfNQTDDVB1JJyzMDRsJmb2b7H50FqMq3Me6u6WTUAF2DCQswcqLIWeso+FG2pM0mwhPASm6iiZErigAjvqHBdLe64jEy6PldrH/QkZiwup2/oYbtmlWeyrioOWQQzeKsJB7ya7mpXJjLMa6seYkMH7na2TaUEF8rJUeKMAPGwQnseSmGQI/ziQeoLWXThKHM3ttMRasGYxXyXzEx0lVQ3tOXMH9rXZ94RvPie0mY/g+Mrfc/cExnu/OOyHO0vDTHt6d2KuzuRZ/gu+r+qvhsKxmNlpYGDnqOt37pNiOLpjWwTlRU4tf92gYJLHNBKrOH9+bntw1lZFwcY5w/I++O4ue9hjL7zYL6jCqOZYZ+3a/lREWlamSJiIiIiMgPXuCDK9n+fAxhnmAuchan9vTFtHAXb7i+2BNAyO3egJGoMMesKWPf2WzZnkBcX18ozyR5xTrS8swY/aKJT9pVn8AC8Luf9e8lM2OwL8aTReQfKaCixojv4Nls2VKfFAAjEVM3s/35WCK6Oop65xeZoWs4cX9vmJgy9p3NGynTGOTnNtulmXby/bgc48oYPs3RNtwba3kB+UeKMONNxJhlDRJYbY8H8Ajgob+nsXhMOPVz+ZobqxD2x811ddmoqXAmsLwJuWcaKRn/NxJYPzbtNBMLsOazImkiS5x5I9+g1Xz4QNN3A5S8/yQjDh8ksNFbCMv/8zUGw5VuSwitFKZMZ+TcdMxdI4l/ahq/HxJKoLcRaq2U5L3D68sXk3TAjHHwPLb/PZYQt8FadbIam+0Mfj+7tl0uT0RERERE5HtVa8Vsdsw+MXp7N1mVAmCtAaNn0+11x3p6432uFVTOti2do8H5zGasGPE+V6c1Zsxn29BOLr/LNa6sZsw1LZ/jfOJp3LYtY5UaM2ZrG/r8EfoxzcRqvyQWULH/SYZ96FoyaCDutrdYGNV0SV/hoYMY+95el8CynTlD6fFybuwZWNfGNZWQwfPYsjSWMG/AnEfyukwY8Chxfb0BK4WvTWb4gkys0Ql8kNQwk/vZFyUEXO+H4cor2+sSRURERERERER+MD7IOsTZs7XnbtiKq64yEtUvop0iunDttJzQwffXjzP3GoPzLxvJ/55I0idN24X0q09gAVgsNXT2urp+w8l0ls7PxBr8KOtdCSyAmkIyV6xmd6FrraKRkEdW8sYfgyFzDgmNXo/Z2etqLJaa9rq8H7/TFmxtHde1Niy2SxrND5KtWjcFOL+xJCIiIiIiIpfMLaE/56qrLnwW5FVXGfnFTSHnbngZtGsSi5/6YzLNJa6u12L+tiOOuVllrR52+vR3eHp2qvu7ZMda0mqMxD09jahzvrDCSMTU2cR5wq617zQoGujp2YnTp79r8cg2qbVwbOdS/hIby7jYCfz11X9TdZm/nB9dG8vGI21oeDGJg9N5rHoyjdaflEPV9lkMuz+Wqc8421/KhMWRVxm3tqB9+6zYyXMLdlLV6HdL5nwGj3qV4nMc3pqytJms+vDiE1nHkicwzhVjozg7tnL2zLqXYbETWLy9/MLvaa0Ny+lW9rfXmKu1YWlrNrZx28IUxt0zn316GaqIiIiIiHRgXa/xJqpfBHfeEXlBP1H9Iuh6Tcd4m2j7JrEAfO5g4agnMLklspL/PZr+6zaR+03zh9hsZzAaXDO4KsjNzANiuGdAGzOFntHcMxo4so+c8vrNRoMBm+0iirvXlrN92iiWFYYTv+Qllq+awwO8ydjYVzl2ORNZlnIqm3nxQmNHX/4tSXkXeIrMNygbNoygc64NLidrZxnxqzazfrEJ/4s87zlZLRyztOHiz0etjcoTtia/ew2YwPrnYgi6iK6Dhg+jbHXbkoEt+jyFldursJxwS5i4x9yRlf+bPWVjeGnLBp4x+V34Pc17mWEvtzyo2m3MVezkr0+2MTnYuO0NJuatmsDALq0dJCIiIiIiIu3FY/78+fPbvdfONzEsKJiqYxkcdiZ7zNb/ZtP/vMH2I8X85Lvr8LmmM12c1dS+rjyJ77VdnQd/ytbHU8mNHM3jD4bR4PuhpYC0dXupHfwHRvZumAW86sQRkvZkcsOdUxngLK3105/+lK8rT9LN55oLugzbv5Yz/etYXps1kG5eBgxXdaF7+BD6nljAP6uHMzDYkXizfP4+2zds58CRcozX/ZzuzjJglrxUsr8N4sy/U9i6q4CfBN2Kf20hezak8H5uFd7Bveh2FVCdx9YPzhB8NovUf+4iuxBuCAugkzMReOJgMsU94uh3HVBroTgjldR393HsuIHrbvKn00+hLH0FKemf8NlX1ZRVGLi5tz+GVmJrqJz0F9MJeDCGm1y31fIludu3sv29AxRb/AkM6oLhpxaObnyVd7ILKT1VwRff+nNdUfJ5nbcsPZXiq4wc3pzC4Z+E8Qt/Q5NoLJ/sZOs/d3H0uIHrvL5kY2kPxt3u79hZW87Rt1NJaxCX88CKQva8ncL7+woo8/DnpuvcLrb832x99S0OFH7Ldd1sHPw39L/v53SyfMKeD5y/nzrG+5lVBPb2x9bg2f0PZYYgbvLv5NZfHts3vsm+fxVi634jgV2d19GpMzXpKZT1GcZNzd7rc6j9kq0zU7lpyjCqD3s44nLcFPZ8YOMXIV+yp5kx0ugOUpa1k3ff3kN24bdcG3QD11zl6t/Csd3113Rd+Xayvwujp/Pj17bx0oLqPLau38qBouNYKgqxdb+Nnh7199RQncfWD77lZ1/vZMNbX3PdbTfQufpLsrcks2tfQf09/vx9kt5I5/CnJdSWVmH8eRjdr6o/TUtjvaVnYmnpWbriLficSle8XWl+HDXX1qecwymfYLztesq3ryb3bDjBvs4scG0h+1b9mzPhvejmcZH3VURERERERIBLMRPLxf8OFsZv5OWf9cK3bqONwm/fY+6h8fRfdzdBiQMJShyInTbWlvdwfJPN+tt0VuaZz9H44n2a8z7D7+5P4zRLr4lv88wQx7dQ24crmJqQj/+wGIb/ysa706ay/bijne3z/SQteZkjvoMZ3qeKpAkT+POL+/EfYiKSnUz4204sAKe/5GBKAovTIDLGRKTHTmZOSKW4yWwvC0dXTGBxgT9DYkz0rn6DsdPSqAI639iPm7pDt5v70edmx5f6hrFVsXHCVPaUN+4TOF3IscIwel7v+juPpHEz2WP7FcPjTPQ8Mp8JawsAA91uDiXIy4ug0H70udHrvM9bWZDC4oUbOHVDP27q3kwCK/1pYpYUEjTMRKTf/5D04v76nbXlbJ82k3dtv2J4zDA658xh6uo8bADlaTzxyBK+8BvGyJjb4M2p/DXdOWem4GVip+6ESBPDw0+xNTGFL5p74Ke/5OD+L7G5nt3cJWT7DmbkMD+OLRpXv0zweBpPTEvD9isTI4f5kDtnAkl5rllSfvT8eSG5H1maO8M5lW1dwp7omQwPanpvOJ7K1v0+RJqG0bvqVcY8f4Cmc7NsHF0xjlm7bPQ2jWX4Dfksjn/ZOXOwin1Pj2JZYRBDTAO4Lmc5T69O4Wil88hWxnKbGPy5KTQIL68gevfpR88uNLinnP6Sg2vns/KQD3369KBzbQHJExI4eoOJh2Nuw/ZqLIn7bdClB31u7gbdQ+nTJ4RujW5Fc2OutWfS4rNsLt6WxlFzbank6NZ8KjEQ1OUUy9IO1T+PvJ0stnQmyHAen0MRERERERFpnf0y+O6rHPtrrz9m//WyO+w9mvk5/MnH9jNnzjpbn7C/PT7E3vOG+fZ/nW2mr08225+8K8Te84YQ+13zM+wnnG3+tTDE3vOG8faNZfVtz5w5a//s85ILjnvbxD/Zt5W21qLYvmXsWPuW4vot1g+etQ9N2G+32u32yi1T7FM3uwIqs2+bOMa+5TNXy1z7yujl9iN2u91e9rZ9lukl+6d112u15yQMsy/7wGq32+32I4mD7Sv/2263F2+2T5i5w36q7myn7FlzTfbX8u0N29XFNtOeXukW23vz7DEv5Te9jLK37bMmvm2vdN9mtdb/XrrZPrVuf5l928Qp9m1u9/l8znskcbB95cGmIdQdO6Zh36d2zLT3T8x19PPBs/Yxq9ziP3vM/s8xc+yZ3zQN2X5wifM4qz1r4TC3+Ox2+0cv2WNc1+N+7W6/N3x2dvup3XOc12C15yTE2F/7yK2/j9fbxz65t+65VG6ZYp+1xe0i3FiLc+1Hik81u89e9rZ91mjnOGj8TMrets8av9n+Vd21H6wfP42dtdqtdWOpzL5tvPOefrbBPrbBc660p890PbvWx3LD/ivtn/7rmL2ymc9ns3G731/3azjxtn3WmPX14969v/9eXvfcm9NwzLX+TFp+ls3Ea29pHDXX1u0zfDbXvnLYs/acGlc8rs/veXwORUREREREpFVXXI5EmdE/grjY1cRZy8g/nEvOZwfZ+U0xJaeLKQGOf3uCYFsPrriiE+BLRHQ47N3A7v2ziYpuWBfLeFMMi3dFc8+i8Ty2fjJPhqSxZkQJuzcC4QPpUz/tC6vNhsFw5QXH3c2/kNwy4PqWWlRS9nk4A3vUbzH0CuOWjZW45uEYG9SY8sHQieb1DXOrR2Wg580hbKyoAvzq25wo52j+fqbGptRtslVZiPxNS7Hls3VqLP90bTpdRdkAU0sX00BV/k7S3z1A+sdlcNpCsd/YNh3XpvO2WHerkrLjvRjodslevj3A+YZLS0U5ZekJjMtyO6LCh/jTQBcLlfvT2Pr+fnKKLWCpgug7gSoqy0IICnA7ja8fPdtwJe7PzsvHj7ICK1DFV6UWtiTEkunaX2uh2GdsM7OiGrNxNOVpZnnMY+cTtzWa4VfFvhdT6T13A71auj8GA3WfBo9mZmrVdZVPetpODmYW8FWtjcoqP+IBKqs41isUn7qGPvhfD58C5xrLPu79H9/Lsqf3ct+Glxje4mejDdfgO5j4wTOZev9Obgq/jYF3xjB8cI8mMx/P7dzPpPln2ZyWxtE5eIQzZPgCtv/bRsRth9iXZ2L4EwYu9nMoIiIiIiIi9S5LEquO0Z+wfvcR1u8+4tw2V52spqbmNFc731AYeO94TIsmkfxMIvfsmk2UZ6N+PHwZ9Jc09j9chLFnAPkvTCK5xojpD/cQ4vZltabmNJ06XcWFujk0lMRDBUzqG9pge9nWWWz0eoppQwBsjrek1X15tmFtLcHQkrKGyYJTliro0kw/0TNY/8Rtbex0MNNfm0HEOYu1AzYbdV/rP3mVqWth3txnGe1ncCyxWtjGU57veZsG0uR+uus9Zgl/j/FrcpRl7xJmZd/BC08tZ5KXAT5cwYAD9X3ivjSz1ob1AlIl9XoQv6TlBI611tZCos5AxFM72NvcrrwU/ppnIag0lkxwJGGqYGZsFdM3jKV3m2MrZOOkV2H+HJ4Z64fBo5ztkxIcuzzAYPkWG9Rdva3B7W3jWO4Rw5qMmDZH1DIveo19iZ1xNizH80lfPZU/Vyxnzege5z60aVAtPpPzeaNj6+Oodb2ih5G99RDxHh+QE/1bptWNgYv5PIiIiIiIiIjLpauJdR68vDw5Zfm2fkPXIcyYH42xdB1jJq+jsKb543yDAzixYTK/e6UI4+B5zLjXt8H+U5Zv8fJqnAFrO59hY7k7cz6Je+sL2Fg+SSFxrYGIvj5AGJFD9rIxzbXfwtHUVGxR4Q1nrrRFwV6yXPWHLHls327g9vBGvYSF80DmXrKrnX/XlrNn6QpyXaf3MGCpds0Bc8S2L7O+NlNZ2lKSspopxuPXg15ffkmZK9FzykJxUCi9/BwJjKq8Q+S0FvuFnreJRvez1kL2nvqUj0/fAdh27qyvFWbJY+PfUjlmA1t1Fd1uDsXfyxHzsUOuWlp+9BlgY2NqXt2snOL0neS2IZrmOfp7N72wbovtw1dJTC2s67/s80KCrm+aaGvVrX8ibfMGlq96yfEzP4bePWJ4atWY80hgAZyisiKIm272w+ABVOSR6wr15/0Z/dFmtn7ieDaWT1LZmuk6rh3Hclt9uZPnVh/A5mHAq0c4w+/+FZ9WOAt0eYCh6mTLs9sajLlzP5MWeQDVVZxyjqmWx1HTtk2EDmP0Z5v568YCHhjmSnxfzOdBRERERERE3F3emVgtMFx5JZ6dOlF1shqfro73EQY+uJLt1umMnLuYu/vtJn7+U/z+3nACPYFaKyV57/D68sUkHTDjfe883kiIIdBtpkPVyWo8O3XCcOWFLyekUzjxq2aQPHccA5Z60ctgoaxLfyYlPsVAHwADEVOW8OnMCZhSvehms8CtU3l2ygXMJAkJomxFLONKwWaBWyYu4YHG3XTqz6TnjjHrkd+S5OUFFgudTQm84MyX9B42lqSpozBlzyTlL3c6Y4vFtNaLblio7GJiyZLmkith9IlexNFPICIUCI/hmU0TMD3sRTegZ1R4q4mUCz9vYwYipjzL0Umu++nFkD+NIbLAubtHDM+Mns/TI0eBl6HuPo02AEPG0jt+ArFpXhhqDUTeVj97zj8mgUeenkDMSC+6YeDmiSZGf9iGcFrgH5PA6L/NxDQSunWyUVkbyvQlY52zmwo4mnUnEVPOs1MPA17uM+9Oe2E0QLfmZuO1KpwHHn+DCSNj6eYFBNxGH9c46hRO/CobyYsmMO4EXBc1hYHRruWE7TiW2+r6cG6vmEnMyJfo1smGzeM2XkgMd+y7dRiTXp6K6bcHmf3aHAZ2aXho4zHX+jNphe8ARtw6lQkj9/NIwnpGtzKOmrQNbdxZDyKHWEhMNzHtBtc2w0V8HkRERERERMTdT+x2extfDXhp2c6cofjLr+gR4I/RWP/V01yQSsITC9jUXA0bz1Aemr+IOSNC8XZLYFmtNr4sLSOox3UXl8RyV2vDUmvAq6VvxactWDy8Wt7fGudyvdmrTPictmAzeDlm0bTCZrFAp3O3czbGQuux2T5cwZ8PDWbNRLdv5hdzTW08b8uHtn59tmobhmYSPK0e18Z722a1Niw2A17udc6OvEzs9jDWP9X0rZaXVa0Ny2nwanzz3ZcLArnP30X2ne8xqa9bm4t97ufLZsFS69XwPl6o5p7JBYV0Hp+vtnd6wZ8HERERERER6UBJLIBqs4Vvqs1c7/8zrrii4bdHa0UR+Z+c4Dvn31f5hxDW07dR4XQ4e7aW42X/4Zou3nTx9ro8gV8s9yTW9xZEFfuefwnD+DlEfn9B/MBVkb1oOZax87m7I060sRWQ/Mh8ykwTeOBWP77+aDNJad2Yvn4KvdsjgSQiIiIiIiJyCXWoJBY4lgGesnyLX/drG8zIagur1Ub5ia/p7HV13bLEH4TT5Rz9GG4K9/t+Z+/Ij19tOcfS95JZWEXn6/sROfg2gn5AHxURERERERH5v6vDJbHAMSPrREUl3XyuaXMyqupkNZVV39Ddt9sPZwaWiIiIiIiIiIi0SYdMYoGjRlZVVTU1p0/T2etqPD07YTQY6pYZnj1bi9Vmo6bmNKcs3+LZqRM+Pl3arwaWiIiIiIiIiIh0GB02ieViO3MGi6WG06e/w2Y7w9lax/vtr/DwwGC4kk6drsLLy1PJKxERERERERGRH7EOn8QSERERERERERH56fcdgIiIiIiIiIiIyLkoiSUiIiIiIiIiIh2eklgiIiIiIiIiItLhKYklIiIiIiIiIiIdnpJYIiIiIiIiIiLS4SmJJVNv83wAAAAcSURBVCIiIiIiIiIiHZ6SWCIiIiIiIiIi0uH9f4hvdJ+p2JrjAAAAAElFTkSuQmCC)"
      ],
      "metadata": {
        "id": "zdcyZyeSCqcy"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Explaining"
      ],
      "metadata": {
        "id": "aRzujr-REu0H"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "An important part of analysis is to understand which features the model considered most important for decision making. For this challenge, we are going to use SHAP."
      ],
      "metadata": {
        "id": "36Bnk6SFE2NA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def shapley_feature_ranking(shap_values, X, top=10):\n",
        "  \"\"\"\n",
        "  Extract global values from SHAP\n",
        "  \"\"\"\n",
        "  feature_order = np.argsort(np.mean(np.abs(shap_values), axis=0))\n",
        "  return pd.DataFrame(\n",
        "      {\n",
        "          \"features\": [X.columns[i] for i in feature_order][::-1],\n",
        "          \"importance\": [\n",
        "              np.mean(np.abs(shap_values), axis=0)[i] for i in feature_order\n",
        "          ][::-1],\n",
        "      }\n",
        "  ).head(top)"
      ],
      "metadata": {
        "id": "RSuMFPAfEwX_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "explainer = shap.TreeExplainer(final_model)\n",
        "shap_values = explainer.shap_values(X_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rm6e7XWBpuva",
        "outputId": "f6619948-4d89-40bd-817a-c5d861c6a9f7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "LightGBM binary classifier with TreeExplainer shap values output has changed to a list of ndarray\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "top_features = shapley_feature_ranking(shap_values[1], X_test, top=X_test.shape[1])\n",
        "feat_name =  pd.merge(pd.DataFrame({'features':X_test.columns}), top_features, how=\"left\", on=[\"features\"])\n",
        "\n",
        "feat_val = [\n",
        "             col + \" [\" + str(round(feat_name.iloc[i]['importance'], 3)) + \"]\"\n",
        "             for i, col in enumerate(feat_name['features'])\n",
        "             ]\n",
        "\n",
        "shap.summary_plot(shap_values[1], X_test, feature_names=feat_val, show=False)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 574
        },
        "id": "Zu4CTgrZqT6n",
        "outputId": "b40aa43b-00ed-4cab-affe-b9ac4365cf7b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "No data for colormapping provided via 'c'. Parameters 'vmin', 'vmax' will be ignored\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x550 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "---"
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