Edelweiss_Hackathon_Machine_Learning_Hackerearth.ipynb

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        "<a href=\"https://colab.research.google.com/github/buntys2010/Edelweiss-Hackathon-Machine-Learning-Hackerearth/blob/master/Edelweiss_Hackathon_Machine_Learning_Hackerearth.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "source": [
        "# Machine Learning hackathon by Edelweiss organised by Hackerearth\n",
        "\n",
        "# Problem statements - Machine Learning – Propensity to Foreclose \n",
        "\n",
        "India is a diversified financial sector undergoing rapid expansion, both in terms of strong growth of existing financial services firms and new entities entering the market. The sector comprises commercial banks, insurance companies, non-banking financial companies, co-operatives, pension funds, mutual funds and other smaller financial entities . The Edelweiss Group is one of India's leading diversified financial services company providing a broad range of financial products and services to a substantial and diversified client base that includes corporations, institutions and individuals. Edelweiss's products and services span multiple asset classes and consumer segments across domestic and global geographies. Given the availability of various alternatives across the industry customer has a propensity to move to another financial institution for Balance Transfer. Foreclosure and balance transfer has added to the concerns of NBFCs. Foreclosure means repaying the outstanding loan amount in a single payment instead of with EMIs while balance transfer is transferring outstanding Loan availed from one Bank / Financial Institution to another Bank / Financial Institution, usually on the grounds of better service, top-up on the existing loan, proximity of branch, saving on interest repayments, etc. Losing out on customers on grounds on foreclosure and balance transfer leads to revenue loss. Acquiring a new customer can cost up to five times more than retaining an existing customer and an increase in customer retention by 5% increases profits up to 25%. NBFCs have started taking pro-active measures to ensure this is curbed; and this is where you come in! Objective is primarily to arrive at a propensity to foreclose and balance transfer an existing loan based on lead indicators such as demographics, internal behavior and performance on all credit lines; along with the estimated ‘Time to Foreclose’. May the best algorithm win!\n",
        "\n",
        "Participants are free to use any openly available data source to further enrich the dataset.\n",
        "\n",
        "# Data Description\n",
        "\n",
        "We have provided the following dataset:\n",
        "\n",
        "\n",
        "\n",
        "1.   Customer demographics\n",
        "2.   Customer transactions (ie. repayments made by the client)\n",
        "3.   List of foreclosed customers\n",
        "4.   Email interaction of the customer with the customer representative\n",
        "\n",
        "The participants are expected to predict the probability of foreclosure for each of the data points in the test set."
      ]
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    {
      "metadata": {
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      "cell_type": "markdown",
      "source": [
        "## Import Everything"
      ]
    },
    {
      "metadata": {
        "id": "gaHPaeZgZcz4",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "import numpy as np # linear algebra\n",
        "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "%matplotlib inline\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "from sklearn import model_selection\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import RobustScaler\n",
        "import lightgbm as lgb\n",
        "import xgboost as xgb\n",
        "from sklearn.decomposition import PCA,KernelPCA\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.svm import SVC\n",
        "from keras import Sequential\n",
        "from keras import layers\n",
        "from keras import backend as K\n",
        "from keras.layers.core import Dense\n",
        "from keras import regularizers\n",
        "from keras.layers import Dropout\n",
        "from keras.constraints import max_norm\n",
        "import tensorflow as tf\n",
        "import keras\n",
        "from lightgbm import LGBMClassifier"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "-kAEiaKianey",
        "colab_type": "text"
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      "cell_type": "markdown",
      "source": [
        "## Read Data and check the heads\n",
        "\n",
        "---\n"
      ]
    },
    {
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        "id": "gIbns-Jvajyk",
        "colab_type": "code",
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      "cell_type": "code",
      "source": [
        "#@title Default title text\n",
        "train_df = pd.read_csv(\"../content/train_df.csv\")\n",
        "test_df=pd.read_csv(\"../content/test_df.csv\")"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/IPython/core/interactiveshell.py:2718: DtypeWarning: Columns (35,36) have mixed types. Specify dtype option on import or set low_memory=False.\n",
            "  interactivity=interactivity, compiler=compiler, result=result)\n"
          ],
          "name": "stderr"
        }
      ]
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          "height": 34
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      "cell_type": "code",
      "source": [
        "train_df.shape"
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      "execution_count": 0,
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        {
          "output_type": "execute_result",
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      "source": [
        "train_df.head()"
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              "   AGREEMENTID  CUSTOMERID      LOAN_AMT  NET_DISBURSED_AMT  \\\n",
              "0     11220001  12001000.0  1.171011e+07       1.171011e+07   \n",
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              "4     11220010  12001009.0  7.755937e+06       7.755937e+06   \n",
              "\n",
              "  INTEREST_START_DATE  CURRENT_ROI  ORIGNAL_ROI  CURRENT_TENOR  ORIGNAL_TENOR  \\\n",
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              "1          2010-09-15    13.734072    10.612692          137.0            180   \n",
              "2          2010-11-01    16.231176    11.549106          358.0            180   \n",
              "3          2010-10-06    13.421934    10.612692           91.0            180   \n",
              "4          2010-10-26    15.606900    10.924830          341.0            180   \n",
              "\n",
              "   DUEDAY     ...      CUST_CATEGORYID   AGE  SEX  MARITAL_STATUS  \\\n",
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              "1       1     ...                  5.0  44.0    M               M   \n",
              "2       5     ...                  4.0  43.0    M               M   \n",
              "3       1     ...                  5.0  33.0    M               M   \n",
              "4       5     ...                  4.0  37.0    M               M   \n",
              "\n",
              "   QUALIFICATION  NO_OF_DEPENDENT   GROSS_INCOME  NETTAKEHOMEINCOME  \\\n",
              "0       POSTGRAD              0.0  198375.221829      198375.221829   \n",
              "1       POSTGRAD              0.0  242703.982915      242703.982915   \n",
              "2           GRAD              0.0   53536.208228       53536.208228   \n",
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              "   BRANCH_PINCODE  FORECLOSURE  \n",
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              "2        400070.0          1.0  \n",
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              "      <td>187342.198995</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>11220007</td>\n",
              "      <td>12001006.0</td>\n",
              "      <td>6.155313e+06</td>\n",
              "      <td>6.155313e+06</td>\n",
              "      <td>2010-10-11</td>\n",
              "      <td>16.543314</td>\n",
              "      <td>12.485520</td>\n",
              "      <td>262.0</td>\n",
              "      <td>180</td>\n",
              "      <td>5</td>\n",
              "      <td>...</td>\n",
              "      <td>4.0</td>\n",
              "      <td>48.0</td>\n",
              "      <td>M</td>\n",
              "      <td>M</td>\n",
              "      <td>UG</td>\n",
              "      <td>0.0</td>\n",
              "      <td>49842.149808</td>\n",
              "      <td>49842.145305</td>\n",
              "      <td>400070.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>11220009</td>\n",
              "      <td>12001008.0</td>\n",
              "      <td>9.499284e+06</td>\n",
              "      <td>9.499284e+06</td>\n",
              "      <td>2010-11-10</td>\n",
              "      <td>18.915563</td>\n",
              "      <td>13.609217</td>\n",
              "      <td>52.0</td>\n",
              "      <td>180</td>\n",
              "      <td>5</td>\n",
              "      <td>...</td>\n",
              "      <td>4.0</td>\n",
              "      <td>44.0</td>\n",
              "      <td>M</td>\n",
              "      <td>M</td>\n",
              "      <td>GRAD</td>\n",
              "      <td>0.0</td>\n",
              "      <td>183331.982693</td>\n",
              "      <td>183331.987196</td>\n",
              "      <td>400070.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 49 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "   AGREEMENTID  CUSTOMERID      LOAN_AMT  NET_DISBURSED_AMT  \\\n",
              "0     11220003  12001002.0  4.053499e+06       4.053499e+06   \n",
              "1     11220004  12001003.0  1.325703e+07       1.325703e+07   \n",
              "2     11220005  12001004.0  1.321140e+07       1.321140e+07   \n",
              "3     11220007  12001006.0  6.155313e+06       6.155313e+06   \n",
              "4     11220009  12001008.0  9.499284e+06       9.499284e+06   \n",
              "\n",
              "  INTEREST_START_DATE  CURRENT_ROI  ORIGNAL_ROI  CURRENT_TENOR  ORIGNAL_TENOR  \\\n",
              "0          2010-09-06    17.167590    12.485520          360.0            180   \n",
              "1          2010-09-29    15.606900    10.924830          198.0            180   \n",
              "2          2010-09-06    16.543314    11.861244          142.0            180   \n",
              "3          2010-10-11    16.543314    12.485520          262.0            180   \n",
              "4          2010-11-10    18.915563    13.609217           52.0            180   \n",
              "\n",
              "   DUEDAY     ...      CUST_CATEGORYID   AGE  SEX  MARITAL_STATUS  \\\n",
              "0       5     ...                  7.0  50.0    M               M   \n",
              "1       5     ...                  4.0  40.0    M               M   \n",
              "2       5     ...                  4.0  27.0    M               M   \n",
              "3       5     ...                  4.0  48.0    M               M   \n",
              "4       5     ...                  4.0  44.0    M               M   \n",
              "\n",
              "   QUALIFICATION  NO_OF_DEPENDENT   GROSS_INCOME  NETTAKEHOMEINCOME  \\\n",
              "0           GRAD              0.0       0.000000           0.000000   \n",
              "1           GRAD              0.0  365263.511624      365263.516128   \n",
              "2           GRAD              0.0  187342.198995      187342.198995   \n",
              "3             UG              0.0   49842.149808       49842.145305   \n",
              "4           GRAD              0.0  183331.982693      183331.987196   \n",
              "\n",
              "   BRANCH_PINCODE  FORECLOSURE  \n",
              "0        400070.0          NaN  \n",
              "1        400070.0          NaN  \n",
              "2        400070.0          NaN  \n",
              "3        400070.0          NaN  \n",
              "4        400070.0          NaN  \n",
              "\n",
              "[5 rows x 49 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 225
        }
      ]
    },
    {
      "metadata": {
        "id": "mX_2Rianb5B5",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Lets check Missing Values"
      ]
    },
    {
      "metadata": {
        "id": "a55tOUPVcFXt",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# Function to calculate missing values by column# Funct \n",
        "def missing_values_table(df):\n",
        "        # Total missing values\n",
        "        mis_val = df.isnull().sum()\n",
        "        \n",
        "        # Percentage of missing values\n",
        "        mis_val_percent = 100 * df.isnull().sum() / len(df)\n",
        "        \n",
        "        # Make a table with the results\n",
        "        mis_val_table = pd.concat([mis_val, mis_val_percent], axis=1)\n",
        "        \n",
        "        # Rename the columns\n",
        "        mis_val_table_ren_columns = mis_val_table.rename(\n",
        "        columns = {0 : 'Missing Values', 1 : '% of Total Values'})\n",
        "        \n",
        "        # Sort the table by percentage of missing descending\n",
        "        mis_val_table_ren_columns = mis_val_table_ren_columns[\n",
        "            mis_val_table_ren_columns.iloc[:,1] != 0].sort_values(\n",
        "        '% of Total Values', ascending=False).round(1)\n",
        "        \n",
        "        # Print some summary information\n",
        "        print (\"Your selected dataframe has \" + str(df.shape[1]) + \" columns.\\n\"      \n",
        "            \"There are \" + str(mis_val_table_ren_columns.shape[0]) +\n",
        "              \" columns that have missing values.\")\n",
        "        \n",
        "        # Return the dataframe with missing information\n",
        "        return mis_val_table_ren_columns"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "Jpy-uetzcF8Y",
        "colab_type": "code",
        "outputId": "5ee96621-f14d-4f5a-cb33-98fc16762f8b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 525
        }
      },
      "cell_type": "code",
      "source": [
        "# Missing values statistics of Customers\n",
        "missing_values = missing_values_table(train_df)\n",
        "missing_values.head(40)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Your selected dataframe has 49 columns.\n",
            "There are 15 columns that have missing values.\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>Missing Values</th>\n",
              "      <th>% of Total Values</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>QUALIFICATION</th>\n",
              "      <td>14393</td>\n",
              "      <td>71.9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>AGE</th>\n",
              "      <td>14266</td>\n",
              "      <td>71.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>MARITAL_STATUS</th>\n",
              "      <td>14266</td>\n",
              "      <td>71.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>SEX</th>\n",
              "      <td>14265</td>\n",
              "      <td>71.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>NO_OF_DEPENDENT</th>\n",
              "      <td>14063</td>\n",
              "      <td>70.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>BRANCH_PINCODE</th>\n",
              "      <td>14063</td>\n",
              "      <td>70.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>CUST_CONSTTYPE_ID</th>\n",
              "      <td>14020</td>\n",
              "      <td>70.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>CUST_CATEGORYID</th>\n",
              "      <td>14020</td>\n",
              "      <td>70.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>GROSS_INCOME</th>\n",
              "      <td>14020</td>\n",
              "      <td>70.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>NETTAKEHOMEINCOME</th>\n",
              "      <td>14020</td>\n",
              "      <td>70.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>NPA_IN_LAST_MONTH</th>\n",
              "      <td>12192</td>\n",
              "      <td>60.9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>NPA_IN_CURRENT_MONTH</th>\n",
              "      <td>12170</td>\n",
              "      <td>60.8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>CUSTOMERID</th>\n",
              "      <td>281</td>\n",
              "      <td>1.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>SCHEMEID</th>\n",
              "      <td>281</td>\n",
              "      <td>1.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LAST_RECEIPT_DATE</th>\n",
              "      <td>75</td>\n",
              "      <td>0.4</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                      Missing Values  % of Total Values\n",
              "QUALIFICATION                  14393               71.9\n",
              "AGE                            14266               71.3\n",
              "MARITAL_STATUS                 14266               71.3\n",
              "SEX                            14265               71.3\n",
              "NO_OF_DEPENDENT                14063               70.3\n",
              "BRANCH_PINCODE                 14063               70.3\n",
              "CUST_CONSTTYPE_ID              14020               70.1\n",
              "CUST_CATEGORYID                14020               70.1\n",
              "GROSS_INCOME                   14020               70.1\n",
              "NETTAKEHOMEINCOME              14020               70.1\n",
              "NPA_IN_LAST_MONTH              12192               60.9\n",
              "NPA_IN_CURRENT_MONTH           12170               60.8\n",
              "CUSTOMERID                       281                1.4\n",
              "SCHEMEID                         281                1.4\n",
              "LAST_RECEIPT_DATE                 75                0.4"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 227
        }
      ]
    },
    {
      "metadata": {
        "id": "gJDWZ5dYcJNv",
        "colab_type": "code",
        "outputId": "fcafc2e9-9755-4f73-d5a6-dfe8a7761d7d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 584
        }
      },
      "cell_type": "code",
      "source": [
        "# Missing values statistics of Customers\n",
        "missing_values = missing_values_table(test_df)\n",
        "missing_values.head(40)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Your selected dataframe has 49 columns.\n",
            "There are 17 columns that have missing values.\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>Missing Values</th>\n",
              "      <th>% of Total Values</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>FORECLOSURE</th>\n",
              "      <td>13342</td>\n",
              "      <td>100.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>QUALIFICATION</th>\n",
              "      <td>9564</td>\n",
              "      <td>71.7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>MARITAL_STATUS</th>\n",
              "      <td>9484</td>\n",
              "      <td>71.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>AGE</th>\n",
              "      <td>9484</td>\n",
              "      <td>71.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>SEX</th>\n",
              "      <td>9483</td>\n",
              "      <td>71.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>NO_OF_DEPENDENT</th>\n",
              "      <td>9370</td>\n",
              "      <td>70.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>BRANCH_PINCODE</th>\n",
              "      <td>9364</td>\n",
              "      <td>70.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>CUST_CONSTTYPE_ID</th>\n",
              "      <td>9334</td>\n",
              "      <td>70.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>CUST_CATEGORYID</th>\n",
              "      <td>9334</td>\n",
              "      <td>70.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>GROSS_INCOME</th>\n",
              "      <td>9334</td>\n",
              "      <td>70.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>NETTAKEHOMEINCOME</th>\n",
              "      <td>9334</td>\n",
              "      <td>70.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>NPA_IN_LAST_MONTH</th>\n",
              "      <td>8146</td>\n",
              "      <td>61.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>NPA_IN_CURRENT_MONTH</th>\n",
              "      <td>8133</td>\n",
              "      <td>61.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>SCHEMEID</th>\n",
              "      <td>178</td>\n",
              "      <td>1.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>CUSTOMERID</th>\n",
              "      <td>178</td>\n",
              "      <td>1.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LAST_RECEIPT_DATE</th>\n",
              "      <td>53</td>\n",
              "      <td>0.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>DPD</th>\n",
              "      <td>1</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                      Missing Values  % of Total Values\n",
              "FORECLOSURE                    13342              100.0\n",
              "QUALIFICATION                   9564               71.7\n",
              "MARITAL_STATUS                  9484               71.1\n",
              "AGE                             9484               71.1\n",
              "SEX                             9483               71.1\n",
              "NO_OF_DEPENDENT                 9370               70.2\n",
              "BRANCH_PINCODE                  9364               70.2\n",
              "CUST_CONSTTYPE_ID               9334               70.0\n",
              "CUST_CATEGORYID                 9334               70.0\n",
              "GROSS_INCOME                    9334               70.0\n",
              "NETTAKEHOMEINCOME               9334               70.0\n",
              "NPA_IN_LAST_MONTH               8146               61.1\n",
              "NPA_IN_CURRENT_MONTH            8133               61.0\n",
              "SCHEMEID                         178                1.3\n",
              "CUSTOMERID                       178                1.3\n",
              "LAST_RECEIPT_DATE                 53                0.4\n",
              "DPD                                1                0.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 228
        }
      ]
    },
    {
      "metadata": {
        "id": "qTnjEGTSemdK",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Delete Some Features with large missing values"
      ]
    },
    {
      "metadata": {
        "id": "7zrONFv8ex1o",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "#train_df = train_df.drop(['NPA_IN_CURRENT_MONTH','INTEREST_START_DATE','AUTHORIZATIONDATE','LAST_RECEIPT_DATE','DUEDAY'],axis=1)\n",
        "#test_df = test_df.drop(['NPA_IN_CURRENT_MONTH','INTEREST_START_DATE','AUTHORIZATIONDATE','LAST_RECEIPT_DATE','DUEDAY'],axis=1)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "MsyqA8JEe1Zc",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Lets create some feature genration "
      ]
    },
    {
      "metadata": {
        "id": "ArNs8GVzeziX",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "train_df['CUST_CONSTTYPE_ID']=train_df['CUST_CONSTTYPE_ID'].astype(object)\n",
        "test_df['CUST_CONSTTYPE_ID']=test_df['CUST_CONSTTYPE_ID'].astype(object)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "3cCbVJoAe-DS",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "train_df['CUST_CATEGORYID']=train_df['CUST_CATEGORYID'].astype(object)\n",
        "test_df['CUST_CATEGORYID']=test_df['CUST_CATEGORYID'].astype(object)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "IP6u9rfTfAF0",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "#train_df['Is_EXCESS_AVAILABLE'] = train_df['EXCESS_AVAILABLE'].apply(lambda x: 1 if (x!=0) else 0 )\n",
        "#test_df['Is_EXCESS_AVAILABLE'] = test_df['EXCESS_AVAILABLE'].apply(lambda x: 1 if (x!=0) else 0 )"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "ypFE8cP0JcZE",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "#train_df['Ratio_ROI'] = train_df['CURRENT_ROI'] / train_df['ORIGNAL_ROI']\n",
        "#test_df['Ratio_ROI'] = test_df['CURRENT_ROI'] / test_df['ORIGNAL_ROI']"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "hzAGAs2K5dLa",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "#test_df[test_df['Ratio_ROI'].isna()]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "HHKgE0W0TC68",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "#train_df['BALANCE_EXCESS_Min'] = train_df['BALANCE_EXCESS'].min()\n",
        "#test_df['BALANCE_EXCESS_Min'] = test_df['BALANCE_EXCESS'].min()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "UxWu9sUITC1q",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "#train_df['BALANCE_EXCESS_Mean'] = train_df['BALANCE_EXCESS'].mean()\n",
        "#test_df['BALANCE_EXCESS_Mean'] = test_df['BALANCE_EXCESS'].mean()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "vhTQHeblfB1K",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "train_df['BRANCH_PINCODE']=train_df['BRANCH_PINCODE'].astype(object)\n",
        "test_df['BRANCH_PINCODE']=test_df['BRANCH_PINCODE'].astype(object)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "Y3G9jABifDei",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# This feature overfits completly \n",
        "#train_df['ROI_Diff']=train_df['CURRENT_ROI'] - train_df['ORIGNAL_ROI'] \n",
        "#test_df['ROI_Diff']=test_df['CURRENT_ROI'] - test_df['ORIGNAL_ROI']"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "wQFN_EtzfFdK",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "#AUC decreased a bit but Confustion matrix output was good\n",
        "\n",
        "#train_df['REM_TENOR']=train_df['ORIGNAL_TENOR'] - train_df['CURRENT_TENOR']\n",
        "#test_df['REM_TENOR']=test_df['ORIGNAL_TENOR'] - test_df['CURRENT_TENOR']"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "ruOFME7TFCZ2",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "train_df['MOB']=train_df['MOB'].astype(object)\n",
        "test_df['MOB']=test_df['MOB'].astype(object)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "z5cFJexvGW9H",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "\n",
        "train_df['INTEREST_START_DATE']= pd.to_datetime(train_df['INTEREST_START_DATE'])\n",
        "train_df['AUTHORIZATIONDATE']= pd.to_datetime(train_df['AUTHORIZATIONDATE'])\n",
        "train_df['LAST_RECEIPT_DATE']= pd.to_datetime(train_df['LAST_RECEIPT_DATE'])\n",
        "\n",
        "\n",
        "test_df['INTEREST_START_DATE']= pd.to_datetime(test_df['INTEREST_START_DATE'])\n",
        "test_df['AUTHORIZATIONDATE']= pd.to_datetime(test_df['AUTHORIZATIONDATE'])\n",
        "test_df['LAST_RECEIPT_DATE']= pd.to_datetime(test_df['LAST_RECEIPT_DATE'])\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "6Ocy470WGWw3",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "train_df['INTEREST_year'] = train_df['INTEREST_START_DATE'].dt.year\n",
        "train_df['INTEREST_day_of_year'] = train_df['INTEREST_START_DATE'].dt.dayofyear\n",
        "train_df['INTEREST_weekday'] = train_df['INTEREST_START_DATE'].dt.weekday\n",
        "train_df['INTEREST_week_of_year'] = train_df['INTEREST_START_DATE'].dt.week\n",
        "train_df['INTEREST_day_of_month'] = train_df['INTEREST_START_DATE'].dt.day\n",
        "train_df['INTEREST_quarter'] = train_df['INTEREST_START_DATE'].dt.quarter\n",
        "\n",
        "test_df['INTEREST_year'] = test_df['INTEREST_START_DATE'].dt.year\n",
        "test_df['INTEREST_day_of_year'] = test_df['INTEREST_START_DATE'].dt.dayofyear\n",
        "test_df['INTEREST_weekday'] = test_df['INTEREST_START_DATE'].dt.weekday\n",
        "test_df['INTEREST_week_of_year'] = test_df['INTEREST_START_DATE'].dt.week\n",
        "test_df['INTEREST_day_of_month'] = test_df['INTEREST_START_DATE'].dt.day\n",
        "test_df['INTEREST_quarter'] = test_df['INTEREST_START_DATE'].dt.quarter"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "zS_y7O-uIqnb",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "train_df['AUTHORIZATION_year'] = train_df['AUTHORIZATIONDATE'].dt.year\n",
        "train_df['AUTHORIZATION_day_of_year'] = train_df['AUTHORIZATIONDATE'].dt.dayofyear\n",
        "train_df['AUTHORIZATION_weekday'] = train_df['AUTHORIZATIONDATE'].dt.weekday\n",
        "train_df['AUTHORIZATION_week_of_year'] = train_df['AUTHORIZATIONDATE'].dt.week\n",
        "train_df['AUTHORIZATION_day_of_month'] = train_df['AUTHORIZATIONDATE'].dt.day\n",
        "train_df['AUTHORIZATION_quarter'] = train_df['AUTHORIZATIONDATE'].dt.quarter\n",
        "\n",
        "test_df['AUTHORIZATION_year'] = test_df['AUTHORIZATIONDATE'].dt.year\n",
        "test_df['AUTHORIZATION_day_of_year'] = test_df['AUTHORIZATIONDATE'].dt.dayofyear\n",
        "test_df['AUTHORIZATION_weekday'] = test_df['AUTHORIZATIONDATE'].dt.weekday\n",
        "test_df['AUTHORIZATION_week_of_year'] = test_df['AUTHORIZATIONDATE'].dt.week\n",
        "test_df['AUTHORIZATION_day_of_month'] = test_df['AUTHORIZATIONDATE'].dt.day\n",
        "test_df['AUTHORIZATION_quarter'] = test_df['AUTHORIZATIONDATE'].dt.quarter"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "uy1PkCToIqkb",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "train_df['LAST_RECEIPT_year'] = train_df['LAST_RECEIPT_DATE'].dt.year\n",
        "train_df['LAST_RECEIPT_day_of_year'] = train_df['LAST_RECEIPT_DATE'].dt.dayofyear\n",
        "train_df['LAST_RECEIPT_weekday'] = train_df['LAST_RECEIPT_DATE'].dt.weekday\n",
        "train_df['LAST_RECEIPT_week_of_year'] = train_df['LAST_RECEIPT_DATE'].dt.week\n",
        "train_df['LAST_RECEIPT_day_of_month'] = train_df['LAST_RECEIPT_DATE'].dt.day\n",
        "train_df['LAST_RECEIPT_quarter'] = train_df['LAST_RECEIPT_DATE'].dt.quarter\n",
        "\n",
        "test_df['LAST_RECEIPT_year'] = test_df['LAST_RECEIPT_DATE'].dt.year\n",
        "test_df['LAST_RECEIPT_day_of_year'] = test_df['LAST_RECEIPT_DATE'].dt.dayofyear\n",
        "test_df['LAST_RECEIPT_weekday'] = test_df['LAST_RECEIPT_DATE'].dt.weekday\n",
        "test_df['LAST_RECEIPT_week_of_year'] = test_df['LAST_RECEIPT_DATE'].dt.week\n",
        "test_df['LAST_RECEIPT_day_of_month'] = test_df['LAST_RECEIPT_DATE'].dt.day\n",
        "test_df['LAST_RECEIPT_quarter'] = test_df['LAST_RECEIPT_DATE'].dt.quarter"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "_rqKvna2Iqhy",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "train_df = train_df.drop(['EXCESS_ADJUSTED_AMT','NPA_IN_CURRENT_MONTH','INTEREST_START_DATE','AUTHORIZATIONDATE','LAST_RECEIPT_DATE'],axis=1)\n",
        "test_df = test_df.drop(['EXCESS_ADJUSTED_AMT','NPA_IN_CURRENT_MONTH','INTEREST_START_DATE','AUTHORIZATIONDATE','LAST_RECEIPT_DATE'],axis=1)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "deJ6A8BqilSO",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Lets do one hot encoding "
      ]
    },
    {
      "metadata": {
        "id": "j0YwXPw3gBos",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "train_df = pd.get_dummies(train_df)\n",
        "test_df_lgb = pd.get_dummies(test_df)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "9wSJpYOJCOBn",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "train_df = train_df.drop(['AGREEMENTID','CUSTOMERID'],axis=1)\n",
        "test_df_lgb = test_df_lgb.drop(['AGREEMENTID','CUSTOMERID'],axis=1)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "zh_Q-McRirKx",
        "colab_type": "code",
        "outputId": "9ae79850-3680-460c-da0e-40bf6eb2995f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 244
        }
      },
      "cell_type": "code",
      "source": [
        "train_df.head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
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              "    }\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>LOAN_AMT</th>\n",
              "      <th>NET_DISBURSED_AMT</th>\n",
              "      <th>CURRENT_ROI</th>\n",
              "      <th>ORIGNAL_ROI</th>\n",
              "      <th>CURRENT_TENOR</th>\n",
              "      <th>ORIGNAL_TENOR</th>\n",
              "      <th>DUEDAY</th>\n",
              "      <th>PRE_EMI_DUEAMT</th>\n",
              "      <th>PRE_EMI_RECEIVED_AMT</th>\n",
              "      <th>PRE_EMI_OS_AMOUNT</th>\n",
              "      <th>...</th>\n",
              "      <th>BRANCH_PINCODE_627005.0</th>\n",
              "      <th>BRANCH_PINCODE_628002.0</th>\n",
              "      <th>BRANCH_PINCODE_631501.0</th>\n",
              "      <th>BRANCH_PINCODE_632007.0</th>\n",
              "      <th>BRANCH_PINCODE_635109.0</th>\n",
              "      <th>BRANCH_PINCODE_636004.0</th>\n",
              "      <th>BRANCH_PINCODE_637001.0</th>\n",
              "      <th>BRANCH_PINCODE_639001.0</th>\n",
              "      <th>BRANCH_PINCODE_641002.0</th>\n",
              "      <th>BRANCH_PINCODE_641601.0</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1.171011e+07</td>\n",
              "      <td>1.171011e+07</td>\n",
              "      <td>13.421934</td>\n",
              "      <td>10.612692</td>\n",
              "      <td>45.0</td>\n",
              "      <td>120</td>\n",
              "      <td>1</td>\n",
              "      <td>5.595992e+04</td>\n",
              "      <td>5.595992e+04</td>\n",
              "      <td>0.0</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1.929025e+07</td>\n",
              "      <td>1.929025e+07</td>\n",
              "      <td>13.734072</td>\n",
              "      <td>10.612692</td>\n",
              "      <td>137.0</td>\n",
              "      <td>180</td>\n",
              "      <td>1</td>\n",
              "      <td>1.005046e+05</td>\n",
              "      <td>1.005046e+05</td>\n",
              "      <td>0.0</td>\n",
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              "      <td>0</td>\n",
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              "      <th>2</th>\n",
              "      <td>3.933395e+06</td>\n",
              "      <td>3.933395e+06</td>\n",
              "      <td>16.231176</td>\n",
              "      <td>11.549106</td>\n",
              "      <td>358.0</td>\n",
              "      <td>180</td>\n",
              "      <td>5</td>\n",
              "      <td>2.735910e+05</td>\n",
              "      <td>2.735910e+05</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1.002259e+07</td>\n",
              "      <td>1.002259e+07</td>\n",
              "      <td>13.421934</td>\n",
              "      <td>10.612692</td>\n",
              "      <td>91.0</td>\n",
              "      <td>180</td>\n",
              "      <td>1</td>\n",
              "      <td>3.317720e+06</td>\n",
              "      <td>3.317720e+06</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>7.755937e+06</td>\n",
              "      <td>7.755937e+06</td>\n",
              "      <td>15.606900</td>\n",
              "      <td>10.924830</td>\n",
              "      <td>341.0</td>\n",
              "      <td>180</td>\n",
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              "      <td>1.497571e+06</td>\n",
              "      <td>1.497571e+06</td>\n",
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              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 459 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "       LOAN_AMT  NET_DISBURSED_AMT  CURRENT_ROI  ORIGNAL_ROI  CURRENT_TENOR  \\\n",
              "0  1.171011e+07       1.171011e+07    13.421934    10.612692           45.0   \n",
              "1  1.929025e+07       1.929025e+07    13.734072    10.612692          137.0   \n",
              "2  3.933395e+06       3.933395e+06    16.231176    11.549106          358.0   \n",
              "3  1.002259e+07       1.002259e+07    13.421934    10.612692           91.0   \n",
              "4  7.755937e+06       7.755937e+06    15.606900    10.924830          341.0   \n",
              "\n",
              "   ORIGNAL_TENOR  DUEDAY  PRE_EMI_DUEAMT  PRE_EMI_RECEIVED_AMT  \\\n",
              "0            120       1    5.595992e+04          5.595992e+04   \n",
              "1            180       1    1.005046e+05          1.005046e+05   \n",
              "2            180       5    2.735910e+05          2.735910e+05   \n",
              "3            180       1    3.317720e+06          3.317720e+06   \n",
              "4            180       5    1.497571e+06          1.497571e+06   \n",
              "\n",
              "   PRE_EMI_OS_AMOUNT           ...             BRANCH_PINCODE_627005.0  \\\n",
              "0                0.0           ...                                   0   \n",
              "1                0.0           ...                                   0   \n",
              "2                0.0           ...                                   0   \n",
              "3                0.0           ...                                   0   \n",
              "4                0.0           ...                                   0   \n",
              "\n",
              "   BRANCH_PINCODE_628002.0  BRANCH_PINCODE_631501.0  BRANCH_PINCODE_632007.0  \\\n",
              "0                        0                        0                        0   \n",
              "1                        0                        0                        0   \n",
              "2                        0                        0                        0   \n",
              "3                        0                        0                        0   \n",
              "4                        0                        0                        0   \n",
              "\n",
              "   BRANCH_PINCODE_635109.0  BRANCH_PINCODE_636004.0  BRANCH_PINCODE_637001.0  \\\n",
              "0                        0                        0                        0   \n",
              "1                        0                        0                        0   \n",
              "2                        0                        0                        0   \n",
              "3                        0                        0                        0   \n",
              "4                        0                        0                        0   \n",
              "\n",
              "   BRANCH_PINCODE_639001.0  BRANCH_PINCODE_641002.0  BRANCH_PINCODE_641601.0  \n",
              "0                        0                        0                        0  \n",
              "1                        0                        0                        0  \n",
              "2                        0                        0                        0  \n",
              "3                        0                        0                        0  \n",
              "4                        0                        0                        0  \n",
              "\n",
              "[5 rows x 459 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 248
        }
      ]
    },
    {
      "metadata": {
        "id": "gf81XLl0itGh",
        "colab_type": "code",
        "outputId": "6a4f77c6-7901-4219-f03f-ab82371fabe6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 244
        }
      },
      "cell_type": "code",
      "source": [
        "test_df_lgb.head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
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              "    }\n",
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              "    .dataframe thead th {\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>LOAN_AMT</th>\n",
              "      <th>NET_DISBURSED_AMT</th>\n",
              "      <th>CURRENT_ROI</th>\n",
              "      <th>ORIGNAL_ROI</th>\n",
              "      <th>CURRENT_TENOR</th>\n",
              "      <th>ORIGNAL_TENOR</th>\n",
              "      <th>DUEDAY</th>\n",
              "      <th>PRE_EMI_DUEAMT</th>\n",
              "      <th>PRE_EMI_RECEIVED_AMT</th>\n",
              "      <th>PRE_EMI_OS_AMOUNT</th>\n",
              "      <th>...</th>\n",
              "      <th>BRANCH_PINCODE_627005.0</th>\n",
              "      <th>BRANCH_PINCODE_628002.0</th>\n",
              "      <th>BRANCH_PINCODE_631501.0</th>\n",
              "      <th>BRANCH_PINCODE_632007.0</th>\n",
              "      <th>BRANCH_PINCODE_635109.0</th>\n",
              "      <th>BRANCH_PINCODE_636004.0</th>\n",
              "      <th>BRANCH_PINCODE_637001.0</th>\n",
              "      <th>BRANCH_PINCODE_639001.0</th>\n",
              "      <th>BRANCH_PINCODE_641002.0</th>\n",
              "      <th>BRANCH_PINCODE_641601.0</th>\n",
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              "      <td>4.053499e+06</td>\n",
              "      <td>17.167590</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1.325703e+07</td>\n",
              "      <td>1.325703e+07</td>\n",
              "      <td>15.606900</td>\n",
              "      <td>10.924830</td>\n",
              "      <td>198.0</td>\n",
              "      <td>180</td>\n",
              "      <td>5</td>\n",
              "      <td>2.844173e+04</td>\n",
              "      <td>2.844173e+04</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1.321140e+07</td>\n",
              "      <td>1.321140e+07</td>\n",
              "      <td>16.543314</td>\n",
              "      <td>11.861244</td>\n",
              "      <td>142.0</td>\n",
              "      <td>180</td>\n",
              "      <td>5</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>6.155313e+06</td>\n",
              "      <td>6.155313e+06</td>\n",
              "      <td>16.543314</td>\n",
              "      <td>12.485520</td>\n",
              "      <td>262.0</td>\n",
              "      <td>180</td>\n",
              "      <td>5</td>\n",
              "      <td>4.514479e+06</td>\n",
              "      <td>4.514479e+06</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>9.499284e+06</td>\n",
              "      <td>9.499284e+06</td>\n",
              "      <td>18.915563</td>\n",
              "      <td>13.609217</td>\n",
              "      <td>52.0</td>\n",
              "      <td>180</td>\n",
              "      <td>5</td>\n",
              "      <td>1.508017e+06</td>\n",
              "      <td>1.508017e+06</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 425 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "       LOAN_AMT  NET_DISBURSED_AMT  CURRENT_ROI  ORIGNAL_ROI  CURRENT_TENOR  \\\n",
              "0  4.053499e+06       4.053499e+06    17.167590    12.485520          360.0   \n",
              "1  1.325703e+07       1.325703e+07    15.606900    10.924830          198.0   \n",
              "2  1.321140e+07       1.321140e+07    16.543314    11.861244          142.0   \n",
              "3  6.155313e+06       6.155313e+06    16.543314    12.485520          262.0   \n",
              "4  9.499284e+06       9.499284e+06    18.915563    13.609217           52.0   \n",
              "\n",
              "   ORIGNAL_TENOR  DUEDAY  PRE_EMI_DUEAMT  PRE_EMI_RECEIVED_AMT  \\\n",
              "0            180       5    0.000000e+00          0.000000e+00   \n",
              "1            180       5    2.844173e+04          2.844173e+04   \n",
              "2            180       5    0.000000e+00          0.000000e+00   \n",
              "3            180       5    4.514479e+06          4.514479e+06   \n",
              "4            180       5    1.508017e+06          1.508017e+06   \n",
              "\n",
              "   PRE_EMI_OS_AMOUNT           ...             BRANCH_PINCODE_627005.0  \\\n",
              "0                0.0           ...                                   0   \n",
              "1                0.0           ...                                   0   \n",
              "2                0.0           ...                                   0   \n",
              "3                0.0           ...                                   0   \n",
              "4                0.0           ...                                   0   \n",
              "\n",
              "   BRANCH_PINCODE_628002.0  BRANCH_PINCODE_631501.0  BRANCH_PINCODE_632007.0  \\\n",
              "0                        0                        0                        0   \n",
              "1                        0                        0                        0   \n",
              "2                        0                        0                        0   \n",
              "3                        0                        0                        0   \n",
              "4                        0                        0                        0   \n",
              "\n",
              "   BRANCH_PINCODE_635109.0  BRANCH_PINCODE_636004.0  BRANCH_PINCODE_637001.0  \\\n",
              "0                        0                        0                        0   \n",
              "1                        0                        0                        0   \n",
              "2                        0                        0                        0   \n",
              "3                        0                        0                        0   \n",
              "4                        0                        0                        0   \n",
              "\n",
              "   BRANCH_PINCODE_639001.0  BRANCH_PINCODE_641002.0  BRANCH_PINCODE_641601.0  \n",
              "0                        0                        0                        0  \n",
              "1                        0                        0                        0  \n",
              "2                        0                        0                        0  \n",
              "3                        0                        0                        0  \n",
              "4                        0                        0                        0  \n",
              "\n",
              "[5 rows x 425 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 249
        }
      ]
    },
    {
      "metadata": {
        "id": "zS1Zk5Aii6PY",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Align train and test dataframes "
      ]
    },
    {
      "metadata": {
        "id": "-oHk90MjjE4b",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "test_df_lgb = test_df_lgb.drop('FORECLOSURE',axis=1)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "tYrMa0mZjDTW",
        "colab_type": "code",
        "outputId": "0a2345e5-8723-468b-825d-4c576c242f78",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        }
      },
      "cell_type": "code",
      "source": [
        "# Align train and test\n",
        "\n",
        "train_labels = train_df['FORECLOSURE']\n",
        "\n",
        "# Align the training and testing data, keep only columns present in both dataframes\n",
        "train_df, test_df_lgb = train_df.align(test_df_lgb, join = 'inner', axis = 1)\n",
        "\n",
        "# Add the target back in\n",
        "train_df['FORECLOSURE'] = train_labels\n",
        "\n",
        "print('Training Features shape: ', train_df.shape)\n",
        "print('Testing Features shape: ', test_df_lgb.shape)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Training Features shape:  (20012, 381)\n",
            "Testing Features shape:  (13342, 380)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "msJifTVpHZV4",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "#train_df.to_csv(\"weka_train.csv\",index=False)\n",
        "#test_df.to_csv(\"weka_test.csv\",index=False)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "Qg562kyqjJ0z",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Lets do train_test_Split"
      ]
    },
    {
      "metadata": {
        "id": "1v9eUr9AjHGF",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "train_df = train_df.reindex(\n",
        "    np.random.permutation(train_df.index))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "VhwkWSO6FNTP",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "train_df=train_df.fillna(0)\n",
        "test_df= test_df.fillna(0)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "0OKFcBK3jQlF",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "y = train_df['FORECLOSURE']\n",
        "X = train_df.drop('FORECLOSURE',axis=1)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "yDm7Mr8poEAN",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# Feature Scaling\n",
        "#from sklearn.preprocessing import StandardScaler\n",
        "#sc = StandardScaler()\n",
        "#X = sc.fit_transform(X)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "areJrX1LjTHO",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.20, random_state=10,stratify=y.values)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "4IqGQ-XfjVG2",
        "colab_type": "code",
        "outputId": "4a822e7b-dfee-4b2d-e97c-bb0636b111b4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 171
        }
      },
      "cell_type": "code",
      "source": [
        "# Feature Scaling\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "sc = StandardScaler()\n",
        "X_train = sc.fit_transform(X_train)\n",
        "X_test = sc.transform(X_test)\n",
        "test_df_lgb = sc.transform(test_df_lgb)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/preprocessing/data.py:645: DataConversionWarning: Data with input dtype uint8, int64, float64 were all converted to float64 by StandardScaler.\n",
            "  return self.partial_fit(X, y)\n",
            "/usr/local/lib/python3.6/dist-packages/sklearn/base.py:464: DataConversionWarning: Data with input dtype uint8, int64, float64 were all converted to float64 by StandardScaler.\n",
            "  return self.fit(X, **fit_params).transform(X)\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:4: DataConversionWarning: Data with input dtype uint8, int64, float64 were all converted to float64 by StandardScaler.\n",
            "  after removing the cwd from sys.path.\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:5: DataConversionWarning: Data with input dtype uint8, int64, float64 were all converted to float64 by StandardScaler.\n",
            "  \"\"\"\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "metadata": {
        "id": "CBuGb06iDwTH",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "##Traditional algos - Baseline"
      ]
    },
    {
      "metadata": {
        "id": "A06nDSVxDztG",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import LogisticRegression\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "PNOvmg-dEa3o",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "Log = RandomForestClassifier(n_estimators=5000,class_weight='balanced',max_depth=7,min_samples_leaf=10,min_weight_fraction_leaf=0.0001)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "-hIjlSqPEawt",
        "colab_type": "code",
        "outputId": "7d2b6294-3163-4459-b4c3-9d11f11632ca",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 134
        }
      },
      "cell_type": "code",
      "source": [
        "Log.fit(X_train,y_train)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestClassifier(bootstrap=True, class_weight='balanced',\n",
              "            criterion='gini', max_depth=7, max_features='auto',\n",
              "            max_leaf_nodes=None, min_impurity_decrease=0.0,\n",
              "            min_impurity_split=None, min_samples_leaf=10,\n",
              "            min_samples_split=2, min_weight_fraction_leaf=0.0001,\n",
              "            n_estimators=5000, n_jobs=None, oob_score=False,\n",
              "            random_state=None, verbose=0, warm_start=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 362
        }
      ]
    },
    {
      "metadata": {
        "id": "kmAaBxg-Eap-",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import confusion_matrix"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "Uyd-P4DPEaj8",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "y_pred = Log.predict(X_test)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "Emx5JTEiF8IC",
        "colab_type": "code",
        "outputId": "732f945f-3fbc-49bc-e9d8-aaed7c746aa6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        }
      },
      "cell_type": "code",
      "source": [
        "confusion_matrix(y_test,y_pred)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[3378,  266],\n",
              "       [  11,  348]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 365
        }
      ]
    },
    {
      "metadata": {
        "id": "6QlKQgTBF8A7",
        "colab_type": "code",
        "outputId": "89e429cd-4c7e-4cae-f69a-6d68494804a7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import roc_auc_score\n",
        "roc_auc_score(y_test,y_pred)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9481813122804227"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 366
        }
      ]
    },
    {
      "metadata": {
        "id": "xtE5iNqhjfoa",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Create a model LightGBM"
      ]
    },
    {
      "metadata": {
        "id": "45joyC-jjdH9",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "def run_lgb(X_train, X_test, y_train, y_test, test_df):\n",
        "    params = {\n",
        "        \"objective\" : \"binary\",\n",
        "       \"n_estimators\":20000, # changed from 20000\n",
        "       \"reg_alpha\" :0.5005020213127344,\n",
        "       \"reg_lambda\":0.45121616279208887, # changed from 0.3\n",
        "        #'lambda_l2':0.6, # changed from 0.6\n",
        "      # \"n_jobs\":-1,\n",
        "       \"colsample_bytree\":0.8970523178797932, #changed from 0.8\n",
        "      # \"min_child_weight\":0.99,\n",
        "       \"subsample_for_bin\":260000,\n",
        "       \"min_data_in_leaf\":45, #changed form 100\n",
        "       #\"nthread\":4,\n",
        "       \"metric\" : \"auc\",\n",
        "       \"num_leaves\" : 138,  #best upto now 128\n",
        "       \"learning_rate\" :0.035742378218536576,  #changed from 0.001\n",
        "       #\"verbosity\" : -1,\n",
        "       #\"seed\": 120,\n",
        "       #\"max_bin\":250, #changed from 200\n",
        "       #'max_depth':7, # changed from 7\n",
        "       #'min_gain_to_split':0.2, #Changed from 0.09\n",
        "       #'scale_pos_weight':3.5, #Changed from 3\n",
        "       # 'bagging_fraction':0.7, #changed from 0.8\n",
        "       # 'feature_fraction':0.99,\n",
        "        #'tree_learner':'voting'\n",
        "        'class_weight':'balanced'\n",
        "    }\n",
        "    \n",
        "    lgtrain = lgb.Dataset(X_train, label=y_train)\n",
        "    lgval = lgb.Dataset(X_test, label=y_test)\n",
        "    evals_result = {}\n",
        "    model = lgb.train(params, lgtrain, 10000, \n",
        "                      valid_sets=[lgtrain, lgval], \n",
        "                      early_stopping_rounds=100, \n",
        "                      verbose_eval=50, \n",
        "                      evals_result=evals_result)\n",
        "    \n",
        "    pred_test_y = model.predict(test_df, num_iteration=model.best_iteration)\n",
        "    return pred_test_y, model, evals_result"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "ru8pGzdKjkM7",
        "colab_type": "code",
        "outputId": "b0e0fe6a-ec3b-456c-bfef-59e93806b235",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 221
        }
      },
      "cell_type": "code",
      "source": [
        "pred_test, model, evals_result = run_lgb(X_train, X_test, y_train, y_test, test_df_lgb)\n",
        "print(\"LightGBM Training Completed...\")"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/lightgbm/engine.py:118: UserWarning: Found `n_estimators` in params. Will use it instead of argument\n",
            "  warnings.warn(\"Found `{}` in params. Will use it instead of argument\".format(alias))\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Training until validation scores don't improve for 100 rounds.\n",
            "[50]\ttraining's auc: 0.999128\tvalid_1's auc: 0.996316\n",
            "[100]\ttraining's auc: 0.999722\tvalid_1's auc: 0.997013\n",
            "[150]\ttraining's auc: 0.999967\tvalid_1's auc: 0.997642\n",
            "[200]\ttraining's auc: 0.999999\tvalid_1's auc: 0.997816\n",
            "[250]\ttraining's auc: 1\tvalid_1's auc: 0.997893\n",
            "[300]\ttraining's auc: 1\tvalid_1's auc: 0.99797\n",
            "Early stopping, best iteration is:\n",
            "[236]\ttraining's auc: 1\tvalid_1's auc: 0.997881\n",
            "LightGBM Training Completed...\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "GlXoxYN2S2Mv",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Lets try and Tune HyperParameter ,Refer blog\n",
        "\n",
        "Using Hyperopt library to find best hyper parameters"
      ]
    },
    {
      "metadata": {
        "id": "Mt4eIKw-eFaa",
        "colab_type": "code",
        "outputId": "b2648d46-3416-43d8-fbfb-b1f63cd3ae33",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 287
        }
      },
      "cell_type": "code",
      "source": [
        "# Refer https://github.com/WillKoehrsen/hyperparameter-optimization/blob/master/Bayesian%20Hyperparameter%20Optimization%20of%20Gradient%20Boosting%20Machine.ipynb\n",
        "\n",
        "train = pd.DataFrame(X_train)\n",
        "test = pd.DataFrame(X_test)\n",
        "\n",
        "# Extract the labels and format properly\n",
        "train_labels = np.array(y_train.astype(np.int32)).reshape((-1,))\n",
        "test_labels = np.array(y_test.astype(np.int32)).reshape((-1,))\n",
        "\n",
        "# Drop the unneeded columns\n",
        "#train = train.drop(columns = ['FORECLOSURE'],axis=1)\n",
        "\n",
        "\n",
        "# Convert to numpy array for splitting in cross validation\n",
        "features = np.array(train)\n",
        "test_features = np.array(test)\n",
        "labels = train_labels[:]\n",
        "\n",
        "print('Train shape: ', train.shape)\n",
        "print('Test shape: ', test.shape)\n",
        "train.head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train shape:  (16009, 380)\n",
            "Test shape:  (4003, 380)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "  <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>5</th>\n",
              "      <th>6</th>\n",
              "      <th>7</th>\n",
              "      <th>8</th>\n",
              "      <th>9</th>\n",
              "      <th>...</th>\n",
              "      <th>370</th>\n",
              "      <th>371</th>\n",
              "      <th>372</th>\n",
              "      <th>373</th>\n",
              "      <th>374</th>\n",
              "      <th>375</th>\n",
              "      <th>376</th>\n",
              "      <th>377</th>\n",
              "      <th>378</th>\n",
              "      <th>379</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>-0.354702</td>\n",
              "      <td>-0.362445</td>\n",
              "      <td>1.714958</td>\n",
              "      <td>1.778090</td>\n",
              "      <td>0.831828</td>\n",
              "      <td>1.265841</td>\n",
              "      <td>-0.277332</td>\n",
              "      <td>-0.119002</td>\n",
              "      <td>-0.118752</td>\n",
              "      <td>-0.028603</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.06385</td>\n",
              "      <td>-0.067682</td>\n",
              "      <td>-0.040333</td>\n",
              "      <td>-0.054839</td>\n",
              "      <td>-0.041102</td>\n",
              "      <td>-0.051288</td>\n",
              "      <td>-0.054263</td>\n",
              "      <td>-0.071312</td>\n",
              "      <td>-0.109302</td>\n",
              "      <td>-0.063355</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>-0.352963</td>\n",
              "      <td>-0.351566</td>\n",
              "      <td>-0.819104</td>\n",
              "      <td>-0.969440</td>\n",
              "      <td>1.715829</td>\n",
              "      <td>1.265841</td>\n",
              "      <td>-0.277332</td>\n",
              "      <td>-0.114779</td>\n",
              "      <td>-0.114524</td>\n",
              "      <td>-0.028603</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.06385</td>\n",
              "      <td>-0.067682</td>\n",
              "      <td>-0.040333</td>\n",
              "      <td>-0.054839</td>\n",
              "      <td>-0.041102</td>\n",
              "      <td>-0.051288</td>\n",
              "      <td>-0.054263</td>\n",
              "      <td>-0.071312</td>\n",
              "      <td>-0.109302</td>\n",
              "      <td>-0.063355</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>-0.218917</td>\n",
              "      <td>-0.216420</td>\n",
              "      <td>0.076777</td>\n",
              "      <td>0.235617</td>\n",
              "      <td>-0.208172</td>\n",
              "      <td>-0.076116</td>\n",
              "      <td>-0.277332</td>\n",
              "      <td>-0.117218</td>\n",
              "      <td>-0.116966</td>\n",
              "      <td>-0.028603</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.06385</td>\n",
              "      <td>-0.067682</td>\n",
              "      <td>-0.040333</td>\n",
              "      <td>-0.054839</td>\n",
              "      <td>-0.041102</td>\n",
              "      <td>-0.051288</td>\n",
              "      <td>-0.054263</td>\n",
              "      <td>-0.071312</td>\n",
              "      <td>-0.109302</td>\n",
              "      <td>-0.063355</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>-0.246301</td>\n",
              "      <td>-0.244028</td>\n",
              "      <td>-1.228649</td>\n",
              "      <td>-1.451463</td>\n",
              "      <td>0.190495</td>\n",
              "      <td>-0.076116</td>\n",
              "      <td>-0.277332</td>\n",
              "      <td>-0.109837</td>\n",
              "      <td>-0.109575</td>\n",
              "      <td>-0.028603</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.06385</td>\n",
              "      <td>-0.067682</td>\n",
              "      <td>-0.040333</td>\n",
              "      <td>-0.054839</td>\n",
              "      <td>-0.041102</td>\n",
              "      <td>-0.051288</td>\n",
              "      <td>-0.054263</td>\n",
              "      <td>-0.071312</td>\n",
              "      <td>-0.109302</td>\n",
              "      <td>-0.063355</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>3.363542</td>\n",
              "      <td>3.395419</td>\n",
              "      <td>-0.358365</td>\n",
              "      <td>-0.848935</td>\n",
              "      <td>3.518497</td>\n",
              "      <td>1.265841</td>\n",
              "      <td>-0.277332</td>\n",
              "      <td>-0.027511</td>\n",
              "      <td>-0.027148</td>\n",
              "      <td>-0.028603</td>\n",
              "      <td>...</td>\n",
              "      <td>-0.06385</td>\n",
              "      <td>-0.067682</td>\n",
              "      <td>-0.040333</td>\n",
              "      <td>-0.054839</td>\n",
              "      <td>-0.041102</td>\n",
              "      <td>-0.051288</td>\n",
              "      <td>-0.054263</td>\n",
              "      <td>-0.071312</td>\n",
              "      <td>-0.109302</td>\n",
              "      <td>-0.063355</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 380 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "        0         1         2         3         4         5         6    \\\n",
              "0 -0.354702 -0.362445  1.714958  1.778090  0.831828  1.265841 -0.277332   \n",
              "1 -0.352963 -0.351566 -0.819104 -0.969440  1.715829  1.265841 -0.277332   \n",
              "2 -0.218917 -0.216420  0.076777  0.235617 -0.208172 -0.076116 -0.277332   \n",
              "3 -0.246301 -0.244028 -1.228649 -1.451463  0.190495 -0.076116 -0.277332   \n",
              "4  3.363542  3.395419 -0.358365 -0.848935  3.518497  1.265841 -0.277332   \n",
              "\n",
              "        7         8         9      ...         370       371       372  \\\n",
              "0 -0.119002 -0.118752 -0.028603    ...    -0.06385 -0.067682 -0.040333   \n",
              "1 -0.114779 -0.114524 -0.028603    ...    -0.06385 -0.067682 -0.040333   \n",
              "2 -0.117218 -0.116966 -0.028603    ...    -0.06385 -0.067682 -0.040333   \n",
              "3 -0.109837 -0.109575 -0.028603    ...    -0.06385 -0.067682 -0.040333   \n",
              "4 -0.027511 -0.027148 -0.028603    ...    -0.06385 -0.067682 -0.040333   \n",
              "\n",
              "        373       374       375       376       377       378       379  \n",
              "0 -0.054839 -0.041102 -0.051288 -0.054263 -0.071312 -0.109302 -0.063355  \n",
              "1 -0.054839 -0.041102 -0.051288 -0.054263 -0.071312 -0.109302 -0.063355  \n",
              "2 -0.054839 -0.041102 -0.051288 -0.054263 -0.071312 -0.109302 -0.063355  \n",
              "3 -0.054839 -0.041102 -0.051288 -0.054263 -0.071312 -0.109302 -0.063355  \n",
              "4 -0.054839 -0.041102 -0.051288 -0.054263 -0.071312 -0.109302 -0.063355  \n",
              "\n",
              "[5 rows x 380 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1045
        }
      ]
    },
    {
      "metadata": {
        "id": "iep-mVTfkDPZ",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "train_set = lgb.Dataset(features, label = labels)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "Y3mEY-sLS6mg",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# Modeling\n",
        "import lightgbm as lgb\n",
        "\n",
        "# Evaluation of the model\n",
        "from sklearn.model_selection import KFold\n",
        "\n",
        "MAX_EVALS = 500\n",
        "N_FOLDS = 10"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "6ufY_0RAZieq",
        "colab_type": "code",
        "outputId": "c2aaa68c-ba02-4c3e-a57f-dfb2b381e9b1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 119
        }
      },
      "cell_type": "code",
      "source": [
        "# Model with default hyperparameters\n",
        "model = lgb.LGBMClassifier()\n",
        "model"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LGBMClassifier(boosting_type='gbdt', class_weight=None, colsample_bytree=1.0,\n",
              "        importance_type='split', learning_rate=0.1, max_depth=-1,\n",
              "        min_child_samples=20, min_child_weight=0.001, min_split_gain=0.0,\n",
              "        n_estimators=100, n_jobs=-1, num_leaves=31, objective=None,\n",
              "        random_state=None, reg_alpha=0.0, reg_lambda=0.0, silent=True,\n",
              "        subsample=1.0, subsample_for_bin=200000, subsample_freq=0)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1048
        }
      ]
    },
    {
      "metadata": {
        "id": "56O1fduzZia_",
        "colab_type": "code",
        "outputId": "5f4c0152-30a6-459c-efca-a8a80b8bb477",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import roc_auc_score\n",
        "from timeit import default_timer as timer\n",
        "\n",
        "start = timer()\n",
        "model.fit(features, labels)\n",
        "train_time = timer() - start\n",
        "\n",
        "predictions = model.predict_proba(test_features)[:, 1]\n",
        "auc = roc_auc_score(test_labels, predictions)\n",
        "\n",
        "print('The baseline score on the test set is {:.4f}.'.format(auc))\n",
        "print('The baseline training time is {:.4f} seconds'.format(train_time))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "The baseline score on the test set is 0.9974.\n",
            "The baseline training time is 2.9697 seconds\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "HPmb4EcaZiXO",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "import csv\n",
        "from hyperopt import STATUS_OK\n",
        "from timeit import default_timer as timer\n",
        "\n",
        "def objective(params, n_folds = N_FOLDS):\n",
        "    \"\"\"Objective function for Gradient Boosting Machine Hyperparameter Optimization\"\"\"\n",
        "    \n",
        "    # Keep track of evals\n",
        "    global ITERATION\n",
        "    \n",
        "    ITERATION += 1\n",
        "    \n",
        "    # Retrieve the subsample if present otherwise set to 1.0\n",
        "    subsample = params['boosting_type'].get('subsample', 1.0)\n",
        "    \n",
        "    # Extract the boosting type\n",
        "    params['boosting_type'] = params['boosting_type']['boosting_type']\n",
        "    params['subsample'] = subsample\n",
        "    \n",
        "    # Make sure parameters that need to be integers are integers\n",
        "    for parameter_name in ['num_leaves', 'subsample_for_bin', 'min_child_samples']:\n",
        "        params[parameter_name] = int(params[parameter_name])\n",
        "    \n",
        "    start = timer()\n",
        "    \n",
        "    # Perform n_folds cross validation\n",
        "    cv_results = lgb.cv(params, train_set, num_boost_round = 2000, nfold = n_folds, \n",
        "                        early_stopping_rounds = 100, metrics = 'auc', seed = 50)\n",
        "    \n",
        "    run_time = timer() - start\n",
        "    \n",
        "    # Extract the best score\n",
        "    best_score = np.max(cv_results['auc-mean'])\n",
        "    \n",
        "    # Loss must be minimized\n",
        "    loss = 1 - best_score\n",
        "    \n",
        "    # Boosting rounds that returned the highest cv score\n",
        "    n_estimators = int(np.argmax(cv_results['auc-mean']) + 1)\n",
        "\n",
        "    # Write to the csv file ('a' means append)\n",
        "    of_connection = open(out_file, 'a')\n",
        "    writer = csv.writer(of_connection)\n",
        "    writer.writerow([loss, params, ITERATION, n_estimators, run_time])\n",
        "    \n",
        "    # Dictionary with information for evaluation\n",
        "    return {'loss': loss, 'params': params, 'iteration': ITERATION,\n",
        "            'estimators': n_estimators, \n",
        "            'train_time': run_time, 'status': STATUS_OK}\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "Z3F77yRpZiT4",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "from hyperopt import hp\n",
        "from hyperopt.pyll.stochastic import sample"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "exN4qqtiZiPe",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# Create the learning rate\n",
        "learning_rate = {'learning_rate': hp.loguniform('learning_rate', np.log(0.0001), np.log(0.05))}"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "BhCghdPhZiGY",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# Discrete uniform distribution\n",
        "num_leaves = {'num_leaves': hp.quniform('num_leaves', 30, 200, 1)}"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "IrLrFVBoZiB_",
        "colab_type": "code",
        "outputId": "5e57bc93-6985-4840-f477-057614ec78b2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "# boosting type domain \n",
        "boosting_type = {'boosting_type': hp.choice('boosting_type', \n",
        "                                            [{'boosting_type': 'gbdt', 'subsample': hp.uniform('subsample', 0.5, 1)}, \n",
        "                                             {'boosting_type': 'dart', 'subsample': hp.uniform('subsample', 0.5, 1)},\n",
        "                                             {'boosting_type': 'goss', 'subsample': 1.0}])}\n",
        "\n",
        "# Draw a sample\n",
        "params = sample(boosting_type)\n",
        "params"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'boosting_type': {'boosting_type': 'goss', 'subsample': 1.0}}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1054
        }
      ]
    },
    {
      "metadata": {
        "id": "t5693HLabxJP",
        "colab_type": "code",
        "outputId": "90ea7634-0b0a-4d23-e5a3-f143c234ddd6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "# Retrieve the subsample if present otherwise set to 1.0\n",
        "subsample = params['boosting_type'].get('subsample', 1.0)\n",
        "\n",
        "# Extract the boosting type\n",
        "params['boosting_type'] = params['boosting_type']['boosting_type']\n",
        "params['subsample'] = subsample\n",
        "\n",
        "params"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'boosting_type': 'goss', 'subsample': 1.0}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1055
        }
      ]
    },
    {
      "metadata": {
        "id": "kwaicIFnbw-0",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# Define the search space\n",
        "space = {\n",
        "    'class_weight': hp.choice('class_weight', [None, 'balanced']),\n",
        "    'boosting_type': hp.choice('boosting_type', [{'boosting_type': 'gbdt', 'subsample': hp.uniform('gdbt_subsample', 0.5, 1)}, \n",
        "                                                 {'boosting_type': 'dart', 'subsample': hp.uniform('dart_subsample', 0.5, 1)},\n",
        "                                                 {'boosting_type': 'goss', 'subsample': 1.0}]),\n",
        "    'num_leaves': hp.quniform('num_leaves', 30, 250, 1),\n",
        "    'learning_rate': hp.loguniform('learning_rate', np.log(0.01), np.log(0.2)),\n",
        "    'subsample_for_bin': hp.quniform('subsample_for_bin', 20000, 300000, 20000),\n",
        "    'min_child_samples': hp.quniform('min_child_samples', 20, 500, 5),\n",
        "    'reg_alpha': hp.uniform('reg_alpha', 0.0, 1.0),\n",
        "    'reg_lambda': hp.uniform('reg_lambda', 0.0, 1.0),\n",
        "    'colsample_bytree': hp.uniform('colsample_by_tree', 0.6, 1.0),\n",
        "    'scale_pos_weight':hp.quniform('scale_pos_weight', 1, 10, 1),\n",
        "    'bagging_fraction':hp.quniform('bagging_fraction', 0.1, 1.0, 0.1),\n",
        "    #'max_depth': hp.choice('max_depth', np.arange(1, 14, dtype=int)),\n",
        "    'max_bin':hp.choice('max_bin', np.arange(1, 450, dtype=int)),\n",
        "    'lambda_l2':hp.quniform('lambda_l2', 0.0, 1.0, 0.1)\n",
        "}"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "UL2cjqXlbwzs",
        "colab_type": "code",
        "outputId": "3b54d2b8-ec5a-4c51-f57c-9f51473cc78c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 255
        }
      },
      "cell_type": "code",
      "source": [
        "# Sample from the full space\n",
        "x = sample(space)\n",
        "\n",
        "# Conditional logic to assign top-level keys\n",
        "subsample = x['boosting_type'].get('subsample', 1.0)\n",
        "x['boosting_type'] = x['boosting_type']['boosting_type']\n",
        "x['subsample'] = subsample\n",
        "\n",
        "x"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'bagging_fraction': 0.30000000000000004,\n",
              " 'boosting_type': 'goss',\n",
              " 'class_weight': None,\n",
              " 'colsample_bytree': 0.8062121513192683,\n",
              " 'lambda_l2': 0.9,\n",
              " 'learning_rate': 0.012598418121390169,\n",
              " 'max_bin': 242,\n",
              " 'min_child_samples': 375.0,\n",
              " 'num_leaves': 204.0,\n",
              " 'reg_alpha': 0.8352685137612773,\n",
              " 'reg_lambda': 0.8955823083124309,\n",
              " 'scale_pos_weight': 7.0,\n",
              " 'subsample': 1.0,\n",
              " 'subsample_for_bin': 240000.0}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1057
        }
      ]
    },
    {
      "metadata": {
        "id": "RmclD6TZbwuL",
        "colab_type": "code",
        "outputId": "cc5d8be7-105c-48cf-f52a-43efa644a1f0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 255
        }
      },
      "cell_type": "code",
      "source": [
        "\n",
        "x = sample(space)\n",
        "subsample = x['boosting_type'].get('subsample', 1.0)\n",
        "x['boosting_type'] = x['boosting_type']['boosting_type']\n",
        "x['subsample'] = subsample\n",
        "x"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'bagging_fraction': 1.0,\n",
              " 'boosting_type': 'gbdt',\n",
              " 'class_weight': None,\n",
              " 'colsample_bytree': 0.6148470107658849,\n",
              " 'lambda_l2': 0.7000000000000001,\n",
              " 'learning_rate': 0.08982841846703167,\n",
              " 'max_bin': 16,\n",
              " 'min_child_samples': 390.0,\n",
              " 'num_leaves': 77.0,\n",
              " 'reg_alpha': 0.293731843692861,\n",
              " 'reg_lambda': 0.9764520123069593,\n",
              " 'scale_pos_weight': 2.0,\n",
              " 'subsample': 0.5646721690792957,\n",
              " 'subsample_for_bin': 100000.0}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1058
        }
      ]
    },
    {
      "metadata": {
        "id": "RhKuM4yHbwpE",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "from hyperopt import tpe\n",
        "\n",
        "# optimization algorithm\n",
        "tpe_algorithm = tpe.suggest"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "1ahCO9GhbwJ7",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "from hyperopt import Trials\n",
        "\n",
        "# Keep track of results\n",
        "bayes_trials = Trials()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "_dR1Z-tMcW9Z",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# File to save first results\n",
        "out_file = '../content/gbm_trials.csv'\n",
        "of_connection = open(out_file, 'w')\n",
        "writer = csv.writer(of_connection)\n",
        "\n",
        "# Write the headers to the file\n",
        "writer.writerow(['loss', 'params', 'iteration', 'estimators', 'train_time'])\n",
        "of_connection.close()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "j6624ONacW44",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "from hyperopt import fmin"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "6lEO0TuTcWz4",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "%%capture\n",
        "\n",
        "# Global variable\n",
        "global  ITERATION\n",
        "\n",
        "ITERATION = 0\n",
        "\n",
        "# Run optimization\n",
        "best = fmin(fn = objective, space = space, algo = tpe.suggest, \n",
        "            max_evals = MAX_EVALS, trials = bayes_trials, rstate = np.random.RandomState(50))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "kOjfNk8xlz3P",
        "colab_type": "code",
        "outputId": "509332ab-5a5b-4618-9d75-20af34d876eb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "# Sort the trials with lowest loss (highest AUC) first\n",
        "bayes_trials_results = sorted(bayes_trials.results, key = lambda x: x['loss'])\n",
        "bayes_trials_results[:2]"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "[]"
            ],
            "text/plain": [
              "[]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1004
        }
      ]
    },
    {
      "metadata": {
        "id": "YQOfLilbl35i",
        "colab_type": "code",
        "outputId": "03c5a479-29b2-4130-9b7b-c0cfc3ac1e7a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49
        }
      },
      "cell_type": "code",
      "source": [
        "results = pd.read_csv('../content/gbm_trials.csv')\n",
        "\n",
        "# Sort with best scores on top and reset index for slicing\n",
        "results.sort_values('loss', ascending = True, inplace = True)\n",
        "results.reset_index(inplace = True, drop = True)\n",
        "results.head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>loss</th>\n",
              "      <th>params</th>\n",
              "      <th>iteration</th>\n",
              "      <th>estimators</th>\n",
              "      <th>train_time</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "Empty DataFrame\n",
              "Columns: [loss, params, iteration, estimators, train_time]\n",
              "Index: []"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1005
        }
      ]
    },
    {
      "metadata": {
        "id": "NqfQBJOTl300",
        "colab_type": "code",
        "outputId": "352e0140-64b0-42d8-b452-4ea105dc2610",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1166
        }
      },
      "cell_type": "code",
      "source": [
        "import ast\n",
        "\n",
        "# Convert from a string to a dictionary\n",
        "ast.literal_eval(results.loc[0, 'params'])"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "error",
          "ename": "KeyError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/pandas/core/indexing.py\u001b[0m in \u001b[0;36m_has_valid_type\u001b[0;34m(self, key, axis)\u001b[0m\n\u001b[1;32m   1505\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcontains\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1506\u001b[0;31m                     \u001b[0merror\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1507\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mTypeError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/pandas/core/indexing.py\u001b[0m in \u001b[0;36merror\u001b[0;34m()\u001b[0m\n\u001b[1;32m   1500\u001b[0m                                .format(key=key,\n\u001b[0;32m-> 1501\u001b[0;31m                                        axis=self.obj._get_axis_name(axis)))\n\u001b[0m\u001b[1;32m   1502\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyError\u001b[0m: 'the label [0] is not in the [index]'",
            "\nDuring handling of the above exception, another exception occurred:\n",
            "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-1006-8b1b891e942d>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;31m# Convert from a string to a dictionary\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0mast\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mliteral_eval\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresults\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'params'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/pandas/core/indexing.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   1365\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mKeyError\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mIndexError\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1366\u001b[0m                 \u001b[0;32mpass\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1367\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_tuple\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1368\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1369\u001b[0m             \u001b[0;31m# we by definition only have the 0th axis\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/pandas/core/indexing.py\u001b[0m in \u001b[0;36m_getitem_tuple\u001b[0;34m(self, tup)\u001b[0m\n\u001b[1;32m    856\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_getitem_tuple\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtup\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    857\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 858\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_lowerdim\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtup\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    859\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mIndexingError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    860\u001b[0m             \u001b[0;32mpass\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/pandas/core/indexing.py\u001b[0m in \u001b[0;36m_getitem_lowerdim\u001b[0;34m(self, tup)\u001b[0m\n\u001b[1;32m    989\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m \u001b[0;32min\u001b[0m \u001b[0menumerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtup\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    990\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mis_label_like\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtuple\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 991\u001b[0;31m                 \u001b[0msection\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_axis\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    992\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    993\u001b[0m                 \u001b[0;31m# we have yielded a scalar ?\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/pandas/core/indexing.py\u001b[0m in \u001b[0;36m_getitem_axis\u001b[0;34m(self, key, axis)\u001b[0m\n\u001b[1;32m   1624\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1625\u001b[0m         \u001b[0;31m# fall thru to straight lookup\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1626\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_has_valid_type\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1627\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_label\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1628\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/pandas/core/indexing.py\u001b[0m in \u001b[0;36m_has_valid_type\u001b[0;34m(self, key, axis)\u001b[0m\n\u001b[1;32m   1512\u001b[0m                 \u001b[0;32mraise\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1513\u001b[0m             \u001b[0;32mexcept\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1514\u001b[0;31m                 \u001b[0merror\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1515\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1516\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/pandas/core/indexing.py\u001b[0m in \u001b[0;36merror\u001b[0;34m()\u001b[0m\n\u001b[1;32m   1499\u001b[0m                 raise KeyError(u\"the label [{key}] is not in the [{axis}]\"\n\u001b[1;32m   1500\u001b[0m                                .format(key=key,\n\u001b[0;32m-> 1501\u001b[0;31m                                        axis=self.obj._get_axis_name(axis)))\n\u001b[0m\u001b[1;32m   1502\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1503\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyError\u001b[0m: 'the label [0] is not in the [index]'"
          ]
        }
      ]
    },
    {
      "metadata": {
        "id": "p17nQ4fimC17",
        "colab_type": "code",
        "outputId": "5d204f75-3b9e-417f-df97-296163884b84",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1200
        }
      },
      "cell_type": "code",
      "source": [
        "# Extract the ideal number of estimators and hyperparameters\n",
        "best_bayes_estimators = int(results.loc[0, 'estimators'])\n",
        "best_bayes_params = ast.literal_eval(results.loc[0, 'params']).copy()\n",
        "\n",
        "# Re-create the best model and train on the training data\n",
        "best_bayes_model = lgb.LGBMClassifier(n_estimators=best_bayes_estimators, n_jobs = -1, \n",
        "                                       objective = 'binary', random_state = 50, **best_bayes_params)\n",
        "best_bayes_model.fit(features, labels)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "error",
          "ename": "KeyError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/pandas/core/indexing.py\u001b[0m in \u001b[0;36m_has_valid_type\u001b[0;34m(self, key, axis)\u001b[0m\n\u001b[1;32m   1505\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcontains\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1506\u001b[0;31m                     \u001b[0merror\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1507\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mTypeError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/pandas/core/indexing.py\u001b[0m in \u001b[0;36merror\u001b[0;34m()\u001b[0m\n\u001b[1;32m   1500\u001b[0m                                .format(key=key,\n\u001b[0;32m-> 1501\u001b[0;31m                                        axis=self.obj._get_axis_name(axis)))\n\u001b[0m\u001b[1;32m   1502\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyError\u001b[0m: 'the label [0] is not in the [index]'",
            "\nDuring handling of the above exception, another exception occurred:\n",
            "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-1007-29b71899f324>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mbest_bayes_estimators\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresults\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'estimators'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mbest_bayes_params\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mast\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mliteral_eval\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresults\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'params'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;31m# Re-create the best model and train on the training data\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m best_bayes_model = lgb.LGBMClassifier(n_estimators=best_bayes_estimators, n_jobs = -1, \n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/pandas/core/indexing.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   1365\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mKeyError\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mIndexError\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1366\u001b[0m                 \u001b[0;32mpass\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1367\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_tuple\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1368\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1369\u001b[0m             \u001b[0;31m# we by definition only have the 0th axis\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/pandas/core/indexing.py\u001b[0m in \u001b[0;36m_getitem_tuple\u001b[0;34m(self, tup)\u001b[0m\n\u001b[1;32m    856\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_getitem_tuple\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtup\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    857\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 858\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_lowerdim\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtup\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    859\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mIndexingError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    860\u001b[0m             \u001b[0;32mpass\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/pandas/core/indexing.py\u001b[0m in \u001b[0;36m_getitem_lowerdim\u001b[0;34m(self, tup)\u001b[0m\n\u001b[1;32m    989\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m \u001b[0;32min\u001b[0m \u001b[0menumerate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtup\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    990\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mis_label_like\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtuple\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 991\u001b[0;31m                 \u001b[0msection\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_axis\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    992\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    993\u001b[0m                 \u001b[0;31m# we have yielded a scalar ?\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/pandas/core/indexing.py\u001b[0m in \u001b[0;36m_getitem_axis\u001b[0;34m(self, key, axis)\u001b[0m\n\u001b[1;32m   1624\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1625\u001b[0m         \u001b[0;31m# fall thru to straight lookup\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1626\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_has_valid_type\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1627\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_label\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1628\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/pandas/core/indexing.py\u001b[0m in \u001b[0;36m_has_valid_type\u001b[0;34m(self, key, axis)\u001b[0m\n\u001b[1;32m   1512\u001b[0m                 \u001b[0;32mraise\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1513\u001b[0m             \u001b[0;32mexcept\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1514\u001b[0;31m                 \u001b[0merror\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1515\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1516\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/pandas/core/indexing.py\u001b[0m in \u001b[0;36merror\u001b[0;34m()\u001b[0m\n\u001b[1;32m   1499\u001b[0m                 raise KeyError(u\"the label [{key}] is not in the [{axis}]\"\n\u001b[1;32m   1500\u001b[0m                                .format(key=key,\n\u001b[0;32m-> 1501\u001b[0;31m                                        axis=self.obj._get_axis_name(axis)))\n\u001b[0m\u001b[1;32m   1502\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1503\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyError\u001b[0m: 'the label [0] is not in the [index]'"
          ]
        }
      ]
    },
    {
      "metadata": {
        "id": "r1ruk-2LmEVp",
        "colab_type": "code",
        "outputId": "20a79e41-04fb-4403-889f-388e954fab73",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 197
        }
      },
      "cell_type": "code",
      "source": [
        "# Evaluate on the testing data \n",
        "preds = best_bayes_model.predict_proba(test_features)[:, 1]\n",
        "print('The best model from Bayes optimization scores {:.5f} AUC ROC on the test set.'.format(roc_auc_score(test_labels, preds)))\n",
        "print('This was achieved after {} search iterations'.format(results.loc[0, 'iteration']))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-1008-3f0971263cae>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mpreds\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbest_bayes_model\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict_proba\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtest_features\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'The best model from Bayes optimization scores {:.5f} AUC ROC on the test set.'\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mroc_auc_score\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtest_labels\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpreds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'This was achieved after {} search iterations'\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresults\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'iteration'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mNameError\u001b[0m: name 'best_bayes_model' is not defined"
          ]
        }
      ]
    },
    {
      "metadata": {
        "id": "8uctBicMkBQ2",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Lets Eval the model with Confusion metrix and AUC "
      ]
    },
    {
      "metadata": {
        "id": "_ZxaxEnwjmML",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import confusion_matrix"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "9k8dvXcQkMz7",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "y_pred = model.predict(X_test)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "mWiKZf-SkNCT",
        "colab_type": "code",
        "outputId": "2c7db48f-1ec2-47e5-8e97-08dbafc61b14",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "cell_type": "code",
      "source": [
        "confusion_matrix(y_test,y_pred.round())"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[3629,   15],\n",
              "       [  24,  335]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 718
        }
      ]
    },
    {
      "metadata": {
        "id": "dKp81mGgkOaq",
        "colab_type": "code",
        "outputId": "2f4d6b16-6c92-47cd-e67a-07a6f58a2aee",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import roc_auc_score\n",
        "roc_auc_score(y_test,y_pred.round())"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9543753382520661"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 677
        }
      ]
    },
    {
      "metadata": {
        "id": "8W4omqv0kUNC",
        "colab_type": "code",
        "outputId": "139c7ebe-56b5-4180-c568-fdbd34edbf27",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 170
        }
      },
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import classification_report\n",
        "print(classification_report(y_test,y_pred.round()))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "         0.0       0.99      0.99      0.99      3644\n",
            "         1.0       0.87      0.93      0.90       359\n",
            "\n",
            "   micro avg       0.98      0.98      0.98      4003\n",
            "   macro avg       0.93      0.96      0.95      4003\n",
            "weighted avg       0.98      0.98      0.98      4003\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "lPfp9ljeokuH",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Lets create submission to make sure we are not overfitting"
      ]
    },
    {
      "metadata": {
        "id": "Nlq6aU7DoWVw",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "pred = model.predict(test_df_lgb)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "boGyOK3IosVx",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "test_df['FORECLOSURE'] = pred"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "jYgQXu-aoxnZ",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "sub = test_df[['AGREEMENTID','FORECLOSURE']]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "0Ym5Al0TpHTS",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "sub.to_csv(\"New_Sub_V_16.csv\",index=False)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "WR4VM4XGBQDb",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Lets Analise Feature importance"
      ]
    },
    {
      "metadata": {
        "id": "fiaPsKsMBmu5",
        "colab_type": "code",
        "outputId": "b4cfe191-6929-4d10-a0f2-fda2f6acb9de",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 683
        }
      },
      "cell_type": "code",
      "source": [
        "!pip install shap"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting shap\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/30/b3/866b0101cbd1829844c35964af68c14ba522a5cce7a1e8d0f7937411d910/shap-0.28.5.tar.gz (223kB)\n",
            "\u001b[K    100% |████████████████████████████████| 225kB 9.0MB/s \n",
            "\u001b[?25hRequirement already satisfied: numpy in /usr/local/lib/python3.6/dist-packages (from shap) (1.14.6)\n",
            "Requirement already satisfied: scipy in /usr/local/lib/python3.6/dist-packages (from shap) (1.1.0)\n",
            "Requirement already satisfied: scikit-learn in /usr/local/lib/python3.6/dist-packages (from shap) (0.20.3)\n",
            "Requirement already satisfied: matplotlib in /usr/local/lib/python3.6/dist-packages (from shap) (3.0.3)\n",
            "Requirement already satisfied: pandas in /usr/local/lib/python3.6/dist-packages (from shap) (0.22.0)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.6/dist-packages (from shap) (4.28.1)\n",
            "Requirement already satisfied: ipython in /usr/local/lib/python3.6/dist-packages (from shap) (5.5.0)\n",
            "Requirement already satisfied: scikit-image in /usr/local/lib/python3.6/dist-packages (from shap) (0.13.1)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.6/dist-packages (from matplotlib->shap) (0.10.0)\n",
            "Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib->shap) (2.5.3)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib->shap) (1.0.1)\n",
            "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib->shap) (2.3.1)\n",
            "Requirement already satisfied: pytz>=2011k in /usr/local/lib/python3.6/dist-packages (from pandas->shap) (2018.9)\n",
            "Requirement already satisfied: pickleshare in /usr/local/lib/python3.6/dist-packages (from ipython->shap) (0.7.5)\n",
            "Requirement already satisfied: decorator in /usr/local/lib/python3.6/dist-packages (from ipython->shap) (4.3.2)\n",
            "Requirement already satisfied: setuptools>=18.5 in /usr/local/lib/python3.6/dist-packages (from ipython->shap) (40.8.0)\n",
            "Requirement already satisfied: pexpect; sys_platform != \"win32\" in /usr/local/lib/python3.6/dist-packages (from ipython->shap) (4.6.0)\n",
            "Requirement already satisfied: traitlets>=4.2 in /usr/local/lib/python3.6/dist-packages (from ipython->shap) (4.3.2)\n",
            "Requirement already satisfied: pygments in /usr/local/lib/python3.6/dist-packages (from ipython->shap) (2.1.3)\n",
            "Requirement already satisfied: simplegeneric>0.8 in /usr/local/lib/python3.6/dist-packages (from ipython->shap) (0.8.1)\n",
            "Requirement already satisfied: prompt-toolkit<2.0.0,>=1.0.4 in /usr/local/lib/python3.6/dist-packages (from ipython->shap) (1.0.15)\n",
            "Requirement already satisfied: networkx>=1.8 in /usr/local/lib/python3.6/dist-packages (from scikit-image->shap) (2.2)\n",
            "Requirement already satisfied: pillow>=2.1.0 in /usr/local/lib/python3.6/dist-packages (from scikit-image->shap) (4.1.1)\n",
            "Requirement already satisfied: six>=1.7.3 in /usr/local/lib/python3.6/dist-packages (from scikit-image->shap) (1.11.0)\n",
            "Requirement already satisfied: PyWavelets>=0.4.0 in /usr/local/lib/python3.6/dist-packages (from scikit-image->shap) (1.0.2)\n",
            "Requirement already satisfied: ptyprocess>=0.5 in /usr/local/lib/python3.6/dist-packages (from pexpect; sys_platform != \"win32\"->ipython->shap) (0.6.0)\n",
            "Requirement already satisfied: ipython-genutils in /usr/local/lib/python3.6/dist-packages (from traitlets>=4.2->ipython->shap) (0.2.0)\n",
            "Requirement already satisfied: wcwidth in /usr/local/lib/python3.6/dist-packages (from prompt-toolkit<2.0.0,>=1.0.4->ipython->shap) (0.1.7)\n",
            "Requirement already satisfied: olefile in /usr/local/lib/python3.6/dist-packages (from pillow>=2.1.0->scikit-image->shap) (0.46)\n",
            "Building wheels for collected packages: shap\n",
            "  Building wheel for shap (setup.py) ... \u001b[?25ldone\n",
            "\u001b[?25h  Stored in directory: /root/.cache/pip/wheels/bf/26/bd/912db1314f1cef0171d9b7f128dd01e8b8c92ed8d0062e632d\n",
            "Successfully built shap\n",
            "Installing collected packages: shap\n",
            "Successfully installed shap-0.28.5\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "PJZmOgRjBTIF",
        "colab_type": "code",
        "outputId": "f8766580-65ef-427f-eb5e-d787a0053b70",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 43
        }
      },
      "cell_type": "code",
      "source": [
        "import shap\n",
        "# load JS visualization code to notebook\n",
        "shap.initjs()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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r={topAbort:null,topAnimationEnd:null,topAnimationIteration:null,topAnimationStart:null,topBlur:null,topCanPlay:null,topCanPlayThrough:null,topChange:null,topClick:null,topCompositionEnd:null,topCompositionStart:null,topCompositionUpdate:null,topContextMenu:null,topCopy:null,topCut:null,topDoubleClick:null,topDrag:null,topDragEnd:null,topDragEnter:null,topDragExit:null,topDragLeave:null,topDragOver:null,topDragStart:null,topDrop:null,topDurationChange:null,topEmptied:null,topEncrypted:null,topEnded:null,topError:null,topFocus:null,topInput:null,topInvalid:null,topKeyDown:null,topKeyPress:null,topKeyUp:null,topLoad:null,topLoadedData:null,topLoadedMetadata:null,topLoadStart:null,topMouseDown:null,topMouseMove:null,topMouseOut:null,topMouseOver:null,topMouseUp:null,topPaste:null,topPause:null,topPlay:null,topPlaying:null,topProgress:null,topRateChange:null,topReset:null,topScroll:null,topSeeked:null,topSeeking:null,topSelectionChange:null,topStalled:null,topSubmit:null,topSuspend:null,topTextInput:null,topTimeUpdate:null,topTouchCancel:null,topTouchEnd:null,topTouchMove:null,topTouchStart:null,topTransitionEnd:null,topVolumeChange:null,topWaiting:null,topWheel:null},i={topLevelTypes:r};t.exports=i},function(t,e,n){\"use 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RegExp(\"^(data|aria)-[\"+r.ATTRIBUTE_NAME_CHAR+\"]*$\")),Properties:{accept:0,acceptCharset:0,accessKey:0,action:0,allowFullScreen:o,allowTransparency:0,alt:0,as:0,async:o,autoComplete:0,autoPlay:o,capture:o,cellPadding:0,cellSpacing:0,charSet:0,challenge:0,checked:i|o,cite:0,classID:0,className:0,cols:u,colSpan:0,content:0,contentEditable:0,contextMenu:0,controls:o,controlsList:0,coords:0,crossOrigin:0,data:0,dateTime:0,default:o,defer:o,dir:0,disabled:o,download:c,draggable:0,encType:0,form:0,formAction:0,formEncType:0,formMethod:0,formNoValidate:o,formTarget:0,frameBorder:0,headers:0,height:0,hidden:o,high:0,href:0,hrefLang:0,htmlFor:0,httpEquiv:0,icon:0,id:0,inputMode:0,integrity:0,is:0,keyParams:0,keyType:0,kind:0,label:0,lang:0,list:0,loop:o,low:0,manifest:0,marginHeight:0,marginWidth:0,max:0,maxLength:0,media:0,mediaGroup:0,method:0,min:0,minLength:0,multiple:i|o,muted:i|o,name:0,nonce:0,noValidate:o,open:o,optimum:0,pattern:0,placeholder:0,playsInline:o,poster:0,preload:0,profile:0,radioGroup:0,readOnly:o,referrerPolicy:0,rel:0,required:o,reversed:o,role:0,rows:u,rowSpan:a,sandbox:0,scope:0,scoped:o,scrolling:0,seamless:o,selected:i|o,shape:0,size:u,sizes:0,span:u,spellCheck:0,src:0,srcDoc:0,srcLang:0,srcSet:0,start:a,step:0,style:0,summary:0,tabIndex:0,target:0,title:0,type:0,useMap:0,value:0,width:0,wmode:0,wrap:0,about:0,datatype:0,inlist:0,prefix:0,property:0,resource:0,typeof:0,vocab:0,autoCapitalize:0,autoCorrect:0,autoSave:0,color:0,itemProp:0,itemScope:o,itemType:0,itemID:0,itemRef:0,results:0,security:0,unselectable:0},DOMAttributeNames:{acceptCharset:\"accept-charset\",className:\"class\",htmlFor:\"for\",httpEquiv:\"http-equiv\"},DOMPropertyNames:{},DOMMutationMethods:{value:function(t,e){if(null==e)return t.removeAttribute(\"value\");\"number\"!==t.type||!1===t.hasAttribute(\"value\")?t.setAttribute(\"value\",\"\"+e):t.validity&&!t.validity.badInput&&t.ownerDocument.activeElement!==t&&t.setAttribute(\"value\",\"\"+e)}}};t.exports=s},function(t,e,n){\"use strict\";(function(e){function r(t,e,n,r){var i=void 0===t[n];null!=e&&i&&(t[n]=o(e,!0))}var i=n(24),o=n(174),a=(n(85),n(96)),u=n(177);n(2);void 0!==e&&e.env;var c={instantiateChildren:function(t,e,n,i){if(null==t)return null;var o={};return u(t,r,o),o},updateChildren:function(t,e,n,r,u,c,s,l,f){if(e||t){var p,h;for(p in e)if(e.hasOwnProperty(p)){h=t&&t[p];var d=h&&h._currentElement,v=e[p];if(null!=h&&a(d,v))i.receiveComponent(h,v,u,l),e[p]=h;else{h&&(r[p]=i.getHostNode(h),i.unmountComponent(h,!1));var g=o(v,!0);e[p]=g;var m=i.mountComponent(g,u,c,s,l,f);n.push(m)}}for(p in t)!t.hasOwnProperty(p)||e&&e.hasOwnProperty(p)||(h=t[p],r[p]=i.getHostNode(h),i.unmountComponent(h,!1))}},unmountChildren:function(t,e){for(var n in t)if(t.hasOwnProperty(n)){var r=t[n];i.unmountComponent(r,e)}}};t.exports=c}).call(e,n(156))},function(t,e,n){\"use strict\";var r=n(82),i=n(364),o={processChildrenUpdates:i.dangerouslyProcessChildrenUpdates,replaceNodeWithMarkup:r.dangerouslyReplaceNodeWithMarkup};t.exports=o},function(t,e,n){\"use strict\";function r(t){}function i(t){return!(!t.prototype||!t.prototype.isReactComponent)}function o(t){return!(!t.prototype||!t.prototype.isPureReactComponent)}var a=n(1),u=n(3),c=n(26),s=n(87),l=n(15),f=n(88),p=n(39),h=(n(9),n(168)),d=n(24),v=n(51),g=(n(0),n(81)),m=n(96),y=(n(2),{ImpureClass:0,PureClass:1,StatelessFunctional:2});r.prototype.render=function(){var t=p.get(this)._currentElement.type,e=t(this.props,this.context,this.updater);return e};var _=1,b={construct:function(t){this._currentElement=t,this._rootNodeID=0,this._compositeType=null,this._instance=null,this._hostParent=null,this._hostContainerInfo=null,this._updateBatchNumber=null,this._pendingElement=null,this._pendingStateQueue=null,this._pendingReplaceState=!1,this._pendingForceUpdate=!1,this._renderedNodeType=null,this._renderedComponent=null,this._context=null,this._mountOrder=0,this._topLevelWrapper=null,this._pendingCallbacks=null,this._calledComponentWillUnmount=!1},mountComponent:function(t,e,n,u){this._context=u,this._mountOrder=_++,this._hostParent=e,this._hostContainerInfo=n;var s,l=this._currentElement.props,f=this._processContext(u),h=this._currentElement.type,d=t.getUpdateQueue(),g=i(h),m=this._constructComponent(g,l,f,d);g||null!=m&&null!=m.render?o(h)?this._compositeType=y.PureClass:this._compositeType=y.ImpureClass:(s=m,null===m||!1===m||c.isValidElement(m)||a(\"105\",h.displayName||h.name||\"Component\"),m=new r(h),this._compositeType=y.StatelessFunctional);m.props=l,m.context=f,m.refs=v,m.updater=d,this._instance=m,p.set(m,this);var b=m.state;void 0===b&&(m.state=b=null),(\"object\"!=typeof b||Array.isArray(b))&&a(\"106\",this.getName()||\"ReactCompositeComponent\"),this._pendingStateQueue=null,this._pendingReplaceState=!1,this._pendingForceUpdate=!1;var x;return x=m.unstable_handleError?this.performInitialMountWithErrorHandling(s,e,n,t,u):this.performInitialMount(s,e,n,t,u),m.componentDidMount&&t.getReactMountReady().enqueue(m.componentDidMount,m),x},_constructComponent:function(t,e,n,r){return this._constructComponentWithoutOwner(t,e,n,r)},_constructComponentWithoutOwner:function(t,e,n,r){var i=this._currentElement.type;return t?new i(e,n,r):i(e,n,r)},performInitialMountWithErrorHandling:function(t,e,n,r,i){var o,a=r.checkpoint();try{o=this.performInitialMount(t,e,n,r,i)}catch(u){r.rollback(a),this._instance.unstable_handleError(u),this._pendingStateQueue&&(this._instance.state=this._processPendingState(this._instance.props,this._instance.context)),a=r.checkpoint(),this._renderedComponent.unmountComponent(!0),r.rollback(a),o=this.performInitialMount(t,e,n,r,i)}return o},performInitialMount:function(t,e,n,r,i){var o=this._instance,a=0;o.componentWillMount&&(o.componentWillMount(),this._pendingStateQueue&&(o.state=this._processPendingState(o.props,o.context))),void 0===t&&(t=this._renderValidatedComponent());var u=h.getType(t);this._renderedNodeType=u;var c=this._instantiateReactComponent(t,u!==h.EMPTY);this._renderedComponent=c;var s=d.mountComponent(c,r,e,n,this._processChildContext(i),a);return s},getHostNode:function(){return d.getHostNode(this._renderedComponent)},unmountComponent:function(t){if(this._renderedComponent){var e=this._instance;if(e.componentWillUnmount&&!e._calledComponentWillUnmount)if(e._calledComponentWillUnmount=!0,t){var n=this.getName()+\".componentWillUnmount()\";f.invokeGuardedCallback(n,e.componentWillUnmount.bind(e))}else e.componentWillUnmount();this._renderedComponent&&(d.unmountComponent(this._renderedComponent,t),this._renderedNodeType=null,this._renderedComponent=null,this._instance=null),this._pendingStateQueue=null,this._pendingReplaceState=!1,this._pendingForceUpdate=!1,this._pendingCallbacks=null,this._pendingElement=null,this._context=null,this._rootNodeID=0,this._topLevelWrapper=null,p.remove(e)}},_maskContext:function(t){var e=this._currentElement.type,n=e.contextTypes;if(!n)return v;var r={};for(var i in n)r[i]=t[i];return r},_processContext:function(t){var e=this._maskContext(t);return e},_processChildContext:function(t){var e,n=this._currentElement.type,r=this._instance;if(r.getChildContext&&(e=r.getChildContext()),e){\"object\"!=typeof n.childContextTypes&&a(\"107\",this.getName()||\"ReactCompositeComponent\");for(var i in e)i in n.childContextTypes||a(\"108\",this.getName()||\"ReactCompositeComponent\",i);return u({},t,e)}return t},_checkContextTypes:function(t,e,n){},receiveComponent:function(t,e,n){var r=this._currentElement,i=this._context;this._pendingElement=null,this.updateComponent(e,r,t,i,n)},performUpdateIfNecessary:function(t){null!=this._pendingElement?d.receiveComponent(this,this._pendingElement,t,this._context):null!==this._pendingStateQueue||this._pendingForceUpdate?this.updateComponent(t,this._currentElement,this._currentElement,this._context,this._context):this._updateBatchNumber=null},updateComponent:function(t,e,n,r,i){var o=this._instance;null==o&&a(\"136\",this.getName()||\"ReactCompositeComponent\");var u,c=!1;this._context===i?u=o.context:(u=this._processContext(i),c=!0);var s=e.props,l=n.props;e!==n&&(c=!0),c&&o.componentWillReceiveProps&&o.componentWillReceiveProps(l,u);var f=this._processPendingState(l,u),p=!0;this._pendingForceUpdate||(o.shouldComponentUpdate?p=o.shouldComponentUpdate(l,f,u):this._compositeType===y.PureClass&&(p=!g(s,l)||!g(o.state,f))),this._updateBatchNumber=null,p?(this._pendingForceUpdate=!1,this._performComponentUpdate(n,l,f,u,t,i)):(this._currentElement=n,this._context=i,o.props=l,o.state=f,o.context=u)},_processPendingState:function(t,e){var n=this._instance,r=this._pendingStateQueue,i=this._pendingReplaceState;if(this._pendingReplaceState=!1,this._pendingStateQueue=null,!r)return n.state;if(i&&1===r.length)return r[0];for(var o=u({},i?r[0]:n.state),a=i?1:0;a<r.length;a++){var c=r[a];u(o,\"function\"==typeof c?c.call(n,o,t,e):c)}return o},_performComponentUpdate:function(t,e,n,r,i,o){var a,u,c,s=this._instance,l=Boolean(s.componentDidUpdate);l&&(a=s.props,u=s.state,c=s.context),s.componentWillUpdate&&s.componentWillUpdate(e,n,r),this._currentElement=t,this._context=o,s.props=e,s.state=n,s.context=r,this._updateRenderedComponent(i,o),l&&i.getReactMountReady().enqueue(s.componentDidUpdate.bind(s,a,u,c),s)},_updateRenderedComponent:function(t,e){var n=this._renderedComponent,r=n._currentElement,i=this._renderValidatedComponent(),o=0;if(m(r,i))d.receiveComponent(n,i,t,this._processChildContext(e));else{var a=d.getHostNode(n);d.unmountComponent(n,!1);var u=h.getType(i);this._renderedNodeType=u;var c=this._instantiateReactComponent(i,u!==h.EMPTY);this._renderedComponent=c;var s=d.mountComponent(c,t,this._hostParent,this._hostContainerInfo,this._processChildContext(e),o);this._replaceNodeWithMarkup(a,s,n)}},_replaceNodeWithMarkup:function(t,e,n){s.replaceNodeWithMarkup(t,e,n)},_renderValidatedComponentWithoutOwnerOrContext:function(){var t=this._instance;return t.render()},_renderValidatedComponent:function(){var t;if(this._compositeType!==y.StatelessFunctional){l.current=this;try{t=this._renderValidatedComponentWithoutOwnerOrContext()}finally{l.current=null}}else t=this._renderValidatedComponentWithoutOwnerOrContext();return null===t||!1===t||c.isValidElement(t)||a(\"109\",this.getName()||\"ReactCompositeComponent\"),t},attachRef:function(t,e){var n=this.getPublicInstance();null==n&&a(\"110\");var r=e.getPublicInstance();(n.refs===v?n.refs={}:n.refs)[t]=r},detachRef:function(t){delete this.getPublicInstance().refs[t]},getName:function(){var t=this._currentElement.type,e=this._instance&&this._instance.constructor;return t.displayName||e&&e.displayName||t.name||e&&e.name||null},getPublicInstance:function(){var t=this._instance;return this._compositeType===y.StatelessFunctional?null:t},_instantiateReactComponent:null};t.exports=b},function(t,e,n){\"use strict\";var r=n(4),i=n(372),o=n(167),a=n(24),u=n(12),c=n(385),s=n(401),l=n(171),f=n(408);n(2);i.inject();var p={findDOMNode:s,render:o.render,unmountComponentAtNode:o.unmountComponentAtNode,version:c,unstable_batchedUpdates:u.batchedUpdates,unstable_renderSubtreeIntoContainer:f};\"undefined\"!=typeof __REACT_DEVTOOLS_GLOBAL_HOOK__&&\"function\"==typeof __REACT_DEVTOOLS_GLOBAL_HOOK__.inject&&__REACT_DEVTOOLS_GLOBAL_HOOK__.inject({ComponentTree:{getClosestInstanceFromNode:r.getClosestInstanceFromNode,getNodeFromInstance:function(t){return t._renderedComponent&&(t=l(t)),t?r.getNodeFromInstance(t):null}},Mount:o,Reconciler:a});t.exports=p},function(t,e,n){\"use strict\";function r(t){if(t){var e=t._currentElement._owner||null;if(e){var n=e.getName();if(n)return\" This DOM node was rendered by `\"+n+\"`.\"}}return\"\"}function i(t,e){e&&($[t._tag]&&(null!=e.children||null!=e.dangerouslySetInnerHTML)&&g(\"137\",t._tag,t._currentElement._owner?\" Check the render method of 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Y)Y.hasOwnProperty(n)&&t._wrapperState.listeners.push(M.trapBubbledEvent(n,Y[n],e));break;case\"source\":t._wrapperState.listeners=[M.trapBubbledEvent(\"topError\",\"error\",e)];break;case\"img\":t._wrapperState.listeners=[M.trapBubbledEvent(\"topError\",\"error\",e),M.trapBubbledEvent(\"topLoad\",\"load\",e)];break;case\"form\":t._wrapperState.listeners=[M.trapBubbledEvent(\"topReset\",\"reset\",e),M.trapBubbledEvent(\"topSubmit\",\"submit\",e)];break;case\"input\":case\"select\":case\"textarea\":t._wrapperState.listeners=[M.trapBubbledEvent(\"topInvalid\",\"invalid\",e)]}}function p(){P.postUpdateWrapper(this)}function h(t){Z.call(Q,t)||(X.test(t)||g(\"65\",t),Q[t]=!0)}function d(t,e){return t.indexOf(\"-\")>=0||null!=e.is}function v(t){var e=t.type;h(e),this._currentElement=t,this._tag=e.toLowerCase(),this._namespaceURI=null,this._renderedChildren=null,this._previousStyle=null,this._previousStyleCopy=null,this._hostNode=null,this._hostParent=null,this._rootNodeID=0,this._domID=0,this._hostContainerInfo=null,this._wrapperState=null,this._topLevelWrapper=null,this._flags=0}var g=n(1),m=n(3),y=n(346),_=n(348),b=n(20),x=n(83),w=n(21),C=n(160),k=n(22),E=n(84),M=n(53),T=n(161),S=n(4),N=n(365),A=n(366),P=n(162),O=n(369),I=(n(9),n(378)),D=n(383),R=(n(11),n(56)),L=(n(0),n(95),n(81),n(173)),U=(n(97),n(2),T),F=k.deleteListener,j=S.getNodeFromInstance,B=M.listenTo,V=E.registrationNameModules,W={string:!0,number:!0},z=\"__html\",H={children:null,dangerouslySetInnerHTML:null,suppressContentEditableWarning:null},q=11,Y={topAbort:\"abort\",topCanPlay:\"canplay\",topCanPlayThrough:\"canplaythrough\",topDurationChange:\"durationchange\",topEmptied:\"emptied\",topEncrypted:\"encrypted\",topEnded:\"ended\",topError:\"error\",topLoadedData:\"loadeddata\",topLoadedMetadata:\"loadedmetadata\",topLoadStart:\"loadstart\",topPause:\"pause\",topPlay:\"play\",topPlaying:\"playing\",topProgress:\"progress\",topRateChange:\"ratechange\",topSeeked:\"seeked\",topSeeking:\"seeking\",topStalled:\"stalled\",topSuspend:\"suspend\",topTimeUpdate:\"timeupdate\",topVolumeChange:\"volumechange\",topWaiting:\"waiting\"},K={area:!0,base:!0,br:!0,col:!0,embed:!0,hr:!0,img:!0,input:!0,keygen:!0,link:!0,meta:!0,param:!0,source:!0,track:!0,wbr:!0},G={listing:!0,pre:!0,textarea:!0},$=m({menuitem:!0},K),X=/^[a-zA-Z][a-zA-Z:_\\.\\-\\d]*$/,Q={},Z={}.hasOwnProperty,J=1;v.displayName=\"ReactDOMComponent\",v.Mixin={mountComponent:function(t,e,n,r){this._rootNodeID=J++,this._domID=n._idCounter++,this._hostParent=e,this._hostContainerInfo=n;var o=this._currentElement.props;switch(this._tag){case\"audio\":case\"form\":case\"iframe\":case\"img\":case\"link\":case\"object\":case\"source\":case\"video\":this._wrapperState={listeners:null},t.getReactMountReady().enqueue(f,this);break;case\"input\":N.mountWrapper(this,o,e),o=N.getHostProps(this,o),t.getReactMountReady().enqueue(l,this),t.getReactMountReady().enqueue(f,this);break;case\"option\":A.mountWrapper(this,o,e),o=A.getHostProps(this,o);break;case\"select\":P.mountWrapper(this,o,e),o=P.getHostProps(this,o),t.getReactMountReady().enqueue(f,this);break;case\"textarea\":O.mountWrapper(this,o,e),o=O.getHostProps(this,o),t.getReactMountReady().enqueue(l,this),t.getReactMountReady().enqueue(f,this)}i(this,o);var a,p;null!=e?(a=e._namespaceURI,p=e._tag):n._tag&&(a=n._namespaceURI,p=n._tag),(null==a||a===x.svg&&\"foreignobject\"===p)&&(a=x.html),a===x.html&&(\"svg\"===this._tag?a=x.svg:\"math\"===this._tag&&(a=x.mathml)),this._namespaceURI=a;var h;if(t.useCreateElement){var d,v=n._ownerDocument;if(a===x.html)if(\"script\"===this._tag){var g=v.createElement(\"div\"),m=this._currentElement.type;g.innerHTML=\"<\"+m+\"></\"+m+\">\",d=g.removeChild(g.firstChild)}else d=o.is?v.createElement(this._currentElement.type,o.is):v.createElement(this._currentElement.type);else d=v.createElementNS(a,this._currentElement.type);S.precacheNode(this,d),this._flags|=U.hasCachedChildNodes,this._hostParent||C.setAttributeForRoot(d),this._updateDOMProperties(null,o,t);var _=b(d);this._createInitialChildren(t,o,r,_),h=_}else{var w=this._createOpenTagMarkupAndPutListeners(t,o),k=this._createContentMarkup(t,o,r);h=!k&&K[this._tag]?w+\"/>\":w+\">\"+k+\"</\"+this._currentElement.type+\">\"}switch(this._tag){case\"input\":t.getReactMountReady().enqueue(u,this),o.autoFocus&&t.getReactMountReady().enqueue(y.focusDOMComponent,this);break;case\"textarea\":t.getReactMountReady().enqueue(c,this),o.autoFocus&&t.getReactMountReady().enqueue(y.focusDOMComponent,this);break;case\"select\":case\"button\":o.autoFocus&&t.getReactMountReady().enqueue(y.focusDOMComponent,this);break;case\"option\":t.getReactMountReady().enqueue(s,this)}return h},_createOpenTagMarkupAndPutListeners:function(t,e){var n=\"<\"+this._currentElement.type;for(var r in e)if(e.hasOwnProperty(r)){var i=e[r];if(null!=i)if(V.hasOwnProperty(r))i&&o(this,r,i,t);else{\"style\"===r&&(i&&(i=this._previousStyleCopy=m({},e.style)),i=_.createMarkupForStyles(i,this));var a=null;null!=this._tag&&d(this._tag,e)?H.hasOwnProperty(r)||(a=C.createMarkupForCustomAttribute(r,i)):a=C.createMarkupForProperty(r,i),a&&(n+=\" \"+a)}}return t.renderToStaticMarkup?n:(this._hostParent||(n+=\" \"+C.createMarkupForRoot()),n+=\" \"+C.createMarkupForID(this._domID))},_createContentMarkup:function(t,e,n){var r=\"\",i=e.dangerouslySetInnerHTML;if(null!=i)null!=i.__html&&(r=i.__html);else{var o=W[typeof e.children]?e.children:null,a=null!=o?null:e.children;if(null!=o)r=R(o);else if(null!=a){var u=this.mountChildren(a,t,n);r=u.join(\"\")}}return G[this._tag]&&\"\\n\"===r.charAt(0)?\"\\n\"+r:r},_createInitialChildren:function(t,e,n,r){var i=e.dangerouslySetInnerHTML;if(null!=i)null!=i.__html&&b.queueHTML(r,i.__html);else{var o=W[typeof e.children]?e.children:null,a=null!=o?null:e.children;if(null!=o)\"\"!==o&&b.queueText(r,o);else if(null!=a)for(var u=this.mountChildren(a,t,n),c=0;c<u.length;c++)b.queueChild(r,u[c])}},receiveComponent:function(t,e,n){var r=this._currentElement;this._currentElement=t,this.updateComponent(e,r,t,n)},updateComponent:function(t,e,n,r){var o=e.props,a=this._currentElement.props;switch(this._tag){case\"input\":o=N.getHostProps(this,o),a=N.getHostProps(this,a);break;case\"option\":o=A.getHostProps(this,o),a=A.getHostProps(this,a);break;case\"select\":o=P.getHostProps(this,o),a=P.getHostProps(this,a);break;case\"textarea\":o=O.getHostProps(this,o),a=O.getHostProps(this,a)}switch(i(this,a),this._updateDOMProperties(o,a,t),this._updateDOMChildren(o,a,t,r),this._tag){case\"input\":N.updateWrapper(this),L.updateValueIfChanged(this);break;case\"textarea\":O.updateWrapper(this);break;case\"select\":t.getReactMountReady().enqueue(p,this)}},_updateDOMProperties:function(t,e,n){var r,i,a;for(r in t)if(!e.hasOwnProperty(r)&&t.hasOwnProperty(r)&&null!=t[r])if(\"style\"===r){var u=this._previousStyleCopy;for(i in u)u.hasOwnProperty(i)&&(a=a||{},a[i]=\"\");this._previousStyleCopy=null}else V.hasOwnProperty(r)?t[r]&&F(this,r):d(this._tag,t)?H.hasOwnProperty(r)||C.deleteValueForAttribute(j(this),r):(w.properties[r]||w.isCustomAttribute(r))&&C.deleteValueForProperty(j(this),r);for(r in e){var c=e[r],s=\"style\"===r?this._previousStyleCopy:null!=t?t[r]:void 0;if(e.hasOwnProperty(r)&&c!==s&&(null!=c||null!=s))if(\"style\"===r)if(c?c=this._previousStyleCopy=m({},c):this._previousStyleCopy=null,s){for(i in s)!s.hasOwnProperty(i)||c&&c.hasOwnProperty(i)||(a=a||{},a[i]=\"\");for(i in c)c.hasOwnProperty(i)&&s[i]!==c[i]&&(a=a||{},a[i]=c[i])}else a=c;else if(V.hasOwnProperty(r))c?o(this,r,c,n):s&&F(this,r);else if(d(this._tag,e))H.hasOwnProperty(r)||C.setValueForAttribute(j(this),r,c);else if(w.properties[r]||w.isCustomAttribute(r)){var l=j(this);null!=c?C.setValueForProperty(l,r,c):C.deleteValueForProperty(l,r)}}a&&_.setValueForStyles(j(this),a,this)},_updateDOMChildren:function(t,e,n,r){var i=W[typeof t.children]?t.children:null,o=W[typeof e.children]?e.children:null,a=t.dangerouslySetInnerHTML&&t.dangerouslySetInnerHTML.__html,u=e.dangerouslySetInnerHTML&&e.dangerouslySetInnerHTML.__html,c=null!=i?null:t.children,s=null!=o?null:e.children,l=null!=i||null!=a,f=null!=o||null!=u;null!=c&&null==s?this.updateChildren(null,n,r):l&&!f&&this.updateTextContent(\"\"),null!=o?i!==o&&this.updateTextContent(\"\"+o):null!=u?a!==u&&this.updateMarkup(\"\"+u):null!=s&&this.updateChildren(s,n,r)},getHostNode:function(){return j(this)},unmountComponent:function(t){switch(this._tag){case\"audio\":case\"form\":case\"iframe\":case\"img\":case\"link\":case\"object\":case\"source\":case\"video\":var e=this._wrapperState.listeners;if(e)for(var n=0;n<e.length;n++)e[n].remove();break;case\"input\":case\"textarea\":L.stopTracking(this);break;case\"html\":case\"head\":case\"body\":g(\"66\",this._tag)}this.unmountChildren(t),S.uncacheNode(this),k.deleteAllListeners(this),this._rootNodeID=0,this._domID=0,this._wrapperState=null},getPublicInstance:function(){return j(this)}},m(v.prototype,v.Mixin,I.Mixin),t.exports=v},function(t,e,n){\"use strict\";function r(t,e){var n={_topLevelWrapper:t,_idCounter:1,_ownerDocument:e?e.nodeType===i?e:e.ownerDocument:null,_node:e,_tag:e?e.nodeName.toLowerCase():null,_namespaceURI:e?e.namespaceURI:null};return n}var i=(n(97),9);t.exports=r},function(t,e,n){\"use strict\";var r=n(3),i=n(20),o=n(4),a=function(t){this._currentElement=null,this._hostNode=null,this._hostParent=null,this._hostContainerInfo=null,this._domID=0};r(a.prototype,{mountComponent:function(t,e,n,r){var a=n._idCounter++;this._domID=a,this._hostParent=e,this._hostContainerInfo=n;var u=\" react-empty: \"+this._domID+\" 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c=u.querySelectorAll(\"input[name=\"+JSON.stringify(\"\"+i)+'][type=\"radio\"]'),p=0;p<c.length;p++){var h=c[p];if(h!==o&&h.form===o.form){var d=l.getInstanceFromNode(h);d||a(\"90\"),f.asap(r,d)}}}return n}var a=n(1),u=n(3),c=n(160),s=n(86),l=n(4),f=n(12),p=(n(0),n(2),{getHostProps:function(t,e){var n=s.getValue(e),r=s.getChecked(e);return u({type:void 0,step:void 0,min:void 0,max:void 0},e,{defaultChecked:void 0,defaultValue:void 0,value:null!=n?n:t._wrapperState.initialValue,checked:null!=r?r:t._wrapperState.initialChecked,onChange:t._wrapperState.onChange})},mountWrapper:function(t,e){var n=e.defaultValue;t._wrapperState={initialChecked:null!=e.checked?e.checked:e.defaultChecked,initialValue:null!=e.value?e.value:n,listeners:null,onChange:o.bind(t),controlled:i(e)}},updateWrapper:function(t){var e=t._currentElement.props,n=e.checked;null!=n&&c.setValueForProperty(l.getNodeFromInstance(t),\"checked\",n||!1);var r=l.getNodeFromInstance(t),i=s.getValue(e);if(null!=i)if(0===i&&\"\"===r.value)r.value=\"0\";else if(\"number\"===e.type){var o=parseFloat(r.value,10)||0;(i!=o||i==o&&r.value!=i)&&(r.value=\"\"+i)}else r.value!==\"\"+i&&(r.value=\"\"+i);else null==e.value&&null!=e.defaultValue&&r.defaultValue!==\"\"+e.defaultValue&&(r.defaultValue=\"\"+e.defaultValue),null==e.checked&&null!=e.defaultChecked&&(r.defaultChecked=!!e.defaultChecked)},postMountWrapper:function(t){var e=t._currentElement.props,n=l.getNodeFromInstance(t);switch(e.type){case\"submit\":case\"reset\":break;case\"color\":case\"date\":case\"datetime\":case\"datetime-local\":case\"month\":case\"time\":case\"week\":n.value=\"\",n.value=n.defaultValue;break;default:n.value=n.value}var r=n.name;\"\"!==r&&(n.name=\"\"),n.defaultChecked=!n.defaultChecked,n.defaultChecked=!n.defaultChecked,\"\"!==r&&(n.name=r)}});t.exports=p},function(t,e,n){\"use strict\";function r(t){var e=\"\";return o.Children.forEach(t,function(t){null!=t&&(\"string\"==typeof t||\"number\"==typeof t?e+=t:c||(c=!0))}),e}var i=n(3),o=n(26),a=n(4),u=n(162),c=(n(2),!1),s={mountWrapper:function(t,e,n){var i=null;if(null!=n){var o=n;\"optgroup\"===o._tag&&(o=o._hostParent),null!=o&&\"select\"===o._tag&&(i=u.getSelectValueContext(o))}var a=null;if(null!=i){var c;if(c=null!=e.value?e.value+\"\":r(e.children),a=!1,Array.isArray(i)){for(var s=0;s<i.length;s++)if(\"\"+i[s]===c){a=!0;break}}else a=\"\"+i===c}t._wrapperState={selected:a}},postMountWrapper:function(t){var e=t._currentElement.props;if(null!=e.value){a.getNodeFromInstance(t).setAttribute(\"value\",e.value)}},getHostProps:function(t,e){var n=i({selected:void 0,children:void 0},e);null!=t._wrapperState.selected&&(n.selected=t._wrapperState.selected);var o=r(e.children);return o&&(n.children=o),n}};t.exports=s},function(t,e,n){\"use strict\";function r(t,e,n,r){return t===n&&e===r}function i(t){var e=document.selection,n=e.createRange(),r=n.text.length,i=n.duplicate();i.moveToElementText(t),i.setEndPoint(\"EndToStart\",n);var o=i.text.length;return{start:o,end:o+r}}function o(t){var e=window.getSelection&&window.getSelection();if(!e||0===e.rangeCount)return null;var n=e.anchorNode,i=e.anchorOffset,o=e.focusNode,a=e.focusOffset,u=e.getRangeAt(0);try{u.startContainer.nodeType,u.endContainer.nodeType}catch(t){return null}var c=r(e.anchorNode,e.anchorOffset,e.focusNode,e.focusOffset),s=c?0:u.toString().length,l=u.cloneRange();l.selectNodeContents(t),l.setEnd(u.startContainer,u.startOffset);var f=r(l.startContainer,l.startOffset,l.endContainer,l.endOffset),p=f?0:l.toString().length,h=p+s,d=document.createRange();d.setStart(n,i),d.setEnd(o,a);var v=d.collapsed;return{start:v?h:p,end:v?p:h}}function a(t,e){var n,r,i=document.selection.createRange().duplicate();void 0===e.end?(n=e.start,r=n):e.start>e.end?(n=e.end,r=e.start):(n=e.start,r=e.end),i.moveToElementText(t),i.moveStart(\"character\",n),i.setEndPoint(\"EndToStart\",i),i.moveEnd(\"character\",r-n),i.select()}function u(t,e){if(window.getSelection){var n=window.getSelection(),r=t[l()].length,i=Math.min(e.start,r),o=void 0===e.end?i:Math.min(e.end,r);if(!n.extend&&i>o){var a=o;o=i,i=a}var u=s(t,i),c=s(t,o);if(u&&c){var f=document.createRange();f.setStart(u.node,u.offset),n.removeAllRanges(),i>o?(n.addRange(f),n.extend(c.node,c.offset)):(f.setEnd(c.node,c.offset),n.addRange(f))}}}var c=n(6),s=n(405),l=n(172),f=c.canUseDOM&&\"selection\"in document&&!(\"getSelection\"in window),p={getOffsets:f?i:o,setOffsets:f?a:u};t.exports=p},function(t,e,n){\"use strict\";var r=n(1),i=n(3),o=n(82),a=n(20),u=n(4),c=n(56),s=(n(0),n(97),function(t){this._currentElement=t,this._stringText=\"\"+t,this._hostNode=null,this._hostParent=null,this._domID=0,this._mountIndex=0,this._closingComment=null,this._commentNodes=null});i(s.prototype,{mountComponent:function(t,e,n,r){var i=n._idCounter++,o=\" react-text: \"+i+\" \";if(this._domID=i,this._hostParent=e,t.useCreateElement){var s=n._ownerDocument,l=s.createComment(o),f=s.createComment(\" /react-text \"),p=a(s.createDocumentFragment());return a.queueChild(p,a(l)),this._stringText&&a.queueChild(p,a(s.createTextNode(this._stringText))),a.queueChild(p,a(f)),u.precacheNode(this,l),this._closingComment=f,p}var h=c(this._stringText);return t.renderToStaticMarkup?h:\"\\x3c!--\"+o+\"--\\x3e\"+h+\"\\x3c!-- /react-text --\\x3e\"},receiveComponent:function(t,e){if(t!==this._currentElement){this._currentElement=t;var n=\"\"+t;if(n!==this._stringText){this._stringText=n;var r=this.getHostNode();o.replaceDelimitedText(r[0],r[1],n)}}},getHostNode:function(){var t=this._commentNodes;if(t)return t;if(!this._closingComment)for(var e=u.getNodeFromInstance(this),n=e.nextSibling;;){if(null==n&&r(\"67\",this._domID),8===n.nodeType&&\" /react-text \"===n.nodeValue){this._closingComment=n;break}n=n.nextSibling}return t=[this._hostNode,this._closingComment],this._commentNodes=t,t},unmountComponent:function(){this._closingComment=null,this._commentNodes=null,u.uncacheNode(this)}}),t.exports=s},function(t,e,n){\"use strict\";function r(){this._rootNodeID&&l.updateWrapper(this)}function i(t){var e=this._currentElement.props,n=u.executeOnChange(e,t);return s.asap(r,this),n}var o=n(1),a=n(3),u=n(86),c=n(4),s=n(12),l=(n(0),n(2),{getHostProps:function(t,e){return null!=e.dangerouslySetInnerHTML&&o(\"91\"),a({},e,{value:void 0,defaultValue:void 0,children:\"\"+t._wrapperState.initialValue,onChange:t._wrapperState.onChange})},mountWrapper:function(t,e){var n=u.getValue(e),r=n;if(null==n){var a=e.defaultValue,c=e.children;null!=c&&(null!=a&&o(\"92\"),Array.isArray(c)&&(c.length<=1||o(\"93\"),c=c[0]),a=\"\"+c),null==a&&(a=\"\"),r=a}t._wrapperState={initialValue:\"\"+r,listeners:null,onChange:i.bind(t)}},updateWrapper:function(t){var e=t._currentElement.props,n=c.getNodeFromInstance(t),r=u.getValue(e);if(null!=r){var i=\"\"+r;i!==n.value&&(n.value=i),null==e.defaultValue&&(n.defaultValue=i)}null!=e.defaultValue&&(n.defaultValue=e.defaultValue)},postMountWrapper:function(t){var e=c.getNodeFromInstance(t),n=e.textContent;n===t._wrapperState.initialValue&&(e.value=n)}});t.exports=l},function(t,e,n){\"use strict\";function r(t,e){\"_hostNode\"in t||c(\"33\"),\"_hostNode\"in e||c(\"33\");for(var n=0,r=t;r;r=r._hostParent)n++;for(var i=0,o=e;o;o=o._hostParent)i++;for(;n-i>0;)t=t._hostParent,n--;for(;i-n>0;)e=e._hostParent,i--;for(var a=n;a--;){if(t===e)return t;t=t._hostParent,e=e._hostParent}return null}function i(t,e){\"_hostNode\"in 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i=n(345),o=n(347),a=n(349),u=n(351),c=n(352),s=n(355),l=n(357),f=n(360),p=n(4),h=n(362),d=n(370),v=n(368),g=n(371),m=n(375),y=n(376),_=n(381),b=n(386),x=n(387),w=n(388),C=!1;t.exports={inject:r}},function(t,e,n){\"use strict\";var r=\"function\"==typeof Symbol&&Symbol.for&&Symbol.for(\"react.element\")||60103;t.exports=r},function(t,e,n){\"use strict\";function r(t){i.enqueueEvents(t),i.processEventQueue(!1)}var i=n(22),o={handleTopLevel:function(t,e,n,o){r(i.extractEvents(t,e,n,o))}};t.exports=o},function(t,e,n){\"use strict\";function r(t){for(;t._hostParent;)t=t._hostParent;var e=f.getNodeFromInstance(t),n=e.parentNode;return f.getClosestInstanceFromNode(n)}function i(t,e){this.topLevelType=t,this.nativeEvent=e,this.ancestors=[]}function o(t){var e=h(t.nativeEvent),n=f.getClosestInstanceFromNode(e),i=n;do{t.ancestors.push(i),i=i&&r(i)}while(i);for(var o=0;o<t.ancestors.length;o++)n=t.ancestors[o],v._handleTopLevel(t.topLevelType,n,t.nativeEvent,h(t.nativeEvent))}function 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          "metadata": {
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    },
    {
      "metadata": {
        "id": "exFfavu0x9Bd",
        "colab_type": "code",
        "outputId": "ffbfd32d-a271-43e7-ee65-f08f8180d3fa",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "cell_type": "code",
      "source": [
        "%time \n",
        "\n",
        "#shap_values = shap.TreeExplainer(model).shap_values(X)\n",
        "\n",
        "explainer = shap.TreeExplainer(model)\n",
        "shap_values = explainer.shap_values(X)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "CPU times: user 3 µs, sys: 0 ns, total: 3 µs\n",
            "Wall time: 7.87 µs\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "LNqo2W4Pv_5g",
        "colab_type": "code",
        "outputId": "16061881-a55b-4519-d0c1-f21cedfb3cbf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 272
        }
      },
      "cell_type": "code",
      "source": [
        "shap.force_plot(explainer.expected_value, shap_values[0,:], X.iloc[0,:],matplotlib=True)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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zhe1c1uBhsWMb/ur5IQbj9jZcv6wO0zBY0uAl4E3k172ozmRZo/2ajUsCPPZm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\n/D9BrFg8KxwVLwAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 1440x216 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "wYN5GpTU1m0H",
        "colab_type": "code",
        "outputId": "868c5880-b1ee-4ff2-fb5f-680b4767b57d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 71
        }
      },
      "cell_type": "code",
      "source": [
        "# visualize the training set predictions\n",
        "shap.force_plot(explainer.expected_value, shap_values, X)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/shap/plots/force.py:111: UserWarning: shap.force_plot is slow many thousands of rows, try subsampling your data.\n",
            "  warnings.warn(\"shap.force_plot is slow many thousands of rows, try subsampling your data.\")\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "metadata": {
        "id": "SIidPWc1Bwqi",
        "colab_type": "code",
        "outputId": "7decbaf5-2079-4162-88ea-6efbfe6a1974",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 600
        }
      },
      "cell_type": "code",
      "source": [
        "shap.summary_plot(shap_values, X)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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EnfWTtrRNpsloCZPT2EBddjoZoRYAKnKySItFW89b8iPa7PeR2RIhq8VZH03z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\njDHGGFOELAk0xhhjjClC/w9yJ2gYBJLY0QAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 576x684 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "M7goa6GVDS53",
        "colab_type": "code",
        "outputId": "7643eb1f-0451-4c31-d15b-657162cc7c7c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 580
        }
      },
      "cell_type": "code",
      "source": [
        "shap.summary_plot(shap_values, X, plot_type=\"bar\")"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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IiBrLmcGona7pk8fd8n5PxuNWRUREtJesDEZERETUWJLBiIiIiBpLMhgRERFR\nY0kGIyIiImosyWBEREREjSUZjIiIiKixvFomaqdjxmJg+bGexjAZeCyD/SshERExMWVlMCIiIqLG\nkgxGRERE1FiSwYiIiIgaSzIYERERUWNJBiMiIiJqLLeJh4Gku4ClgUeADqok+1rgQNt/L20mA7cA\nT9ter+n5o4DX2t62tHsauAtYBKwEPAj8EDjW9lP9mM+rgNuA59t+WNL3gfcAr7F9b0O7PYC3Ap8t\n8+22bsP4ALsAzwN+WvptdqLtE0sc+wH3lPKO8t81wPQyl+b4Gl1ne5cyt+2Bg4Dnlz4WAcfZPl3S\nnsCB5ZmVS5u7yvf32N6kr88oIiIiKkkGh89+tmcBSFoO+AFwArBzqd8WuA7olLSR7av76G87278v\n/a0LfBPYWNLmtp8ZxPzmAycC2zdX2F4ITC1jdSdrz45fyjcFHrE9tY9xfmZ724bnlgfOAY4FPtIq\nvmaS3gicAfx3w2fwZuBqSX+x/T3ge6V8D2Bf22/oY14RERHRQraJR4DtJ4FvAVs0FO8NnAmcDuw1\nwP7+DGwNrA9sN8hpHQ28XtI2g3x+UGw/AcwG3jiAx9alWmW9saGf64BXAb8c1glGRETUXFYGR84y\nlG3Qsm37KuCKUn6MpNXLily/2H5S0v8CmwHnD2I+TwAfA06VdJXtRwbRx4BJegGwKzBnAI/NAZYD\nfi7pDODntm+3fd/wz7B+OjunjPUUWmrXeQ1U4mgvEyUOmDixJI72k2RwBEh6PtV5t3NK0V7AGWV7\n90lJs6gSpGMG2PVC4BWDnZftyyX9HPgy8MlBdDFF0rwW5ZvY7j4nuElDmxXLfyeUMRtdKKn5zOAh\nti+0fZ+k/6Q6f/hp4HuS/gacBBxte8kg5h7FggWj8nvAgHR2TmnLeQ1U4mgvEyUOmDixJI6x01vy\nmmRw+BxfLlBAtSJ4CXCkpGWA3YDlJO1f6icB9zHwZPCl5bmhOAD4Y7lUMlADOjMo6XXAXOCsFhdf\nejwzCGD7r1SJYHdyvS3VucNF5X8jIiJiGCQZHD7PXiBpJOmDwE3NN1wl3SRpmu05/elc0gupbgTv\nMJRJ2l4g6WDgZKqVthFj+w+SzqJaGXx3f58rF0iesn1z6ech4HRJGwC5KBIRETGMcoFk5O0DnNui\n/Ef08yKJpFcCs4ArbF8x1AnZnkn1uprBbBUP1OeBt0nats+Wz9kSOK+ctQRA0trAO/nXV+BERETE\nEGVlcARJmgpsCLy3RfWPgBslrdbD491n6lYAHgdmAjOGcXp7A3+g2sbtr57ODF5ve6dWD5SVyC8C\n35B0OdC9XdzqzCBUN6a/RHVQuJScAAAgAElEQVTh5fzymp5JVJ/BCbZPHsB8IyIiog8dXV1dYz2H\niFHVMWNxrX/o5+/6xFhP4d+Mx8PYrSSO9jJR4oCJE0viGDudnVM6eqrLNnFEREREjWWbeBySdBk9\nv2LmV7Y/0kNdRERExL9IMjgO2d5yrOcwnnVNnzzulvd7Mh63KiIior1kmzgiIiKixpIMRkRERNRY\nksGIiIiIGksyGBEREVFjSQYjIiIiaizJYERERESN5dUyUTsdMxYDy4/1NPqtHf9iSERETBxZGYyI\niIiosSSDERERETWWZDAiIiKixpIMRkRERNRYLpCMEElzgNmAgauBA2x/o6lNl+0OSScBG5XiFwPP\nAA+U7y+2fbCku4ClgeY/RHu/7WmS1gLuBG4HlpS6SaWfz9m+pow5E9imof9Gm9m+W9LLgKOBN5W5\nLNMdQ+nz1w3PrAv8FXiyfL+77bm9fjgRERHRNpIMjo67gc9LusD23c2Vtj/W/XVJ1h61vW+Lfvaz\nPauPsTa0vbChvx2B2ZJeZvsfpfjsHvrvdgFwJbCz7SWSpgDnAyfa3gWY2tB/F7CDbfcxr4iIiGhD\nSQZHx53AL4BvAe8Z5bFnAT8AXk21Stkf6wFfsL0EwPYjkt5HtUo4aJKWokqMP2H7olK2MtUq5RuA\nBcBxwFuB5YArgH1tPyFp6VK3KdUK6f8Be9i+XdI04FTgx8CWwPrdc4+IiIje5czg6DkKWKckVaOi\nJF8HAAuBWwbw6IXAaZKOkPQ2Scva/oft5i3qASkJ2lnARxqKtwP+YHseMJPqZ3I94FXAS4BDSrs9\ngLcDryt19wFfa+hnDeBG21OTCEZERPRfVgZHie1FkvYBzpZ0ecOW7UAcL+moprKTbB/f8P1cSUuA\nDmAt4DJgI9uPNbTZSdKmTf1cb3un8vVuVAnbjsB0YJKkS4GDbd85iHk3mgncJOlFtu8HPgCcKWlF\nqlW919teDCDpRKrVwMNsnyTpdNtPlbqrgcat7mWBc4c4t7bU2TllSPXjReJoL4mj/UyUWBJH+0ky\nOIpsXy3pJ1SXM/YeRBcDOjMo6VdUq2V/amrT65nBkoydDJwsaTLwFuBI4FKqVbtBsz1PkoFdJJ0K\n/DewE/A8qlXBC0syC7AU1XYxktYEvi7pDaXuecCDDV0/ZvtJJqAFC3pekO3snNJr/XiRONpL4mg/\nEyWWxDF2ektes008+qYD20h62yiMtT9wsKRX9PcBSZ2Stuj+3vZi27+kmvdUScsNw7xOBz4E7ABc\nbvtBqnODTwNbla3eqbZfbftl5ZkzgcepVg7XpUpOIyIiYoiSDI4y238HDqRaeRvpsa4DzgOO76tt\ng5WA8yXtKmkSQEkAd6m6HJbVt3OBdYDPUiV52H6G6hbzAZI6yrh7STqgPLMy1dnCJyWtQbW9vOIw\nzCUiIqLWsk08BmyfLenDDHzLtdWZQYAtgK4enjkE+LOkrW1fXMpanRkEONz2uZI2Bg4DDpX0DNXP\nyTVU7yccMtv/kHQRsBnV1nO3T1AlrvMkQfXOxI+Xus8D35S0F/BnYD/gUkkXA8cOx7wiIiLqqKOr\nq6ccImLkSDoSeJ7t/UZ77I4Zi8fVD/38XZ/osW48nltpJXG0l8TRfiZKLIlj7HR2TunoqS4rgzHq\nJK1NdVv5HWM9l4iIiLpLMhjDStKhwM69NLmaalv7YNu3j86sIiIioidJBmNY2T6K6gXbbatr+uRx\nt7wfERExUnKbOCIiIqLGkgxGRERE1FiSwYiIiIgaSzIYERERUWNJBiMiIiJqLMlgRERERI0lGYyI\niIiosbxnMGqnY8ZiYPmxnkaPevvzcxEREcMtK4MRERERNZZkMCIiIqLGkgxGRERE1FiSwYiIiIga\nG9ULJJLuAh4E3mx7SUP5TGAOsBYwHfhbw/zmA5+x/YumvmYAewDvtv3LAczhb8C+ti+StAfwPeA9\ntmc3tHkV8BPbr+pHf9cCs2x/o5c2rwWuBc6zvWdT3ZrA0cB/Al3AMsDPgE8Dy5bnuq0L3AUsKt/v\nYvu6XsadDDwNXG77XU11L6D6nK+xvWkp6wD2AfYEVgSWAu4HjrV9QcOzfwPuATa0/UxD+feBnwB/\nB44txSsAawC3dbezPVXSUcBrbW/bNK/PApt2z6k3kt4CfBNYFXgK+JLts/t6LiIiIp4zFiuDzwP2\n66V+ju2p5b9XAccDF0tarbuBpGWBHYHPAnsNcT5/Ab4laaUh9tObfYDDgC0lTWmq+zFwO7Ce7anA\nG4BXAMfbXtj9WQCvLe23a/h8ekwEGywBpkp6WVP5h4F7m8q+DXycKslct3z+R1B9Pp9oarsqsG+r\nAW1f1jDvjwCPNMx5aj/m3CdJywMXAUeXeW4LfFvSa4aj/4iIiLoYi2TwU8DnJL28P41tnws8Cvy/\nhuIdqFbMzgDeKWmVIcxnDvAr4Kgh9NEjSSsA7wXOBGYDH2pqsh5wbfdKqe1/lvafGsZpXALs3lS2\nO1Ui2j3P9ahWWt9v++bucttXAh8DvixpxYbnPwkcLumlwzjPgXgn8JTtWQC2b6ValfzgGM0nIiJi\nXBqL9wzeApxItQr17n4+swzPbY1CtdJ2hO0nJF1Etcr1zSHMaX/gZknft+0h9NPKB4ErbT8s6VTg\nJOC7DfUXAmdI+jZwBXC97YeHeQ5nAedIOtJ2l6Q3A4uBm4D1S5tNgD/ZvqXF8xdTbVm/lWoLG+Bm\n4DvAt4Bthnm+/TEVuLWp7Faei2fc6uxsXjwe3vbtKnG0l8TRfiZKLImj/YzVS6e/DNwoacey8teS\npEnAJ6jOg/2ylK1HdQbtqtLsFKoVwkEng7YfkHQIcLKkDQbbTw/2Bg4u41wnaWlJb7J9fan/MPBR\n4P2lXYekS4CDbf91OCZg+zeSHqNK+K6k2rqd2dRsVeC+Hp5/RtL9wGpNVUcCN0l6r+3zBzG1TSTN\nazGPP/Tj2RWB5rczP1HKx7UFCx7pd9vOzikDat+uEkd7SRztZ6LEkjjGTm/J65jcJra9iCpJOq7F\nFu80SfNKknALsBHVhYLHSv0+wIuBhyQ9TJUUvkbS/2NoTgUeobq4MSwkvRF4E3CRpIfLfF9FFTsA\ntp+2/R3bGwMrA5sBL6La2h1OpwMfLWft3gs0X7SYT5Vkt4qjo8zpgcZy209SxXKCpJUHMaefNZ4l\nLOcJj+3zqcqj/PufEVmxlEdEREQ/jdmrZWz/HLiM6iZto8YLJOva3t72n+HZSwM7A+vaXqX7P+Az\nDPEiie2u0schVLeah8PeVNvZjXNdC3ifpJUkvUDS5g1zWGz7WuAgYH1JSw/TPKDaKn43sBPwS9sL\nmuqvoEqqX9fi2a2okqzfNFfYvqo8+5VhnGt//BFYp6nsNfRvVTEiIiKKsX7P4EHAe4C39LP9B4B5\ntu9uKj8P2GGQq1PPKknn8eW/ISm3hj8E/Ms2uO35wA2l7nnABZJ2LqtvSFoO2AX4je2nhzqPpnF/\nRnVRZmaL+tupttrPKa/C6Y7jv6jOBX66rAS2ciDVauObhmu+/fAzYJKkD8Ozq7AbAz8YxTlERESM\ne2OaDNp+iGpbtr+vG9mbpuSq9HM31WWIDw/DtL4CdAzwmUO6t7bLf9dTrcDdXm65NvsRsFdJwDal\nWu28TdKtVCteywPbDz6EHp1G9W9+aatK2wcAJwBnSbq1zOdLwN62z+ypU9sPUr0fclheG9Mf5ajB\ntsDHJd1GleDuVj7TiIiI6KeOrq6usZ5DxKjqmLG4rX/o5+/afC+mZ+PxEHMriaO9JI72M1FiSRxj\np7NzSo8LXWO9TRwRERERY2isXi0zIiQdQXWusJXFtl/bQ11P/W1J77dbP2T7hoH0OZyGO952ImlP\nqrOIPXmH7Qd6qY+IiIh+yDZx1FHXeFve78l43KpoJXG0l8TRfiZKLIlj7GSbOCIiIiJaSjIYERER\nUWNJBiMiIiJqLMlgRERERI0lGYyIiIiosSSDERERETU2od4zGNEfHTMWU/3Fv9ExkL8oEhERMdqy\nMhgRERFRY0kGIyIiImosyWBEREREjSUZjIiIiKixJIMRERERNZbbxDUl6S7gQeDNtpc0lM8E5gBr\nAdOBv7V4fHdgE2Dn8v3qwHINba+3vVMf4x8ByPZWTeWrA9c2FK0L3AUsKt/vAvwKWNf2X5qefTtw\nGbCG7X/0Nn5ERERUkgzW2/OA/YBje6if05ysNZgLHAU9J3aDYXshMLX0Oxl4GtjO9u+720j6GfBR\n4H+aHt8DOCeJYERERP9lm7jePgV8TtLLx3oiA/QdYLeSLAIgaWVgh1IXERER/ZSVwXq7BTgR+Dbw\n7jGey0BcQjXvdwM/LmU7AbfY9pjNqgednVPGdf+jJXG0l8TRfiZKLImj/SQZjC8DN0ra0fa5TXXT\nJM1rKrvf9rTRmVprthdLOhXYk+eSwT2Ak8ZuVj1bsOCREeu7s3PKiPY/WhJHe0kc7WeixJI4xk5v\nyWuSwZqzvUjS3sAPJf20qbq3M4Nj7RTgNklrAC8EXgX8cGynFBERMf7kzGBg++dUt3CPHuu59Jft\nu4HLqW4X7wZ83/ajYzqpiIiIcSgrg9HtIOBPwMNUr5YZD74LfInq1TZbjvFcIiIixqUkgwGA7Yck\nfRr4QUNxqzODACfb7ul1NAPRqv8dbd/Yz+cvA74F3D2AZyIiIqJBR1dX11jPIWJUdcxYPKo/9PN3\nfWLE+h6Ph5hbSRztJXG0n4kSS+IYO52dUzp6qsuZwYiIiIgayzZxDDtJqwC/7qXJibZPHK35NOua\nPnnc/UYXERExUpIMxrCz/TDlT8pFREREe8s2cURERESNJRmMiIiIqLEkgxERERE1lmQwIiIiosaS\nDEZERETUWJLBiIiIiBrLq2WidjpmLAaWH7XxRvIvkERERAxVVgYjIiIiaizJYERERESNJRmMiIiI\nqLEkgxERERE1lmQwIiIiosZym7hNSLoLWBp4pKnqfmA34E7gNNsfbXrutcBNwBm2dytlXcAGtt3H\nmNOAq4BbgQ5gCnAbcJLtc5rmNt32rKbnZwO2fYSk3YDvlnk2O7TxWUmbA+cAx9n+QlOfXcBVtjdp\nKl8NuBeYa3uapIuBdUr1msCjwN/L9yfZPr632CMiIqKSZLC97NeccAFIWguYD7xH0kq2H22o/ijw\n1yGM+bjtqQ1jbQx8R9JU20cMsK/bbL+2H+32AQ4EDpV0pO1nmurXlrS27cbEcmfgvu5vbG/dMOc5\nwGzbMwY434iIiNpLMjh+PAH8Hng/cBqApKWBHYELgRWHYxDbV0naDvidpFNs/204+u0maQ3gTcAO\nwPbAFsClTc0uBnYHDmso2x34MfD64ZzPaOjsnDKu+x8tiaO9JI72M1FiSRztJ8ng+HIW8GlKMgi8\nB7gBWMAwJYMAtv8o6Q5gozLmcNoD+L7txZJOBfbi35PBs4ALJR1h+xlJbwSWAn7HOEwGFyxo3vkf\nPp2dU0a0/9GSONpL4mg/EyWWxDF2ektekwy2l+MlHdVUdhLVihhUK2YnlS3cecBHgJlAf7ZmB2oh\n8PwBPvNqSfNalK9ve4mkpai2tbvPA14CfFPSGrbv6W5s+3pJDwGbAT+hivOMAUcQERERfUoy2F56\nOzOI7aclnQ18VNKxwAZUW60jkQy+lOfO6D1D65vnk4ElDd/3dWbw3cBLgN9K6i5bjipB/GJT29Oo\n4rwaeB/ViuDmAwkgIiIi+pZXy4w/pwE7UV2oOM/2U8M9gKR3Ai+gumkMVVK4doum6zKwyyt7A3va\nXqX7P+AtVElf88/i96mSv52A39q+fyAxRERERP8kGRxnbN8E3EN1G3fmcPdfXjdzKvA52w+W4uOA\nT0maWtosJekIqkst5/ez35cDG1NddnmW7RtLP5s3lT9ItUX8ZUYgzoiIiKhkm7i9tDozCNWN20an\nAR/v6z2C/bRCwzm/lalW+g60fV53A9uzJE0GzpK0EtUvEXOBzWw/1tBXT2cGL6VK+K60/XCL+vOo\nLpJc1lR+GlUCefEg4oqIiIh+6Ojq6hrrOUSMqo4Zi0f1h37+rk+MWN/j8UZbK4mjvSSO9jNRYkkc\nY6ezc0pHT3XZJo6IiIiosWwTT2BNf7Kt2XW2dxnN+bSLrumTx91vdBERESMlyeAE1vgn2yIiIiJa\nyTZxRERERI0lGYyIiIiosSSDERERETWWZDAiIiKixpIMRkRERNRYbhNH7XTMWAwsPypjjeQLpyMi\nIoZDVgYjIiIiaizJYERERESNJRmMiIiIqLEkgxERERE1lmQwIiIiosZym3gUSfoQsD+wCrAUcD9w\njO0LJM0EHrW9b9Mz04GtbE+TtBZwJ3A7sKQ0mQQ8AHzO9jXlmZnANqUcoKP876XAIbYX9dBXt4tt\nHyxpN+B0YDvbFzXMaS1gju21JF0MrFOq1gQeBf5evj/J9vF9fCZzgPUanukAngHOBr5ku6u0Wwn4\nfIkLYBngJuAI279r6K8L2MC2exs3IiIiKkkGR4mkg4F9gR1tzy1lmwLnlkRnIDa0vbCh7x2B2ZJe\nZvsfpfjsxsRS0srAT4DPAYf11FcLfwG+Kelnth9prrS9dcMYc4DZtmcMMJ6jG5+R9GLgWuBe4DRJ\nywJXA38D/sv2QklLAXsDcyRtZvs3AxwzIiIiyDbxqJC0CnA4sFd3Ighg+0pgK+DXQxxiFrAi8Oqe\nGpQk8XLgjQPs+1pgDvCVwU5uoGzfV8btnuuuwAuBD3QnrraX2P428D3g6NGaW0RExESTlcHRsSHQ\nZfsnzRUNq4SD6riskB0ALARu6aXdy4H3A6cOYpgDgD9JOms0VuAk/QewOfCJUrQJcIntRS2a/wjY\nX9IKth8f6bkNVGfnlAkxxmhIHO0lcbSfiRJL4mg/SQZHx6pU5wOHy1xJS6jO160FXAZsZPuxhjY7\nlW1ogJWpzuHNAL7RQ1+Ndm9awVxYtrm/J2mgK4v9cbCkPcrXq1OdHzzY9qxStirV+cBW7qU6f7kK\n0HbJ4IIF/7azPqw6O6eM+BijIXG0l8TRfiZKLIlj7PSWvCYZHB0LgTUkTbL9TA9tnqH1tv1k/v2C\nx7Pn/CT9CrjR9p+a2jx7ZlDSu6guZJzZfSGjVV+9sT1T0oeBg4Af9tV+gJ49Myhpb+DTTWPMB9bo\n4dkXA4t57gJKREREDEDODI6OuVQJ3fbNFZK2LBdA7gPWbvHsVOCvvfS9P9XK2it6amD7p1TnEod6\n7m8fqmTwlUPspzenAE8C0xvKfgpsI6nVHxTeAfiZ7SdHcE4RERETVpLBUWD7n1S3eE+UtHF3efl6\nJvA01Vm+t0jatqF+S6pXqRzbS9/XAecBvb7CBTgQ2EXSBoMMA9u3AccAJwy2j36MsYQqwf2cpDVL\n8Q+Bu4AfSVodQFKHpI8AuwMHj9R8IiIiJrpsE48S28dLuh/4qqTVgKeoXpXyfttzACRtBnxJ0leo\nEvW7gS1s39xH94cAf5a0te2Lexj/z5K+DXxb0lsaqlqdGVxg+797GOto4AN9zGdIbF8t6adU5xu3\nt/20pHdSvWfw2nLZZjLwe2Baiy3yiIiI6KeOrq7mI2QRE1vHjMWj9kM/f9cnRrT/8XiIuZXE0V4S\nR/uZKLEkjrHT2Tmlo6e6bBNHRERE1Fi2iWPElG3v3s4X7lLOPI6qrumTx91vdBERESMlyWCMGNuX\nU92GjoiIiDaVbeKIiIiIGksyGBEREVFjSQYjIiIiaizJYERERESNJRmMiIiIqLEkgxERERE1llfL\nRO10zFgMLD8qY430XyCJiIgYqqwMRkRERNRYksGIiIiIGksyGBEREVFjSQYjIiIiaizJYERERESN\njavbxJI+BOwPrAIsBdwPHGP7glL/SuAI4P8BT5c2PwMOt/1AabMbcDqwi+2zmvr/JHACsLvtmaXt\nd4E7S5OlgEeBL9i+qDwzB5hte0aL+R4BTAf+1iKc3YFNgJ3L96sDyzW0vd72TqX/9YC/t+hjfdtL\nJHUBfwWeBFYqbc8pn82iFs81znEV4NcNRes29NU9z2WBq4BbW3Rxku3jS6yHAm+xfX1D/9OAI2xP\nK1/Ptr1S0xxWBxYAa9u+q0XMHcAzwNnAl2x3lb56nVNvcUdERERl3CSDkg4G9gV2tD23lG0KnCtp\nJeBXwG+A44GP2n6qlB8L/EqSbD9Uuvsr8BHgrKZhdi91jW6z/dqGefwX8FNJb7I9rx9Tn2N7qx7q\n5gJHlX6PANRD26NbJZtNdrDt0tergW8Cm0ra1PYzPT1k+2Fgavf3JbF8tq9SNg143PbUf+/hX9wJ\nfE/SBraX9NG2L/8Ss6QXA9cC9wKnleL+zCkiIiJ6MS6SwbJ6dTjw3u5EEMD2lZK2Ah4EvghcafuL\nDfWPStobuI5qRfHwUnUt8C5Jr7T9lzLGG6hW/u7qbS7/n737jpOrKv84/tkkhLr0VSnS4YYizUcE\nQQwEkSZNEJAaCAQBqQFFUBAjKgYUpIkQQgCBECmhl58ERCL40KReUAg1IQk1YEhIsr8/zpnk5jJt\n68zufN+vV17Zuefec59zZ2b3mVPuuPvDZvYE8G2gmmSw27n7y2a2G/AfYG/ghm469V+AgcAJQKXk\ntU3cfZKZPQxsyvxksO61tDT3inN0B7Wjvqgd9ae3tEXtqD89IhkEtgBa3f3ufEGml3AQoecwX95q\nZjcCezA/GZxNGEY9FDgtbiv0FJbqxcvqD5Qdfq01d59pZncA29N9yWArMJTQEzvW3Sd2VsVm9hVg\nB+DozqqzO0ydOr1L629pae7yc3QHtaO+qB31p7e0Re2onXLJa09JBpclzA+stM+kEmVvA8vlto0E\nbjeznxN6BL8PbEyFZNDMdicMq95RIZ6CgWaW70Gc7O4Dqzwe4BQzG5LbNs7dT6lw3DTC3LvOsFiR\ndgBs7+6vFx64+4tmdgFwMbBTB86XbfPyhPmDp7j72LbGJCIiIqX1lGRwGrCSmfUpM/9tCrBSibIV\ngHeyG9z9STObQuhtWixs8slmlj927UzC0Qd4hZBsvFVl7OXmDFarmjmDxaxM6QS5rdoyP+9s4Gkz\n2y93/rkUX8FeeB1m5xnOa3Mc6j8RuK4DMYmIiEgRPSUZnEBIFPYEsj1DmNlOQDNwD7AfxYdE9wJu\nLrJ9JHAgsDgwqsS5F1hA0lOYWQuwO+GadKu4eOcIYAxwVKZoErComX2xsLo7GkBYvTylRJWXx3qG\nERJNERER6SQ94j6D7v4RYW7fhWa2bWF7/HkU4TYyvwC2NLOzzKx/LF80DlkuDlxYpOprCQseNgXG\ndWUbupOZrUFImse7+121iMHd/w7cTlwtHbe9HLeda2aLxlhXBH4L/K7UbXDiyuTjgdPMbJWujl1E\nRKSR9JSeQeK97CYDvzGz5YBZhHvyfd/dxwOY2WbAL4EXzOwzwv3p7gG+6e4fF6nzPTN7EJjq7rM6\nEF5+Tt9Md98o/lxsziDAZe5+XjvrLxji7g/Hn8ea2afAooRettHAOVXWX41S8/Oecvd9SxxzMmHF\n9dTMtgMI94J8Ig7Jf0boob2g3Mnd/QEzuwf4A6GHuL0xiYiISEZTa2trrWMQ6VZNI2Z324t+ysEz\nurT+nriirRi1o76oHfWnt7RF7aidlpbmplJlPWKYWERERES6Ro8ZJpb2M7NxwDolih9z94O6M55a\nax3Wr8d9ohMREekqSgYbgLvvWusYREREpD5pmFhERESkgSkZFBEREWlgSgZFREREGpiSQREREZEG\npmRQREREpIFpNbE0nKYRswlf1NL5uvom0yIiIp1NPYMiIiIiDUzJoIiIiEgDUzIoIiIi0sCUDIqI\niIg0MCWDIiIiIg2srlcTm9lE4F1gM3efk9k+ChgPrAYMA94scvhgYBBwQHy8PLBIZt/H3X3/Cuc/\nM1N/X8IS1MeAX7v7v3JxDnP3sfHxEOCHwOLxuA+A4e5+aywfD6wHvBerWAi4LdYx28wGAre7+xK5\neJYHpgKru/vEIvU0AXOBa4FfuXtrPG534BRg2bjPLOB8d788lo8CdgPeKXIZtnf312MbFwKmx+vw\nKXALcLa7f1juOuaZ2QTgy8AAd/84s/1M4Axga3f/e+6Yc4ETgW2A/sAFsWgxYGXgpczu62dfLyIi\nIlJaXSeD0ZLAccB5JcrHu/suJcomAMNhXqJhZfYtZV79ZtaPkGTea2bfd/f78jub2c7xnN9w91fi\nth2BW81sfXd/Oe56jruPiOVLA48ARzE/yanWvHpiXSsADwNvAyPNbCPgGuBb7v543OerwHgze8Xd\n/xYPvdbdj6lwruMyCe8KwO+Af5jZ5tmkrhwz25jQIz0O2A/4c26X14BDgb9njlkI+B4wBcDd7wUG\nxLKBhMR5QDXnFxERkQX1hGHiY4HTzGzVWgfi7rPd/c/AaZRO2tYD3iwkgvG4u4DVgf+UqPcD4FFg\n7U6IcRIhGdw0bloH+AR4MrPP43H7gx08z4Gx7qPbcOhQYDRwJXBEkfLbgT3MrDmzbWfgaUA38RMR\nEelkPaFn8AXgQuBiQlJQD24CLjKzL7v7G7myu4CzzOwO4C/AQ+7+hru/VaoyM1udMPx5aEcDM7Ov\nADswP0F7iDBU/VAcDn7Q3V+OyVyHuHurmd0MbA/8torYlgD2BE519w/MbGEz29jdn8rsNo3QK7gv\n83sNDyUkkBt1NOau1tLSXHmnXnDOrqB21Be1o/70lraoHfWnJySDAGcDT5vZPu5+Q65soJm9mNs2\n2d0HdmE80+L/ywALJIPu/qyZbUoY2v4FsKaZvQT8wd0vyex6Spxb2I8w93EkYai4rQr1QJgX+R5w\nSmE4193fMbNNYjzHA5eZ2VvAn4DfuPvseOz+ZrZdru6K8yoJ12KZKmP9AXBf7AkFuJzQU/jD3H4j\ngR8DfzazLwGbAXsBI6hzU6dO79bztbQ0d/s5u4LaUV/UjvrTW9qidtROueS1RySD7j7TzIYC15nZ\nPbnicnMGu8rK8f/JxbZ988IAACAASURBVArd/QXgSAAz+yKhl+s8M3vX3cfE3bJzBhcFfgncD2xF\nWARSbAi/8HxlF0dk6xlKWGRxXS6eNwgLYYbF+Ym7Ab8nLCQ5J+5WzZzBYlYGqu1lHAokZlZIBpuA\nVjMb5u6fZPa7HbjUzNYHdgLGuvssM2tHeCIiIlJOT5gzCIC7PwjcyfzkpZYOBx5z9yn5AjPbyszW\nKDx293fc/XzgPmDjYpW5+wzgImBLM2shJFeLxkQyawBhFe/nzhtdHsuHZeLZJA4dF871gbtfRVhU\nUjSeapnZwoR5g7dXse/XgOXcfQl3Xzr+Wwp4gLCQZB53/yzGdyBwEDCqI3GKiIhIaT0mGYxOBr4L\nfL0WJzezfrH37WjCkGsxBwBXm9mKmeM2BDYnLOwoVm8TYbXsG8D7ccXx7cC5sdeQWN9vgd+5+8xi\n9cTbqRxPWHCzStz8HWCsma2TOd+qcXvReKphZl8gzIl8lzCsW8mRwJgi28dQfCHJSOAQAHf39kUp\nIiIilfSIYeICd3/fzE4kJCEFxeYMAlzm7qVuR9MWhfqbgKUI9xnc1t2fKLH/j4CfAfebWV/C4o33\ngRPc/c7Mftm5fn2Bl4HvZObwHQCcCTwRh0c/IyRIZW894+4PxKH0PxAWa/yW0Fs4JiaWfYH/ERbk\nZOcwFpszCHBGZp7m+WY2HFg4Ph4LHFwqOS0ws6UIQ+VbFym+Dbgi3nIm247nzOw14MZydYuIiEjH\nNLW2ttY6BpFu1TRidpe96Kcc3L13v+mJk5iLUTvqi9pRf3pLW9SO2mlpaW4qVdbTholFREREpBP1\nqGHizhRX1f6zzC4XuvuF3RVPT2dmlxDulVjMW+4+qDvjERERkeo0bDIY73WnrzDrJO6ev1dg3Wod\n1q/Hde+LiIh0FQ0Ti4iIiDQwJYMiIiIiDUzJoIiIiEgDUzIoIiIi0sCUDIqIiIg0MCWDIiIiIg2s\nYW8tI42racRsYNFOr7e7v31ERESkM6hnUERERKSBKRkUERERaWBKBkVEREQamJJBERERkQamBSTS\npcxsIrAQkP8y4HPd/c9mthdwAvBFYC7wCfAnd780U8co4GN3P8bMVgNeBf4DzIm79AHeAU5z94e6\nrjUiIiK9j5JB6Q7HufvY/EYzO4mQCO7v7g/GbRsD15lZ4u4nlKlzC3eflqlrH+B2M/uyu3/YyfGL\niIj0Whomlpows6WBs4DDC4kggLs/BewDHGNma7ahyrHA4sDanRqoiIhIL6dkUGplC6DV3e/KF7j7\nv4GXge2qqcjM+hJ6GKcBL3RmkCIiIr2dhomlO5xvZsNz234JTC5zzNvAcmXKJ5jZHKAJWA24E9jG\n3T/pSKAd0dLS3FDn7WxqR31RO+pPb2mL2lF/lAxKd/jcnEEz+zawopk1uXtrkWNWICwKKWXenEEz\newR42t2f77SI22Hq1Pwama7X0tJck/N2NrWjvqgd9ae3tEXtqJ1yyauGiaVWJgCzgd3yBWa2PrAW\ncE+VdR0PnGJma3ReeCIiIo1ByaDUhLt/DJwKXGxmAwvbzWxd4DrgN+7+ZpV1PQbcCJzfBaGKiIj0\nahomlppx94vM7G3gbDP7AuG+gR8R7kF4VRurOxVIzWxXdx/X2bGKiIj0VkoGpUu5+2oVym8Gbq6w\nzyGZnycSFo3k93kb6D2zeUVERLqJholFREREGpiSQREREZEGpmFiaTitw/r1uFsCiIiIdBX1DIqI\niIg0MCWDIiIiIg1MyaCIiIhIA1MyKCIiItLAlAyKiIiINDAlgyIiIiINTMmgiIiISAPTfQal4TSN\nmA0s2uF6phw8o+PBiIiI1Jh6BkVEREQamJJBERERkQamZFBERESkgSkZFBEREWlgXbqAxMwmAsPc\nfWyJ8h2A64Hfu/svcmUbAL8G1omb+gK3AKfGbX+N2xcC1gD+A8yJ27Z399fLxLUa8GrumD7AO8Bp\n7v5Q3G8UsFvcnjfvHGb2A+B4YOkY52TgXHe/KZafCQwD3ixSz2B3nxDP9bG7H1Mkvj5AKzAaOBv4\nNnBBPH4xYGXgpUyd67v7HCows77A87Hudd29NVM2CjgQWD1/Lc3sr8CesWxi3LYJ8HNgQ+CzuOs4\n4Cx3/zhT58fufkyuvmHALu4+MNP28939+Nx+E4GBwAHxH8DywCLMv7aPu/v+ldouIiIiQa1XEx8J\nnAScbma/dPe5AGa2EHAvMNzdvxu3fQG4D/ifu/8cGBC3r0ZIHrZw92ltPP8Cx5jZPsDtZvZld/8w\nbr42n7xkmdkpwDHAPu4+IW7bDrjBzJZw99Fx1/Huvkt74zOzFYGHgHfc/XLmt38gcLu7D2hj3QDf\nBZ4CmoHtCNc36w1gMDAvUTez5YGvZHcys28AdwGnAN9z97lm1gJcDvyfmX3T3We1Ia73gP3N7Bp3\n93yhuw8HhsdznwlYO66tiIiIUMNhYjNbCfgqcBWhd2rHTPHSwAqE5AcAd58CbEvoLewqY4HFgbWr\n2dnMlgbOAI4oJIIA7n4/sAvwz84KzN3fBq5jwevUUUMJvY1XAkcUKb8FGGxmTZltBxASv6wRwKXu\n/qdCQu/uU4F9gRWBtvbUTQd+AvzZzGr9gUVERKRXq+Uf2iHANe4+28yuICQjd0BIJMzsYeA2M7sE\n+BvwlLu/21XBxCHTE4BpwAtVHrYF0Orud+cLsslhJ+oPzOyMisxsdWB94G7C0PYfzOwLMekueAoY\nxIK9hoOBw4BjYz2LA18Hjsufw91nmNltwPaEhLMtRhKGqU8AftfGY7tFS0tzrUMA6ieOjlI76ova\nUX96S1vUjvpTk2QwJl6HERINgNuAP5rZSu7+Vty2A/BDwty0s4CZZjYW+HE7hoNLmWBmc4AmYDXg\nTmAbd/8ks8/+cdg3qzAvbVnC/MBqDDSzF3PbJrv7wGoONrMBwEGE3rzOcDgwOs4tnGNmNxASvd/m\n9htJeK7uMzMD+ri7hx+B0IvbB5hU4jxvA2u1NTh3bzWzoYTnaKy7v9rWOrra1KnTax0CLS3NdRFH\nR6kd9UXtqD+9pS1qR+2US15r1TO4M2H48F+ZpGIRQtJxFkBMyEYAI8xsYWDr+Hg0sFMnxZGdk/cI\n8LS7P5/bp9ycwWnASmbWpzA8WkZ75gwWktU+wLuERHhcG+v4nDgnczCwuJkV2tYHmGJm52QXkgDX\nAL8ws2XjMVflqnuPsMhlJYovkFmB+Qtw5lJ8akI/5i/kmcfdUzP7A3AJ4cOBiIiIdLJaJYNDgcPd\nfd7QoZltBIwzs+GExGJNdx8P4O4zCT1TvwAu7KKYjgfGm9lod3+lymMmEJKYPQnzDecxs52AZne/\noQMxtWdRTDV2B15y929lN5rZk4Te2vsL2+KQ/X2EuYJ7E1YLkymfYWYPAfsBj+bq609YpHJK3DQJ\n2LRIPAOA10rE+hvgqbhiW0RERDpZty8gMbNVCQtBbs5ud/engRmEHqBVCPMFd8wc10xIOB7uirjc\n/THgRuD8NhzzEXAacKGZbVvYHn8exfxbrNSbI4FiSeoYii8kGQn8FHjM3YsNi58CHGJmR5lZH5i3\nuOZa4GXCdQW4Avi6me1eODAmzbsB5xULNK5CPiKWL1G5aSIiItIW3dEzeH7s7St4Bbjf3T8osu+N\nhJW5u8fbvPzYzC4g3AeviTCn72ddGOupQGpmu2aGY4vNGQQ4w91vcPfzzWwy8BszWw6YRRgu/X6h\nZzMqNmcQ4DJ3L5oIdQUzW5sw5L5fkeIxwJnxNj5ZdxN6QEcVqzPOIfwW4RY0w8xsFmFI+K/Arwr3\nPHT3V8xse+BXZvZrwoeR14Ed3f3ZUjG7+8NmdivFE1URERHpgKbW1tbKe4n0Ik0jZnfKi37KwTM6\no5oO6YmTmItRO+qL2lF/ektb1I7aaWlpbipVpq+jExEREWlgvfKGvvHehNuUKH7L3QeVKOsVzGwc\n87/GL+8xdz+oO+MRERGR+tUrk0F3/2GtY6gld9+11jHUs9Zh/Xpc976IiEhX0TCxiIiISANTMigi\nIiLSwJQMioiIiDQwJYMiIiIiDUzJoIiIiEgDUzIoIiIi0sB65a1lRMppGjEbWLRdx9bDt46IiIh0\nJvUMioiIiDQwJYMiIiIiDUzJoIiIiEgDUzIoIiIi0sC0gES6lZlNBBYCphNWcXwK3AKc7e4fmtkh\nwJ+AV+MhfYGPgV+4+y2xjvHAesB7wMLAXOBe4Cx3n9RdbREREekN1DMotXCcuw9w91WBgcBKwD/M\nbIlY/nIsH+DuawM/Aq41swGZOs6J5asDXwX+B7iZrdSN7RAREenxlAxKTcWevAOBT4CjS+zzMPAE\n8O0S5R+4+0nAP4DTuyhUERGRXknJoNScu7cCNwPbl9mtPzCzQlU3VahDREREcjRnUOrFNGCZYgVm\ntjswALijvXV0lpaW5q6svl3qMab2UDvqi9pRf3pLW9SO+qNkUOrFykBh8cfaZvZi/LkP8Aqwvbu/\n1YY6usTUqdO7svo2a2lprruY2kPtqC9qR/3pLW1RO2qnXPKqZFBqzswWJswbHBE3vezuG7SjqiHA\n7Z0WmIiISANQMig1ZWZfAC4B3gVGAvu1o45m4LfAisA5nRqgiIhIL6dkUGrhfDMbTrhHIMBY4GB3\nn2lm1dZxipkNIdyzcGHCfMLN3f3dTo9WRESkF1MyKN3K3VerUD4KGFVhn4GdFpCIiEiD061lRERE\nRBqYkkERERGRBqZhYmk4rcP69bhbAoiIiHQV9QyKiIiINDAlgyIiIiINTMmgiIiISANTMigiIiLS\nwJQMioiIiDQwJYMiIiIiDUzJoIiIiEgD030GpeE0jZgNLNrm46YcPKPzgxEREakx9QyKiIiINDAl\ngyIiIiINTMmgiIiISANTMigiIiLSwGq+gMTMJgLvApu5+5zM9lHAeGA1YBjwZpHDBwODgAPi4+WB\nRTL7Pg68UK7c3ffPnPMYYDhwtLtfm9m+FvAysIy7fxC3fQW4L+77VzMbAlwITCwS56nufrOZXQNM\nc/fjc9fgUqCfuw+Jj5uBnwG7xl36A08DZ7r707mYLnP3obn6NgGeAK5w9yGZfV8G5uZiu9ndTzWz\n7YCx7r50rONhYGVgfXf/JFP3cOBTdx8eH68E/BLYGphDeE3dDfzM3d+L+zQBRwKHA4sDfYHJwHnu\nflOm7jeBj919QK49CwFvAx+6+1qZfQE+zrXnLXcfhIiIiFSl5slgtCRwHHBeifLx7r5LibIJhAQO\nMzsTsCL7ViovGAocDxwBXFtiH8xsHULCc7y7/zVT9KK7b1zquGqY2SLAg8ArwJbu/q6Z9QWOAh4y\ns0Hu7nH3ycAeZnaCu/8vU82hwGtFqt+skMxW6TNCondiiVhbgEeBG4EN3P1TM1uGkBQ/aGZfdfdZ\nwMXAVsB+7v5sPHY74GozW8HdL8pUu4iZbenu/8hs2xX4hM87xt1vaUN7REREJKdehomPBU4zs1Vr\nFYCZbUnolRoFfNHMkhL7rQbcC/zE3a/vglAOBZYBfuDu7wK4+xx3/yNwJfDbzL6fAI8Be2XiWzg+\nvqsTYjkVONDMvlqi/GRgoruf4O6fxljfj204I8azHjAE+H4hEYz73Q/8EDjbzBbP1HkLcFjuPIOB\nWzuhPSIiIpJTLz2DLxB6ky4Gdq5RDEcCl8efRxJ6B0/K7bMicBvwS3e/uoviGASMiz1qeWOAo2PC\nV3A1oddwdHy8G6G37l3gSx2MZTLwU+AyM1tgGD8T6zX5g9x9JnATgJkNAp539xeK1D8OWBjYHPi/\nuO0vwL1mdpy7TzezFYCvAhdRu9cGAC0tzbU8fUn1GldbqR31Re2oP72lLWpH/amXZBDgbOBpM9vH\n3W/IlQ00sxdz2ya7+8DOOLGZLQvsQOipArgKeNLMfhoTm4JbCMnLCiWqGlAkTnJz4A4ysx1yu3wR\nKAw3Lwv8q0T9bxOes6VyMV1kZmu7+8uEXrlLASty/GNmlp8zeJC7P1bifBAS5AMJw+fn5sqWBSaV\nObbsPu4+18wmA8tlNr8N/APYJ577YOAGwpB13oVm9pv8Nne/sEJM7TJ16vSuqLZDWlqa6zKutlI7\n6ovaUX96S1vUjtopl7zWTTLo7jPNbChwnZndkysuN2ewMxxCmLf4ptm8HGox4HuEnqqCI4HXgUfN\n7CV3H5Orp5o5g6NLLSCJD6cAK5U4dgVgFvBBjLdw3a4DDjWzi4CNgTsongy2dc4g7t5qZkcAj5jZ\nX3PF04BVKlRRsj1xYcmXgHdyRSMJi4YuJzw3+xIW/+RpzqCIiEgH1cucQQDc/UHgTuCcbj71EcB2\n7r504R+wX9ye9YS7/4eQnFxhZpt3QSz3EBaFLFKkbC/gviJDyCMJK6YPAK5392K9aO3m7i8CFxCG\n8bPuA/aNSd08ZtbPzH5jZsvFfdY1sw2LVL0LYTXwo7nt44A1zewAwsrlpzqjHSIiIvJ5dZUMRicD\n3wW+3h0nM7NtgCWAh3NFdwKbxpXDC3D3+4DTgVu6YNHLNcBbwPUxmcLM+sRb1xwI/LhIPE8SeumO\nB0Z1cjwFvwZWB/bIbDuXMAx8WWERiJktHWPYGvgoJs9/jO3ZoHCgmW1FmAd4YmHxSaY9nxFWc4/o\nwvaIiIgIdTRMXODu75vZiSw4PFtsziCEe+yVuh1NtYYCN7p7ay6OGWZ2B6F38NIicZ4fe7vuMLNv\nxM1F5wwSFoScUk0w7j4rLrr4GWFotpXwPD0JbB176YoZCRxWoRet2JzBquZeZobxx2e2vWtmWxBu\n3fOMmc0i3GvwFuCIQg+lu59gZkcSbiVTWDk8CRjq7qVWPY8EjqbMLX4oPmcQYHt3f71Sm0RERASa\nWltbK+8l0os0jZjdrhf9lINndHYoHdYTJzEXo3bUF7Wj/vSWtqgdtdPS0txUqqweh4lFREREpJso\nGRQRERFpYHU3Z1Ckq7UO69fjuvdFRES6inoGRURERBqYkkERERGRBqZkUERERKSBKRkUERERaWBK\nBkVEREQamJJBERERkQamW8tIw2kaMRtYtKp96/FbR0RERDqTegZFREREGpiSQREREZEGpmRQRERE\npIEpGRQRERFpYEoGRURERBpYp6wmNrOJwELAdKCJkGQ+DJwEbAj8DXgpc0gf4BXgZHd/JtYxHlgP\neK/IKdZ39zkVYqh4vJm1An9z90G5Y5cD3gYmuPvATJuGufvYCuddDXgV+A9QiLEP8A5wmrs/FPcb\nBewWt+dt7+6vx/1+ABwPLA30BSYD57r7TbH8TGAY8GaRega7+4R4ro+BH8d27e3u9+biXhmYCGwA\n7FtFneOZf30XBuYC9wJnufukEpenKDO7Htge2NTdJ2a2HwJcCRzk7lfnjvkRcAEwGPgX8NdYtBCw\nBgte/3nXU0RERMrrzFvLHFdInMxsEeAvhD/elwP/c/cBhR3NrAk4GbgdWDVTxznuPqIDMVRz/Opm\ntrq7v5rZdgDQpoSmiC3cfVrhgZntA9xuZl929w/j5mvd/ZhSFZjZKcAxwD7uPiFu2w64wcyWcPfR\ncdfx7r5LpYDc/RMzuwYYQkjcsg4FHnb3F82s2jrnXV8zWxr4GeBmtpm7v1UpnnjcF4DNgd8BhwOn\n5XZ5LcZ2dW774FiGuz8HDIj1rUZIxhe4/iIiIlKdLhkmdvdPgYuAHUuUtwI3AquY2fJdEUMZ4wiJ\nRdZg4NZOPs9YYHFg7Wp2jsnVGcARhUQQwN3vB3YB/tnOOC4FdjOzlsy5mghtvrSddeLuH7j7ScA/\ngNPbcOhgwnM/CjjQzPIfSB4GNjCzNTPxbkzoJZ3Y3nhFRESkuK686XR/YGaxAjPrDxwHPFuD3pyr\ngZvN7Ex3n2tmmxISjSeBjTrjBGbWFzgBmAa8UOVhWwCt7n53viCbHLaVuz9jZo8DBwHnxs3fJiSq\nN7W33oybgF9Vs2NMQg8HdnH3SWb2FPBd4ObMbrOB6wm9g4Vew0JPYcXe0M7W0tLc3adss54QYzXU\njvqidtSf3tIWtaP+dEkyaGbLEIaBr4+bFjOzFzPnXJXwx32H3KGnmNmQ3LZx7n5KlaeueLy7P25m\n7xPmrN1NSDSuqrL+ciaY2RzCnMnVgDuBbdz9k8w++8dh36zH3X1/YFnC/MBqDMxcz4LJhfmORVwK\nnMr8ZHAIcKW7z+pAnQXTgGUqhwyEJHSyuxfOcznwQxZMBgFGEobYf05I1L8PbEwNksGpU6d39ynb\npKWlue5jrIbaUV/UjvrTW9qidtROueS1M5PB881sePx5JnAb8EtCj9e8OYNxPuELwENF5pl1x5xB\nCMnGYWb2ALA3oUcwn5i21bw5a2b2CPC0uz+f26fcnMFpwEpm1sfd51Y4V1VzBjPGAH8ws62AFwm9\ncRt0sM6Clal+vuWRwKZm9kFm2+Jmtlp2IYm7P2lmUwjPyWJhk0+OcxtFRESkE3XmnMHj3H1A/LeR\nu5/u7p8bJo7zCU8GfhvnydXCNYREY3/gX+5ebY9ctY4n9FKu0YZjJhBWw+6ZLzCzneKClHaJ1/wq\nwny9HwB/d/f/tre+nCGEhUBlmdkKwCCgxd2XLvwDLox15I0EDiQMb4/qpFhFREQkpyb3GYyrjl8E\nhlfat4vO/y5hiPhsuiDRcPfHCIskzm/DMR8R5shdaGbbFrbHn0cBn3UwrD8BuxOSwT91sC7MrNnM\nLgZWBM6p4pAhwD25YXMIvZaDiywkuRYYCGxKWPQjIiIiXaArF5BUchzwmJld4e5Pxm3F5vwBDHH3\nh6uosy3HjwS2pesSjVOB1Mx2dffCOYrNGQQ4w91vcPfzzWwy8Jt478NZhHv/fd/dx2f2Lza/D+Ay\ndz+vWDDxFjLPAOtQfOV0NXUWru9ChHsN3gFsHpPrksysDyEZPLFI8T8Ji0YWGKJ29/fM7EFgam5u\no4iIiHSiptbW1lrHINKtmkbMrvpFP+XgGV0ZSof1xEnMxagd9UXtqD+9pS1qR+20tDQ3lSrT19GJ\niIiINLBaDhNXzcy2J3ybSSkHxXl6XXHuS4BtShS/lf9qu0ZlZqcTvsmlqOw30IiIiEj90DCxNKLW\nnta9X0pPHKooRu2oL2pH/ektbVE7akfDxCIiIiJSlJJBERERkQamZFBERESkgSkZFBEREWlgSgZF\nREREGpiSQREREZEG1iPuMyjSmZpGzAYWrWrfev8GEhERkY5Sz6CIiIhIA1MyKCIiItLAlAyKiIiI\nNDAlgyIiIiINTMmgiIiISANr2NXEZjYRWAgofNN0H2C0uw/P7NMXeB5oBdZ199ZM2SjgY3c/psw5\nlonHv+ju2+TKxgNrAuu5+/TM9jMB3P3M+HhF4CxgIDCH8JzdC5zu7u/GfVqB14BPcyE87u77V7gO\n44B14sNVgI+B9+LjS9z9/Er1m9lA4AHgBHf/Q67+VndvysT5NXf33D7PAiPcfVRs/zDgzVjcFP89\nDJzk7u93tM0iIiIyX8Mmg9Fx7j4WwMy+DDxuZv9293Gx/LvAU0AzsB1wXxvrPxgYDexiZmu7+8u5\n8k+BXwHHFjvYzJYHJgC3Al9x9xlmtjRwAfB3M9vE3WfG3ffKJ1nVcPddM+cbD9zu7iOK7Fqp/teB\nn5nZTe7+elvjyBnv7rtk4loUuBY4HzioDTGJiIhIBRomjtz9DeA5YO3M5qGEZO5K4Ih2VHtEPH5U\nieN/CuxrZpuVOP5EYJK7H+vuM2KcHwBDgNPbEU9XehW4GLiosyuObb8N2LSz6xYREWl0jd4zOI+Z\nbQKsDxwVH68eH98N9AX+YGZfcPcpVdb3LeBDd3/OzKYBT5jZae4+K7PbVODHwGVmZu4+O1fNIGBs\nvu5Yx01ta2G3GA7828z2dvcbO6tSM2sBDgHGd1ad1Wppae7uU7ZZT4ixGmpHfVE76k9vaYvaUX8a\nPRk838yGA4sAKwG/Bl6KZYcT5hDOAeaY2Q3AYOC3VdY9FLgCwN3fMbNHgT2AG7I7ufuVZnYQcFKR\nupcFJlV5vrFmlp8/d3phGLwTVKzf3Wea2ZHAtWZ2r7t/2M5zDTSzF+PPiwNLAH8kDKm3KaaOmjp1\neuWdaqilpbnuY6yG2lFf1I7601vaonbUTrnktdGTweycwaUIw5xXmdlgQuK3uJkVFoj0AaaY2TnZ\nhSTFxLl+ewA7m1lh/l0/YGlyyWA0FPinmeV706YRFnVUo6vnz1VVv7s/YGZ3A+cQ2pXVSvGpCf0I\ni2MK5s0ZNLP1gceAazLzI9sUk4iIiJSmOYNR7MW6nJDE7Q685O5LuvvS8d+ShJXHg6qobjAwxt2X\nKhwPLAVsYGZr53d295eA84BLckX3AfuY2QLPk5n1NbPfxOHTejQM2M3MtsxtnwSsnt1gZgvHba8V\nq8jdnyPM2fxjF8QpIiLS8JQMRvE2MnsATwNHUrwHbwwVFpKYWVPcJz8cPAe4mTD8XMw5wMrA3plt\nvyesZL7czJaI9S8FjAS2Ado7DNul3P09wrD3Zbmi84DTzGwlmLdK+I/AI4Rbx5RyBvA1M9urC8IV\nERFpaI0+TFyYMwjhWjwBnArcD+xXZP8xwJlm9oX4eH8z2y63z+nAchS/Dc0Y4Doz+9xKYHefZWZH\nAH/PbHvfzLZg/sKMWYTh1HHAUbnFKMXmzwFsmNuvvUrWX2xnd7/WzA4E1stsPg+YCdxpZv3jtnuB\nPd19bqkTu/u78f6D55nZXe7+SaWYOqnNIiIivV5Ta2vZ6W8ivU7TiNlVv+inHDyjK0PpsJ44ibkY\ntaO+qB31p7e0Re2onZaW5qZSZRomFhEREWlgjT5M3OvFIekDSpW7+4BuDKcutA7r1+M+0YmIiHQV\nJYO9XPyu5eEVdxQREZGGpGFiERERkQamZFBERESkgSkZFBEREWlgSgZFREREGpiSQREREZEGpmRQ\nREREpIHp1jLScJpGzAYWrWrfev8GEhERkY5Sz6CIiIhIA1MyKCIiItLAlAyKiIiINDAlgyIiIiIN\nTAtI2sHMJgILewQlegAAGRlJREFUAdOBJkJS/TBwkru/F/fpB7wAfObu6+WOHw5s4O67x/0+AyYC\nM4ElgHeB64Dz3H1WFfFsB9wDvBw39QFmxeNHxn2uAXYCpsR9muL/twE/dfdZmVg2cfenYpw/Br7m\n7k/lzvcTd98uPl4Y+CmwN9AX6A88CZzh7s9kzj/N3Y83s7VirC8Dc2O8c4GrgN+4e2vmXBsD44Hr\n3P2HuXZPBoa4++2VrpGIiIgUp57B9jvO3Qe4ewJ8BVgKuCBTvjvwGPCmmW1TRX17xPpWBr4PbAvc\nbmbVPkfT4/ED3H2dWMd5ZjYws8/ozD4J8HVga+AnZeqdCFxWKg4zawJuBbYCBsV61yYkxxPMbO0y\ndW+WiXc74Ajg4Nw+RwKnA7ua2eJl6hIREZF2UDLYCdz9U+AiYMfM5qHAaOBKQpLTlvpSYFdgfWCP\ndsb0PHBXLqb8Ph8A9wGblqlqNKG38NgS5TsCWwB7u/ukWO9sdz8P2A94r8p43wSuz8ZrZksAu8UY\n7gb2raYuERERqZ6GiTtPf8IwL3EYdC1CotUfONfMlnf3adVW5u6fmtldwPbAXzsaUzFmtjqwF3BJ\nmTpaCcnsQ2b2V3d/I1c+CBhfGB7PcvfbOhjv/sDd7v6RmV0B/B64oo11dkhLS3N3nq5dekKM1VA7\n6ovaUX96S1vUjvqjZLATmNkywMmEni0IydNV7j4X+NTMxhKGP89tY9XTgDXaGdNWhETyF5nNB5nZ\nDvHnpYHZwO9YcHj7c9z9OTO7hND7uWuueFlgUntizMW7HnAAMDizeSixR9LdHzGzJcxsI3d/uqPn\nq9bUqdO761Tt0tLSXPcxVkPtqC9qR/3pLW1RO2qnXPKqZLD9zo8LLCD0Zt0G/NLM+gOHAIuY2fGx\nvA8hYWprMrgy1SdazWb2YuZ8bwN7ufu/M/uMdvfjAcxsZ8IQ9ujsgo0yhgP/NrO9gfcz26cRhrPb\n4zEzKywgmQac6O53xvg2AzYizJss7L8wIUE8qp3nExERkRwlg+13nLuPzW80s/2AZ9x9UG77M2Y2\n0N3HV1O5mX0R+C5hGLca0919QJX74u53mNkTwK+oIrmKw9ZDgWtYcP7gfcDRZrZCYc5ggZmdADyQ\nXYmcs1mct1jMUOB0d/91pr4VgOfMbJi7/69SzCIiIlKZFpB0viOBG4psH0OVC0nMbE1gLHCfu9/X\nibHlnQgcamblFpDM4+4PAPcCZ2W23QvcD9xkZqtBuK2OmZ1IWKVcKtkrycyWAvYhXLPs+ScBz6CF\nJCIiIp1GPYOdyMwGEFbWfq9I8RjgaTNbrsThN5vZTGAx4H/AKGBEV8RZ4O7Pm9llwMVmtkWVh50E\nvMiCw9d7AacCd8dbzbQCjwJfd/eJ7QjtQOAFd/9vkbJCUj0yPr7EzLLX6QV3b9cKbBERkUbU1Npa\nzXQxkd6jacTsql/0Uw6e0ZWhdFhPnMRcjNpRX9SO+tNb2qJ21E5LS3NTqTINE4uIiIg0MA0T9wBm\ndielbzHziLsf2p3xiIiISO+hZLAHcPedah1Db9I6rF+P694XERHpKhomFhEREWlgSgZFREREGpiS\nQREREZEGpmRQREREpIEpGRQRERFpYEoGRURERBqYkkERERGRBqZkUERERKSBKRkUERERaWBKBkVE\nREQamJJBERERkQamZFBERESkgSkZFBEREWlgSgZFREREGpiSQREREZEG1tTa2lrrGERERESkRtQz\nKCIiItLAlAyKiIiINDAlgyIiIiINTMmgiIiISANTMigiIiLSwJQMioiIiDQwJYMiIiIiDaxfrQMQ\n6Spm9jNgZ6AJuMPdz8qVDwGOAGYDTwHHuPvcbg+0giracQxwMDAH+C8w2N1ndXugVajUlsx+xwEn\nuPtq3Rhe1ap4Tgy4BJgLTAH2dfdPuj3QCqpox/HAPsAs4APgEHd/v9sDrYKZfQm4FljY3bcqUt5T\n3u+V2tEj3u+V2pHZr97f65Wejx7xXq9EPYPSK5nZ14E9ga2BbwLfNbNvZMpXBn4GbA9sCawE7FuD\nUMuqoh0bAMcCW7n75sAiwH61iLWSSm3J7LcOIUGpS1U8J32AG4Bj3f3rwL+Akn8Ma6XK98ixwDfd\n/VvAy8AxtYi1StcB9xYr6Cnv96hcO3rM+50y7Sio9/d6VO756BHv9WooGZTeakfgVnefFT813wrs\nlCnfDnjA3T9w91bgxlx5vajUjueBr7r7zPh4KrB8N8dYrUptKfxyvRQ4rgbxVatSOzYBprv7BAB3\nP8vd76lBnJVUascnQCvQHB8vTXh91avdgEdLlPWU9zuUb0dPer+Xa0dPea9D+Xb0lPd6RUoGpbda\nEZiceTw5bqu2vF6UjdPd57r7dAAzW53wKXtMt0ZYvWqu+cnA3e7+QrdF1XaV2rEW8LaZXWxmj5jZ\nFWa2ZLdGWJ1Kr633gV8Cr5rZK4R2Xd6tEbaBu39UprinvN/LtqMnvd8rPB/QM97rldrRU97rFSkZ\nlEbRROjlaG95vSgap5mtC9wDHO7ub3R7VO2zQFvMbH1Cb9W5NYuofYo9J5sQEqktCXOJftLdQbVD\n/vlYBTgNSNx9DeAZekY7qtFT3u9F9dD3+zw9+L1eTE98r3+OkkHprd7g8z2Bb7ahvF5UjNPM1gPG\nESaS392NsbVVpbZ8D1gW+IeZ/RNYwczu68b4qlWpHW8DL7j7pDgkOQ7YqBvjq1aldmwOPOXu78TH\ntxPmFvZEPeX9XlEPer+X01Pe65X0lPd6RVpNLL3VHcAoMzs7Pt4TOCxTfh8w3MyWA94nTML+c/eG\nWJWy7TCz/sD1hBVsj9cgvrYo25a4knXealYzm+ju3+7eEKtS6bX1T2BlM1vR3d8m9Bg8280xVqNS\nO14ENjazRd19BiE5rOshvTJ6yvu9rB72fi+pB73XK+kp7/WKmlpbe2xPuUhZZnYysDdhOOhGdx9h\nZtcDJ7n7W2Z2AGHy8mzgEWBY/HRXV8q1A/gG4Y/aU5lD7nP3X3V/pJVVek5y+06s49tNVHptbQX8\nAZhBmOR/WD3ekqWKdpxAWHU7g5BEHe7u02oXcXFxSHs0YZHL6sCThGT3q/Sg93uldtBD3u/VPB+5\n/evyvV7l66pHvNcrUTIoIiIi0sA0Z1BERESkgSkZFBEREWlgSgZFREREGpiSQREREZEGpmRQRERE\npIHpPoMiDS5JkquAV9M0PTNJkonAwDRNJyZJ0h8YDuxC+ODYH0iBYWmaPhePbQW+lqap5+r8GNgl\nTdPxmW07EO6R9vs0TX+R278VeA34lPDtEE2Emxz/NE3TTzupnQOB29M0XaKD9ZwZf/w94T5jQ9I0\n/UeFY+Zd446cu54kSXIMsH6apj+sg1iWA76Tpulfah1LWyVJshrwKtCSpmnZ2/bE9+ewNE3HduL5\nvwosnKbpIx2sZ00gSdP0zs6JrHMlSfIi8L00TZ9LkmQQMClN0+fbUc9EKjwHSZKsAjwA7JCm6cvt\nDLlbqWdQpIElSXIQMIDMDWAzfkW4n9bmaZoOANYGHgduS5KkbztOdyThXmmHJElS7HfPXmmaDkjT\nNAE2A7YATm/HebpFmqYfAocCY5IkWbzUfhWucY+VpumF9ZAIRtsCP6h1ED3UoYT7F3bUnsBOnVBP\nl4i/W56LD08E1uvCc71O+CrHMSV+19Ud9QyK1EDsDXgFOBAYBqwMnAo0A4OBFuCkQk9HkiR7AmcA\nixNubjosTdN7YtnWwAhgCWBR4JI0Tc+JZeOBu4GtgQT4GNg3TdMXMj1/h6dpOrdImOsBT6Zp+hFA\nmqZzkiT5BfDHNE3ntLG9KxESy70IfzR2JNy8tag0TT9IkuRy4ChyCWGSJH8BJqdpemJmmwN/AkYR\neuy2AxYifA3ZkDRN/5OrYyC5XsJsb2aSJBsC5wMrEX5PXpym6YgicU5IkuRJ4Fjg10Xa/blrHJ/L\nnwOLAH2BX6ZpOjpJkrOBAWma7pk5fmw4TXpakiRHEm6a3B+YBhyVpunj8bX0X+AU4Ajg24TvSP0T\nsAbhNfH3GMOnSZIsSbiR7uaE3tgrgRGFa1HqPEXadiZgaZruEn9eC/gMGAh8Qnhtnw5sCrwL7Jam\n6VtJkowivIa/DKwPzAEOTNN0Qqz3DMI3hPQFPojn/1cs2zle54WB94CjgSWBS4BFkiR5Ok3Tz30d\nWJIkJxG+XaVPrPPENE0fia+DkYTvyD2M8DV1V6ZpemqROg4BDgb+BexOeH0dQEimto7t2DtN02eS\nJFmI8H21uxNu6D0JODrzvruI8B6YEp+L7Hm+BfyOcKPjVmB4mqZX5+MpEt8BwI9jXDOAn6dpelux\nnsckSZ4l/M74AuH3zcdJkiTAtcB1wHmxrSsAF6ZpekY8boGRgCRJbgcceJnwXM9NkmSNNE0XSArb\n+PpYEriU8J2/ixC+0WNwmqbT4rW7GNiN8N7+fbx+LcAGlHkuC7HH824PbJwkyQbARMLv0w3ifssT\nbh69ehwh+RHhd/THwAK9gUmSrApcAKxLeE3eAPwkvtdvAH4K7BOvaV3rERmrSC/VBHw5TdNNCL80\nzgdmpmm6ISFZ+BVAkiRfA64m/EJcC/gRcGOSJEslSdJE+KVzXpqm6wE7A79OkuQrmfMcAByQpuma\nwEuEpAHgW8BihK/qKuZm4LgkSS5JkmTnJEmWTtP0szRN3ymxfzlDgGvSNJ0NXEFIWirpD8wssv1a\nYK/YdpIkWYuQVIyJ59ka2JDwx2cS8Nu2BJokyWLAvcCYNE3XIXzF1AlJkmxb4pAbgP1LlC1wjZMk\nWYrwh+HI2Nv6I+CKJEmWje3aIUmSQlLWTOhpGZ0kyfeAXxCGndYkJBNjM70OfYCF0jRNYq/EucAb\naZquG6/NFsDhcd9TgeWAVQh/FAdn2l7pPOV8l5CorUH4tpLbCUnymoTkZEhm3x8Ax6dpujohkbs6\nSZKmJEl2JHwA+HqapmsD/0dIDAofKK4DfhB7jy8h/HF+CLgQGF8iEdwNOAHYLl7zPwA3x+cZQsLf\nJ03TTQkfIn4S/8gXswVwZ3xdPALcCVwQY32c0ONEbMN3YjvWJbyeboiv2QMJr9F1AQM2zsS6cqzz\nzHiO3YCL42u8pCRJNonXae/YxqPi+VYqd1z80PgYcE6apoXXRwvQNyZHXwVOTJKk7HdSp2l6LeH3\nxbX5RDCj2tfHaYREeH1gnfhzITkfTHhPrUX4MLN37hwVn8s0TY8D3gKOqzRtI173c4Ed0zRdn/B9\n1ivHsibgNsJXNCbAVwgfxA6J52kFbqT074a6omRQpLZujP//m5A0XJN5vEr8+XvAXWmaPgEQ5+Gl\nwM7xF86qhESIOAfmHcKQbsGtaZq+F39+PFPvVsCEEr2CpGl6OWG+4PKET9zvJkny9yJJ0dgkSV7M\n/ottASAOKR8W64DwC9TK/aGKZUcT5hjm3UPo7do8Pt4XGJem6Ydpml4CbJam6ax4bR7IXYtqbA30\ni3WRpukk4C/xPMU8AqyfJMkyRcoWuMZxaHmpNE3/Gcv/Ruh5XC0OYb1E+KMJIRF4Jk3TlPBH7+o0\nTV+L9Ywm9ARvznw3Z37ej/CHljRNPyH8wS9ch22B6+M1+pCQnBdUc55S/p2m6Uvxuj8LPJqm6Vux\n7c8y/3UHcG+mt/ZqQkKwcpqmdwGrxrgK16cQ907A82maFr779Rpgw1Kv34w9gOvSNH07tqnwmtos\n/t8PuCyWPUv4ALJKvpJoajp/Huy/gf+mafp05nHhuD2AyzPt+COh52pVYBDhPTk9xp79juRdgBfS\nOO8uTdMXCT3736/Qxt0IvyNejMdNIFzz9nzfbx/ChwDSNJ1I6FUe2I568qp9ffwE2CNN07lpmn4W\nz5997d4S3+uzCB8CstryXFZjW+CpdP7cwpGErzOE+Qngr9I0bU3DCMrlLPh74hGgbCJdLzRMLFJb\nhT8WcwDiL5TC48KHtWWAQTHJKmgm9O5AGKY6LPY6zSV8ss9+0Mt+T+ZswvAbhCGiKeWCS8NQdGE4\nutCTdVeSJGulafpG3G2vtPgCkoKdCUM2/wojUUAY/jmMBefRjU2SpLCAZDrhF2/+lz1pms5OkuQG\nQuIygTAM85N43lWA3yVJUuhtWZIwBNUWywDNueu9CGGxSDGT4/9fYsFrDblrHHvYhiVJsjchoS0k\nMoXn6xpCu64jtKswhLgMsG2SJLtk6p4T6387Ps6281vA6fF6zCEM9xWGGpchDP8WvJ75udx5Kvkw\n8/Mc4KPc4+w80+z5Cx9UlomvmxFJkmxJeB0syvxrszxhiBeAmERkX2elfAnILxR4D/gi4YPTjDRN\nsz3Q2fdIXrVt/BJhqLEQ6ydJksyM51yOkDgWTM38vAwwIPfaW5wwlFnOAueL3o3na6uZaZpOzzx+\nL8bVUdVeuw2B4XHYei7heS+895YBnsgcl33tQtuey2osx/zXZ2GqzLzXK2EYP/t7rT/zfx8Qf14y\nSZJF0zSd0YE4upySQZH69yZwd5qm++UL4pyn3wNbpGn6VNxWNsGrRpyb8x3g/9I0/R/M66U4Os5N\nWpcwZ6caQwnz1a7M1L8RMC5JkuGZnp3PJZVlXAtcnyTJFYRE5Z64fTRhftRGaZgfdxRhyCxvgeQk\nSZKFCYkHhOv9bhxu62wHxXi2TNP01ThU+Umm/DrgzNgzug1xyCnG9ERafC7barnHixGG4I4HrkjT\ndG6SJGMyu3xESJILsj20Jc/TyZbP/Fz4UPMe4bW8ImFe2vQkSXYi9noTEreWwkExsV6D8HyXM4lM\nMhuH95aP27tqdCx/zmbCB4pJhA8MS2X2/VLm5zcJvcFbtON8X8lta4nbC/N7s0lRueRukSRJFo89\nyhCen0JyOrcN9bTXrYQPLnvED37nMH+xR7nXbnvlP6hk27TAc5UkST/mv3bfjP9vnHbSHQ9qScPE\nIvXvJsJcsgQgSZKWJEluSJJkBcIvqo8I81aIk537E4b2KplC6R6f2YS5dr8rzK2Kc7r2IfzifLKa\nwON8nW1ZcAiTOLQ2A9ihmnry4jDYZ4QJ6NfFuYgQrse/YyK4EmHIpthK3zcIf/TWjY+HMv+P5mPA\nZ0mS7BfbsFCSJL9Pwu0oiin8MZ9cpCx/jZeK+70Wh89/DMwiPl9pmr4FPEqY3H9/mqaF3r4bgf3i\nc06SJGskSXJjkiSL8nkLEYbpPSaCWxKGeQuviYeBfZMk6Rsn6x+aObYt5+mIbTJzufYnLJJ5k3B9\nXoiJ4NKE3uP+SViQcQewbpIkhUTpe4Q5hXMJ13DpwjzSnJtimwq9ZAcQbmH0r05uU/6ch8UkEEJi\n7oTX3YPArkmSLB4T2uxcyrsJPYNbASRJskSSJCNzc4CLuYXwO2KdeFxhwdi9hCR6FmGhBkmS7M78\nBJxYtmzm8dxCTLFn+ZuE68z/t3c+IVpVYRj/KQNam7ZtdPssBSlId+WiRUGoJLQRJMZaRLuEQK12\nimSBRJugWSiSBmYRDiFTlDAxzSgaM/XiH4SIBDcVERTFtHjfi3c+52u+wZm89D2/3czl3vOeM+f7\n7nvO+zxnKv7mOY8AbY3mn6xMcvgQuSD5qz6fT7Fw7m6X9GAtWBdb6A1Cu88/ABuVxhHIOdfwJbC5\nGVdyXNYCVGVkitSjImmtpP3N90bxMPBr13cFwcmgMZ2ntGSjpCD8O+AL4KvSsp0jS6XXlK7WX0i3\n3ZuSHl/i0ReAxxZ7gdZu3TZyt+yypCDdz3uAJyKityTVj1Eyqfl5kWunGcxI0o9Gx9d2Yx4AXq5x\neod0xa6T9HH7xtJCHQEmJF0kX4A/1rU/SN3eC9XvWdIpeKFPHFuA2YjoLRHD3WN8nEzeb5Clr0nS\nBHGylZie6O1XRIxXvOerb2eA04u9ZEqn9hrwqdI1uoMs72+XtI80Jv1dMZyt9uaX2849cg54W9J1\n8oW+u35/CHiyxv1D4PWKcybSuPQM8L6kq6TDc2dp0MZJXdlPvdrNiPiETK4nqvy6l3SuruYL+t3q\n41S1uZV08c+T7u1pUvd7kdSVNbHeJh3IR+u+aXIHapZ/oRZXeyn9LukG3hERt0pbt480Kl0hTSuT\nrdtPkUaxz+rn34ERSZcrviO1+KKe86qk5jupvcj7CHi6/nb3wivAMUlz5GLpRWCTpGOkHvBbUlv7\nebUJNX+XwUly/r1HJudngCuSviF11c3nYY40tJyveDZwZ5cUUsqxtfr8PWl6GW9d30JqHjvPmvn5\n5Y6hMeb/QK2srwHPR0Tjdr1JHTp9/yLrNqpDp6OciJLOAl9HRL+jZRaMcReQtKYSk+aom7ciop+D\ndqXbHgN+i4iX/ov2zOBohQ5mX0165u5mcndufas60AlqAXgJOBwRPlrGGNNNasfgIPBGn/KaWQLl\nsT+Pkm7Ru+jiGEvaA0xLeqBK1c+R5TBjOk1JNW4qj2KCLPlPdi0RLHaSJqgP7ncgg+Bk0JghJiLG\nyJ2rzv6nj65SersxYFdE9HW1dnCMT5DlxzmytDVCllyN6ToTpLlkpqQCm1ioee0EkjaQkodnBzj6\nqBO4TGyMMcYYM8R4Z9AYY4wxZohxMmiMMcYYM8Q4GTTGGGOMGWKcDBpjjDHGDDFOBo0xxhhjhph/\nAGCLs+xs0A21AAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 576x684 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "uH4HgSi1cmah",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Lets try XG Booster and have a average of both the models"
      ]
    },
    {
      "metadata": {
        "id": "_DWiwNR3DV3H",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "import xgboost"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "WZ-9HGg2dO5y",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "def run_xgb(X_train, X_test, y_train, y_test, test_df_lgb):\n",
        "    params = {'objective': 'binary:logistic', \n",
        "              'booster' : \"gbtree\",\n",
        "              'eval_metric':'auc',\n",
        "              'eta': 0.225,\n",
        "              'max_depth': 10, \n",
        "              'subsample': 0.9500000000000001, #changed from 0.6\n",
        "              'colsample_bytree': 0.65,\n",
        "              'lambda':0.3,\n",
        "              'gamma':1.0,\n",
        "              'max_delta_step':3,\n",
        "              'min_child_weight':2.0,\n",
        "              #'alpha':0.001,\n",
        "              'random_state': 42,\n",
        "              'scale_pos_weight':10.15,\n",
        "              'max_bin':250, #changed from 450\n",
        "              'silent': True}\n",
        "    \n",
        "    tr_data = xgb.DMatrix(X_train, y_train)\n",
        "    va_data = xgb.DMatrix(X_test, y_test)\n",
        "    \n",
        "    watchlist = [(tr_data, 'train'), (va_data, 'valid')]\n",
        "    \n",
        "    model_xgb = xgb.train(params, tr_data, 271, watchlist, maximize=False, early_stopping_rounds = 10, verbose_eval=10)\n",
        "    \n",
        "    #cv = xgb.cv(params, tr_data, 271, nfold=5, early_stopping_rounds=10, verbose_eval=10)\n",
        "    \n",
        "    dtest = xgb.DMatrix(test_df_lgb)\n",
        "    xgb_pred_y = model_xgb.predict(dtest, ntree_limit=model_xgb.best_ntree_limit)\n",
        "    \n",
        "    return xgb_pred_y, model_xgb"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "RMTLxIi3duZG",
        "colab_type": "code",
        "outputId": "c4aaf35e-05a4-401e-da07-9acf908e89c3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 255
        }
      },
      "cell_type": "code",
      "source": [
        "# Training XGB\n",
        "pred_test_xgb, model_xgb = run_xgb(X_train, X_test, y_train, y_test, test_df_lgb)\n",
        "print(\"XGB Training Completed...\")"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[0]\ttrain-auc:0.99234\tvalid-auc:0.982184\n",
            "Multiple eval metrics have been passed: 'valid-auc' will be used for early stopping.\n",
            "\n",
            "Will train until valid-auc hasn't improved in 10 rounds.\n",
            "[10]\ttrain-auc:0.999855\tvalid-auc:0.996634\n",
            "[20]\ttrain-auc:0.999999\tvalid-auc:0.997267\n",
            "[30]\ttrain-auc:1\tvalid-auc:0.9975\n",
            "[40]\ttrain-auc:1\tvalid-auc:0.997854\n",
            "[50]\ttrain-auc:1\tvalid-auc:0.998005\n",
            "[60]\ttrain-auc:1\tvalid-auc:0.99795\n",
            "Stopping. Best iteration:\n",
            "[50]\ttrain-auc:1\tvalid-auc:0.998005\n",
            "\n",
            "XGB Training Completed...\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "2hwHP1t8dySW",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "dtest1 = xgb.DMatrix(X_test)\n",
        "y_pred_X = model_xgb.predict(dtest1)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "s3jQKKTXfYKN",
        "colab_type": "code",
        "outputId": "39c56475-c811-46a8-d296-8a80f6bd05c3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "cell_type": "code",
      "source": [
        "confusion_matrix(y_test,y_pred_X.round())"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[2706,   27],\n",
              "       [  15,  254]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 177
        }
      ]
    },
    {
      "metadata": {
        "id": "30bQf8owfvhW",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "test_df['FORECLOSURE']= pred_test_xgb"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "5Ha05j00iXmN",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "sub1 = test_df[['AGREEMENTID','FORECLOSURE']]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "H-vo2YCMiY8d",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "sub1.to_csv(\"XGB_Sub_V_8.csv\",index=False)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "gZqRgOjApPri",
        "colab_type": "code",
        "outputId": "98ade96c-463b-40a3-8a9f-a742f4214a8a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "pred_test[1]"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9992024940816789"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 179
        }
      ]
    },
    {
      "metadata": {
        "id": "Tvs3yTq9pSyB",
        "colab_type": "code",
        "outputId": "546d493b-b592-43d2-e92a-f4a9e4fd602f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "pred_test_xgb[1]"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9997217"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 180
        }
      ]
    },
    {
      "metadata": {
        "id": "1GPcyycAmirs",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "Combine = (pred_test * 0.7 + pred_test_xgb * 0.3 ) "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "g5KXpcZ7nfmU",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "test_df['FORECLOSURE']=Combine"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "kqm82PYBnh4M",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "sub_combine = test_df[['AGREEMENTID','FORECLOSURE']]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "AxeapW3Rnk5n",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "sub_combine.to_csv(\"XGB_LGB_Comb_V_2.csv\",index=False)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "ggKBcQMBY6sa",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Auto Hyper Parameter tunning with hyperopt for XGBooster"
      ]
    },
    {
      "metadata": {
        "id": "Wiul3-yAbH8f",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "from hyperopt import hp\n",
        "from hyperopt import fmin, tpe, hp, STATUS_OK, Trials\n",
        "\n",
        "import xgboost as xgb\n",
        "import sklearn\n",
        "\n",
        "from sklearn.metrics import log_loss\n",
        "from sklearn import preprocessing\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "See02QnHc6ta",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "def load_train(X,y):\n",
        "    train = X\n",
        "    labels = y\n",
        "    return train.values, labels.astype('int32')\n",
        "\n",
        "\n",
        "def load_test(X_test):\n",
        "    test = X_test\n",
        "    return test.values"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "hkmCZlFhdZ2v",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "def score(params):\n",
        "    print (\"Training with params : \")\n",
        "    print (params)\n",
        "    num_round = int(params['n_estimators'])\n",
        "    del params['n_estimators']\n",
        "    dtrain = xgb.DMatrix(X_train, label=y_train)\n",
        "    dvalid = xgb.DMatrix(X_test, label=y_test)\n",
        "    # watchlist = [(dvalid, 'eval'), (dtrain, 'train')]\n",
        "    model = xgb.train(params, dtrain, num_round)\n",
        "    predictions = model.predict(dvalid)\n",
        "    score = log_loss(y_test, predictions)\n",
        "    print (\"\\tScore {0}\\n\\n\".format(score))\n",
        "    return {'loss': score, 'status': STATUS_OK}"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "UKTxAqRBdcF_",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "def optimize(trials):\n",
        "    space = {\n",
        "             'n_estimators' : hp.quniform('n_estimators', 100, 1000, 1),\n",
        "             'eta' : hp.quniform('eta', 0.025, 0.5, 0.025),\n",
        "             'max_depth' : hp.choice('max_depth', np.arange(1, 14, dtype=int)),\n",
        "             'min_child_weight' : hp.quniform('min_child_weight', 1, 6, 1),\n",
        "             'subsample' : hp.quniform('subsample', 0.5, 1, 0.05),\n",
        "             'gamma' : hp.quniform('gamma', 0.5, 1, 0.05),\n",
        "             'colsample_bytree' : hp.quniform('colsample_bytree', 0.5, 1, 0.05),\n",
        "             'eval_metric': 'auc',\n",
        "             'objective': 'binary:logistic',\n",
        "             'nthread' : 6,\n",
        "             'silent' : 1\n",
        "             }\n",
        "\n",
        "    best = fmin(score, space, algo=tpe.suggest, trials=trials, max_evals=250)\n",
        "\n",
        "    print (best)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "szrtrdCNeN6i",
        "colab_type": "code",
        "outputId": "b643ba9e-a8ab-48f8-8baa-bf24218c1f76",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        }
      },
      "cell_type": "code",
      "source": [
        "X, y = load_train(X,y)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "error",
          "ename": "AttributeError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-407-760b102194b3>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mload_train\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m<ipython-input-404-378d78f7e645>\u001b[0m in \u001b[0;36mload_train\u001b[0;34m(X, y)\u001b[0m\n\u001b[1;32m      2\u001b[0m     \u001b[0mtrain\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m     \u001b[0mlabels\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mtrain\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabels\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'int32'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mAttributeError\u001b[0m: 'numpy.ndarray' object has no attribute 'values'"
          ]
        }
      ]
    },
    {
      "metadata": {
        "id": "yzXbs9Phejbb",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.20, random_state=10,stratify=y.values)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "7lJTsNR6dv55",
        "colab_type": "code",
        "outputId": "0e9959ca-c7b4-4b42-f2dc-f9e7581131b2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 7143
        }
      },
      "cell_type": "code",
      "source": [
        "trials = Trials()\n",
        "\n",
        "optimize(trials)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Training with params : \n",
            "{'colsample_bytree': 1.0, 'eta': 0.30000000000000004, 'eval_metric': 'auc', 'gamma': 0.75, 'max_depth': 4, 'min_child_weight': 4.0, 'n_estimators': 535.0, 'nthread': 6, 'objective': 'binary:logistic', 'silent': 1, 'subsample': 0.8500000000000001}\n",
            "\tScore 0.04791411970210309\n",
            "\n",
            "\n",
            "Training with params : \n",
            "{'colsample_bytree': 0.55, 'eta': 0.225, 'eval_metric': 'auc', 'gamma': 0.6000000000000001, 'max_depth': 12, 'min_child_weight': 4.0, 'n_estimators': 970.0, 'nthread': 6, 'objective': 'binary:logistic', 'silent': 1, 'subsample': 0.8}\n",
            "\tScore 0.046172723169486334\n",
            "\n",
            "\n",
            "Training with params : \n",
            "{'colsample_bytree': 0.65, 'eta': 0.17500000000000002, 'eval_metric': 'auc', 'gamma': 0.75, 'max_depth': 3, 'min_child_weight': 2.0, 'n_estimators': 165.0, 'nthread': 6, 'objective': 'binary:logistic', 'silent': 1, 'subsample': 0.75}\n",
            "\tScore 0.047327221268986086\n",
            "\n",
            "\n",
            "Training with params : \n",
            "{'colsample_bytree': 0.8500000000000001, 'eta': 0.1, 'eval_metric': 'auc', 'gamma': 0.6000000000000001, 'max_depth': 4, 'min_child_weight': 5.0, 'n_estimators': 564.0, 'nthread': 6, 'objective': 'binary:logistic', 'silent': 1, 'subsample': 0.6000000000000001}\n",
            "\tScore 0.04644946081135777\n",
            "\n",
            "\n",
            "Training with params : \n",
            "{'colsample_bytree': 0.9500000000000001, 'eta': 0.4, 'eval_metric': 'auc', 'gamma': 0.75, 'max_depth': 12, 'min_child_weight': 3.0, 'n_estimators': 789.0, 'nthread': 6, 'objective': 'binary:logistic', 'silent': 1, 'subsample': 0.7000000000000001}\n",
            "\tScore 0.05103106816343448\n",
            "\n",
            "\n",
            "Training with params : \n",
            "{'colsample_bytree': 0.5, 'eta': 0.325, 'eval_metric': 'auc', 'gamma': 0.8500000000000001, 'max_depth': 7, 'min_child_weight': 6.0, 'n_estimators': 238.0, 'nthread': 6, 'objective': 'binary:logistic', 'silent': 1, 'subsample': 0.75}\n",
            "\tScore 0.049619733107396764\n",
            "\n",
            "\n",
            "Training with params : \n",
            "{'colsample_bytree': 0.65, 'eta': 0.325, 'eval_metric': 'auc', 'gamma': 0.9, 'max_depth': 9, 'min_child_weight': 2.0, 'n_estimators': 228.0, 'nthread': 6, 'objective': 'binary:logistic', 'silent': 1, 'subsample': 0.5}\n",
            "\tScore 0.052451653617596905\n",
            "\n",
            "\n",
            "Training with params : \n",
            "{'colsample_bytree': 0.6000000000000001, 'eta': 0.125, 'eval_metric': 'auc', 'gamma': 0.8500000000000001, 'max_depth': 12, 'min_child_weight': 5.0, 'n_estimators': 797.0, 'nthread': 6, 'objective': 'binary:logistic', 'silent': 1, 'subsample': 0.9}\n",
            "\tScore 0.045822047014422294\n",
            "\n",
            "\n",
            "Training with params : \n",
            "{'colsample_bytree': 0.8, 'eta': 0.07500000000000001, 'eval_metric': 'auc', 'gamma': 0.65, 'max_depth': 2, 'min_child_weight': 3.0, 'n_estimators': 416.0, 'nthread': 6, 'objective': 'binary:logistic', 'silent': 1, 'subsample': 0.55}\n",
            "\tScore 0.054035799980742635\n",
            "\n",
            "\n",
            "Training with params : \n",
            "{'colsample_bytree': 0.65, 'eta': 0.275, 'eval_metric': 'auc', 'gamma': 0.75, 'max_depth': 11, 'min_child_weight': 3.0, 'n_estimators': 872.0, 'nthread': 6, 'objective': 'binary:logistic', 'silent': 1, 'subsample': 0.8500000000000001}\n",
            "\tScore 0.045492986530878826\n",
            "\n",
            "\n",
            "Training with params : \n",
            "{'colsample_bytree': 0.55, 'eta': 0.17500000000000002, 'eval_metric': 'auc', 'gamma': 0.9, 'max_depth': 8, 'min_child_weight': 5.0, 'n_estimators': 436.0, 'nthread': 6, 'objective': 'binary:logistic', 'silent': 1, 'subsample': 0.5}\n",
            "\tScore 0.050052519236736766\n",
            "\n",
            "\n",
            "Training with params : \n",
            "{'colsample_bytree': 0.6000000000000001, 'eta': 0.375, 'eval_metric': 'auc', 'gamma': 0.55, 'max_depth': 2, 'min_child_weight': 2.0, 'n_estimators': 278.0, 'nthread': 6, 'objective': 'binary:logistic', 'silent': 1, 'subsample': 0.65}\n",
            "\tScore 0.04764812930359754\n",
            "\n",
            "\n",
            "Training with params : \n",
            "{'colsample_bytree': 0.9, 'eta': 0.45, 'eval_metric': 'auc', 'gamma': 0.65, 'max_depth': 7, 'min_child_weight': 3.0, 'n_estimators': 589.0, 'nthread': 6, 'objective': 'binary:logistic', 'silent': 1, 'subsample': 0.9}\n",
            "\tScore 0.049187852939469416\n",
            "\n",
            "\n",
            "Training with params : \n",
            "{'colsample_bytree': 0.8500000000000001, 'eta': 0.30000000000000004, 'eval_metric': 'auc', 'gamma': 0.9, 'max_depth': 8, 'min_child_weight': 5.0, 'n_estimators': 117.0, 'nthread': 6, 'objective': 'binary:logistic', 'silent': 1, 'subsample': 0.65}\n",
            "\tScore 0.049243516291750694\n",
            "\n",
            "\n",
            "Training with params : \n",
            "{'colsample_bytree': 0.6000000000000001, 'eta': 0.42500000000000004, 'eval_metric': 'auc', 'gamma': 0.6000000000000001, 'max_depth': 7, 'min_child_weight': 5.0, 'n_estimators': 802.0, 'nthread': 6, 'objective': 'binary:logistic', 'silent': 1, 'subsample': 0.7000000000000001}\n",
            "\tScore 0.056555754158412824\n",
            "\n",
            "\n",
            "Training with params : \n",
            "{'colsample_bytree': 0.8, 'eta': 0.225, 'eval_metric': 'auc', 'gamma': 0.8500000000000001, 'max_depth': 13, 'min_child_weight': 6.0, 'n_estimators': 731.0, 'nthread': 6, 'objective': 'binary:logistic', 'silent': 1, 'subsample': 0.8500000000000001}\n",
            "\tScore 0.0472110300156747\n",
            "\n",
            "\n",
            "Training with params : \n",
            "{'colsample_bytree': 0.5, 'eta': 0.47500000000000003, 'eval_metric': 'auc', 'gamma': 0.8, 'max_depth': 8, 'min_child_weight': 6.0, 'n_estimators': 309.0, 'nthread': 6, 'objective': 'binary:logistic', 'silent': 1, 'subsample': 0.75}\n",
            "\tScore 0.04995176048081588\n",
            "\n",
            "\n",
            "Training with params : \n",
            "{'colsample_bytree': 0.9, 'eta': 0.17500000000000002, 'eval_metric': 'auc', 'gamma': 0.65, 'max_depth': 7, 'min_child_weight': 4.0, 'n_estimators': 152.0, 'nthread': 6, 'objective': 'binary:logistic', 'silent': 1, 'subsample': 0.55}\n",
            "\tScore 0.049064266613503396\n",
            "\n",
            "\n",
            "Training with params : \n",
            "{'colsample_bytree': 0.55, 'eta': 0.42500000000000004, 'eval_metric': 'auc', 'gamma': 0.9, 'max_depth': 1, 'min_child_weight': 1.0, 'n_estimators': 129.0, 'nthread': 6, 'objective': 'binary:logistic', 'silent': 1, 'subsample': 0.8500000000000001}\n",
            "\tScore 0.07177482766940577\n",
            "\n",
            "\n",
            "Training with params : \n",
            "{'colsample_bytree': 0.9500000000000001, 'eta': 0.30000000000000004, 'eval_metric': 'auc', 'gamma': 0.5, 'max_depth': 5, 'min_child_weight': 3.0, 'n_estimators': 759.0, 'nthread': 6, 'objective': 'binary:logistic', 'silent': 1, 'subsample': 0.75}\n",
            "\tScore 0.04886069663348988\n",
            "\n",
            "\n",
            "Training with params : \n",
            "{'colsample_bytree': 0.7000000000000001, 'eta': 0.025, 'eval_metric': 'auc', 'gamma': 1.0, 'max_depth': 11, 'min_child_weight': 1.0, 'n_estimators': 1000.0, 'nthread': 6, 'objective': 'binary:logistic', 'silent': 1, 'subsample': 1.0}\n",
            "\tScore 0.04133416524536754\n",
            "\n",
            "\n",
            "Training with params : \n",
            "{'colsample_bytree': 0.7000000000000001, 'eta': 0.025, 'eval_metric': 'auc', 'gamma': 0.9500000000000001, 'max_depth': 11, 'min_child_weight': 1.0, 'n_estimators': 972.0, 'nthread': 6, 'objective': 'binary:logistic', 'silent': 1, 'subsample': 1.0}\n",
            "\tScore 0.04111927075066022\n",
            "\n",
            "\n",
            "Training with params : \n",
            "{'colsample_bytree': 0.7000000000000001, 'eta': 0.025, 'eval_metric': 'auc', 'gamma': 1.0, 'max_depth': 11, 'min_child_weight': 1.0, 'n_estimators': 983.0, 'nthread': 6, 'objective': 'binary:logistic', 'silent': 1, 'subsample': 1.0}\n",
            "\tScore 0.04136667016110607\n",
            "\n",
            "\n",
            "Training with params : \n",
            "{'colsample_bytree': 0.75, 'eta': 0.025, 'eval_metric': 'auc', 'gamma': 1.0, 'max_depth': 6, 'min_child_weight': 1.0, 'n_estimators': 876.0, 'nthread': 6, 'objective': 'binary:logistic', 'silent': 1, 'subsample': 1.0}\n",
            "\tScore 0.04268666161998923\n",
            "\n",
            "\n",
            "Training with params : \n",
            "{'colsample_bytree': 0.75, 'eta': 0.05, 'eval_metric': 'auc', 'gamma': 0.9500000000000001, 'max_depth': 11, 'min_child_weight': 2.0, 'n_estimators': 660.0, 'nthread': 6, 'objective': 'binary:logistic', 'silent': 1, 'subsample': 0.9500000000000001}\n",
            "\tScore 0.04334494676111932\n",
            "\n",
            "\n",
            "Training with params : \n",
            "{'colsample_bytree': 0.7000000000000001, 'eta': 0.125, 'eval_metric': 'auc', 'gamma': 0.9500000000000001, 'max_depth': 11, 'min_child_weight': 1.0, 'n_estimators': 936.0, 'nthread': 6, 'objective': 'binary:logistic', 'silent': 1, 'subsample': 0.9500000000000001}\n",
            "\tScore 0.043448344820597846\n",
            "\n",
            "\n",
            "Training with params : \n",
            "{'colsample_bytree': 0.7000000000000001, 'eta': 0.05, 'eval_metric': 'auc', 'gamma': 0.9500000000000001, 'max_depth': 10, 'min_child_weight': 1.0, 'n_estimators': 906.0, 'nthread': 6, 'objective': 'binary:logistic', 'silent': 1, 'subsample': 0.9500000000000001}\n",
            "\tScore 0.04270374132993802\n",
            "\n",
            "\n",
            "Training with params : \n",
            "{'colsample_bytree': 0.8, 'eta': 0.125, 'eval_metric': 'auc', 'gamma': 1.0, 'max_depth': 11, 'min_child_weight': 2.0, 'n_estimators': 994.0, 'nthread': 6, 'objective': 'binary:logistic', 'silent': 1, 'subsample': 1.0}\n",
            "\tScore 0.043631985843504556\n",
            "\n",
            "\n",
            "Training with params : \n",
            "{'colsample_bytree': 0.7000000000000001, 'eta': 0.07500000000000001, 'eval_metric': 'auc', 'gamma': 0.9500000000000001, 'max_depth': 9, 'min_child_weight': 1.0, 'n_estimators': 680.0, 'nthread': 6, 'objective': 'binary:logistic', 'silent': 1, 'subsample': 0.9}\n",
            "\tScore 0.04344856833678374\n",
            "\n",
            "\n",
            "Training with params : \n",
            "{'colsample_bytree': 0.75, 'eta': 0.2, 'eval_metric': 'auc', 'gamma': 0.8, 'max_depth': 5, 'min_child_weight': 2.0, 'n_estimators': 843.0, 'nthread': 6, 'objective': 'binary:logistic', 'silent': 1, 'subsample': 0.9500000000000001}\n",
            "\tScore 0.04464296917788876\n",
            "\n",
            "\n",
            "Training with params : \n",
            "{'colsample_bytree': 0.65, 'eta': 0.025, 'eval_metric': 'auc', 'gamma': 1.0, 'max_depth': 10, 'min_child_weight': 1.0, 'n_estimators': 949.0, 'nthread': 6, 'objective': 'binary:logistic', 'silent': 1, 'subsample': 0.8}\n",
            "\tScore 0.04177660321947237\n",
            "\n",
            "\n",
            "Training with params : \n",
            "{'colsample_bytree': 0.8, 'eta': 0.15000000000000002, 'eval_metric': 'auc', 'gamma': 0.8, 'max_depth': 3, 'min_child_weight': 4.0, 'n_estimators': 494.0, 'nthread': 6, 'objective': 'binary:logistic', 'silent': 1, 'subsample': 0.8}\n",
            "\tScore 0.04487277914419738\n",
            "\n",
            "\n",
            "Training with params : \n",
            "{'colsample_bytree': 0.8500000000000001, 'eta': 0.07500000000000001, 'eval_metric': 'auc', 'gamma': 0.7000000000000001, 'max_depth': 4, 'min_child_weight': 2.0, 'n_estimators': 631.0, 'nthread': 6, 'objective': 'binary:logistic', 'silent': 1, 'subsample': 1.0}\n",
            "\tScore 0.043600460055113546\n",
            "\n",
            "\n",
            "Training with params : \n",
            "{'colsample_bytree': 0.6000000000000001, 'eta': 0.25, 'eval_metric': 'auc', 'gamma': 0.9500000000000001, 'max_depth': 11, 'min_child_weight': 1.0, 'n_estimators': 923.0, 'nthread': 6, 'objective': 'binary:logistic', 'silent': 1, 'subsample': 0.9}\n",
            "\tScore 0.046728362681835285\n",
            "\n",
            "\n",
            "Training with params : \n",
            "{'colsample_bytree': 0.7000000000000001, 'eta': 0.1, 'eval_metric': 'auc', 'gamma': 0.8500000000000001, 'max_depth': 1, 'min_child_weight': 4.0, 'n_estimators': 999.0, 'nthread': 6, 'objective': 'binary:logistic', 'silent': 1, 'subsample': 0.9500000000000001}\n",
            "\tScore 0.06608206344830668\n",
            "\n",
            "\n",
            "Training with params : \n",
            "{'colsample_bytree': 0.75, 'eta': 0.05, 'eval_metric': 'auc', 'gamma': 1.0, 'max_depth': 13, 'min_child_weight': 2.0, 'n_estimators': 719.0, 'nthread': 6, 'objective': 'binary:logistic', 'silent': 1, 'subsample': 1.0}\n",
            "\tScore 0.04282478391372333\n",
            "\n",
            "\n",
            "Training with params : \n",
            "{'colsample_bytree': 0.65, 'eta': 0.1, 'eval_metric': 'auc', 'gamma': 0.9, 'max_depth': 6, 'min_child_weight': 2.0, 'n_estimators': 523.0, 'nthread': 6, 'objective': 'binary:logistic', 'silent': 1, 'subsample': 0.8500000000000001}\n",
            "\tScore 0.04188536657641124\n",
            "\n",
            "\n",
            "Training with params : \n",
            "{'colsample_bytree': 0.65, 'eta': 0.35000000000000003, 'eval_metric': 'auc', 'gamma': 0.7000000000000001, 'max_depth': 3, 'min_child_weight': 1.0, 'n_estimators': 824.0, 'nthread': 6, 'objective': 'binary:logistic', 'silent': 1, 'subsample': 0.9}\n",
            "\tScore 0.04504512491616759\n",
            "\n",
            "\n",
            "Training with params : \n",
            "{'colsample_bytree': 0.8500000000000001, 'eta': 0.15000000000000002, 'eval_metric': 'auc', 'gamma': 0.8500000000000001, 'max_depth': 11, 'min_child_weight': 3.0, 'n_estimators': 394.0, 'nthread': 6, 'objective': 'binary:logistic', 'silent': 1, 'subsample': 0.7000000000000001}\n",
            "\tScore 0.047399185675831075\n",
            "\n",
            "\n",
            "Training with params : \n",
            "{'colsample_bytree': 0.55, 'eta': 0.225, 'eval_metric': 'auc', 'gamma': 0.7000000000000001, 'max_depth': 12, 'min_child_weight': 1.0, 'n_estimators': 870.0, 'nthread': 6, 'objective': 'binary:logistic', 'silent': 1, 'subsample': 0.8}\n",
            "\tScore 0.045559945164351856\n",
            "\n",
            "\n",
            "Training with params : \n",
            "{'colsample_bytree': 1.0, 'eta': 0.15000000000000002, 'eval_metric': 'auc', 'gamma': 0.8, 'max_depth': 4, 'min_child_weight': 2.0, 'n_estimators': 597.0, 'nthread': 6, 'objective': 'binary:logistic', 'silent': 1, 'subsample': 0.9500000000000001}\n",
            "\tScore 0.04376524441506494\n",
            "\n",
            "\n",
            "Training with params : \n",
            "{'colsample_bytree': 0.8, 'eta': 0.1, 'eval_metric': 'auc', 'gamma': 0.9, 'max_depth': 2, 'min_child_weight': 3.0, 'n_estimators': 703.0, 'nthread': 6, 'objective': 'binary:logistic', 'silent': 1, 'subsample': 1.0}\n",
            "\tScore 0.04615562156222028\n",
            "\n",
            "\n",
            "Training with params : \n",
            "{'colsample_bytree': 0.6000000000000001, 'eta': 0.2, 'eval_metric': 'auc', 'gamma': 0.9500000000000001, 'max_depth': 9, 'min_child_weight': 2.0, 'n_estimators': 764.0, 'nthread': 6, 'objective': 'binary:logistic', 'silent': 1, 'subsample': 0.65}\n",
            "\tScore 0.047235737902564245\n",
            "\n",
            "\n",
            "Training with params : \n",
            "{'colsample_bytree': 0.9, 'eta': 0.5, 'eval_metric': 'auc', 'gamma': 0.75, 'max_depth': 11, 'min_child_weight': 4.0, 'n_estimators': 365.0, 'nthread': 6, 'objective': 'binary:logistic', 'silent': 1, 'subsample': 0.9}\n",
            "\tScore 0.049574224580838246\n",
            "\n",
            "\n",
            "Training with params : \n",
            "{'colsample_bytree': 0.55, 'eta': 0.05, 'eval_metric': 'auc', 'gamma': 0.5, 'max_depth': 12, 'min_child_weight': 1.0, 'n_estimators': 460.0, 'nthread': 6, 'objective': 'binary:logistic', 'silent': 1, 'subsample': 0.55}\n",
            "\tScore 0.04426867878009398\n",
            "\n",
            "\n",
            "Training with params : \n",
            "{'colsample_bytree': 0.5, 'eta': 0.07500000000000001, 'eval_metric': 'auc', 'gamma': 0.8500000000000001, 'max_depth': 11, 'min_child_weight': 3.0, 'n_estimators': 204.0, 'nthread': 6, 'objective': 'binary:logistic', 'silent': 1, 'subsample': 0.8500000000000001}\n",
            "\tScore 0.043020652498359735\n",
            "\n",
            "\n",
            "Training with params : \n",
            "{'colsample_bytree': 0.6000000000000001, 'eta': 0.25, 'eval_metric': 'auc', 'gamma': 0.55, 'max_depth': 13, 'min_child_weight': 2.0, 'n_estimators': 900.0, 'nthread': 6, 'objective': 'binary:logistic', 'silent': 1, 'subsample': 0.8}\n",
            "\tScore 0.04705104564997846\n",
            "\n",
            "\n",
            "Training with params : \n",
            "{'colsample_bytree': 0.7000000000000001, 'eta': 0.325, 'eval_metric': 'auc', 'gamma': 0.9, 'max_depth': 10, 'min_child_weight': 5.0, 'n_estimators': 961.0, 'nthread': 6, 'objective': 'binary:logistic', 'silent': 1, 'subsample': 0.9500000000000001}\n",
            "\tScore 0.04521473574244026\n",
            "\n",
            "\n",
            "Training with params : \n",
            "{'colsample_bytree': 0.65, 'eta': 0.275, 'eval_metric': 'auc', 'gamma': 1.0, 'max_depth': 5, 'min_child_weight': 1.0, 'n_estimators': 837.0, 'nthread': 6, 'objective': 'binary:logistic', 'silent': 1, 'subsample': 0.6000000000000001}\n",
            "\tScore 0.046453332453454454\n",
            "\n",
            "\n",
            "Training with params : \n",
            "{'colsample_bytree': 0.8, 'eta': 0.025, 'eval_metric': 'auc', 'gamma': 0.9500000000000001, 'max_depth': 1, 'min_child_weight': 2.0, 'n_estimators': 792.0, 'nthread': 6, 'objective': 'binary:logistic', 'silent': 1, 'subsample': 0.6000000000000001}\n",
            "\tScore 0.09307248804479834\n",
            "\n",
            "\n",
            "Training with params : \n",
            "{'colsample_bytree': 0.8500000000000001, 'eta': 0.2, 'eval_metric': 'auc', 'gamma': 0.8, 'max_depth': 8, 'min_child_weight': 3.0, 'n_estimators': 617.0, 'nthread': 6, 'objective': 'binary:logistic', 'silent': 1, 'subsample': 0.9}\n",
            "\tScore 0.045670542087040876\n",
            "\n",
            "\n",
            "Training with params : \n",
            "{'colsample_bytree': 0.75, 'eta': 0.375, 'eval_metric': 'auc', 'gamma': 0.8500000000000001, 'max_depth': 2, 'min_child_weight': 6.0, 'n_estimators': 535.0, 'nthread': 6, 'objective': 'binary:logistic', 'silent': 1, 'subsample': 0.75}\n",
            "\tScore 0.0497539934321059\n",
            "\n",
            "\n",
            "Training with params : \n",
            "{'colsample_bytree': 1.0, 'eta': 0.125, 'eval_metric': 'auc', 'gamma': 1.0, 'max_depth': 6, 'min_child_weight': 4.0, 'n_estimators': 311.0, 'nthread': 6, 'objective': 'binary:logistic', 'silent': 1, 'subsample': 0.7000000000000001}\n",
            "\tScore 0.0463461146177414\n",
            "\n",
            "\n",
            "Training with params : \n",
            "{'colsample_bytree': 0.5, 'eta': 0.30000000000000004, 'eval_metric': 'auc', 'gamma': 0.6000000000000001, 'max_depth': 7, 'min_child_weight': 1.0, 'n_estimators': 765.0, 'nthread': 6, 'objective': 'binary:logistic', 'silent': 1, 'subsample': 0.5}\n",
            "\tScore 0.051177578193658486\n",
            "\n",
            "\n",
            "Training with params : \n",
            "{'colsample_bytree': 0.9500000000000001, 'eta': 0.17500000000000002, 'eval_metric': 'auc', 'gamma': 0.9, 'max_depth': 11, 'min_child_weight': 3.0, 'n_estimators': 883.0, 'nthread': 6, 'objective': 'binary:logistic', 'silent': 1, 'subsample': 0.8500000000000001}\n",
            "\tScore 0.04482744129293216\n",
            "\n",
            "\n",
            "Training with params : \n",
            "{'colsample_bytree': 0.65, 'eta': 0.07500000000000001, 'eval_metric': 'auc', 'gamma': 0.75, 'max_depth': 3, 'min_child_weight': 5.0, 'n_estimators': 997.0, 'nthread': 6, 'objective': 'binary:logistic', 'silent': 1, 'subsample': 1.0}\n",
            "\tScore 0.043926900116499724\n",
            "\n",
            "\n",
            "Training with params : \n",
            "{'colsample_bytree': 0.55, 'eta': 0.05, 'eval_metric': 'auc', 'gamma': 0.9500000000000001, 'max_depth': 4, 'min_child_weight': 1.0, 'n_estimators': 667.0, 'nthread': 6, 'objective': 'binary:logistic', 'silent': 1, 'subsample': 0.9}\n",
            "\tScore 0.04392288736584891\n",
            "\n",
            "\n",
            "Training with params : \n",
            "{'colsample_bytree': 0.7000000000000001, 'eta': 0.1, 'eval_metric': 'auc', 'gamma': 0.55, 'max_depth': 9, 'min_child_weight': 2.0, 'n_estimators': 967.0, 'nthread': 6, 'objective': 'binary:logistic', 'silent': 1, 'subsample': 0.9500000000000001}\n",
            "\tScore 0.045194812497415554\n",
            "\n",
            "\n",
            "Training with params : \n",
            "{'colsample_bytree': 0.6000000000000001, 'eta': 0.15000000000000002, 'eval_metric': 'auc', 'gamma': 0.65, 'max_depth': 11, 'min_child_weight': 1.0, 'n_estimators': 849.0, 'nthread': 6, 'objective': 'binary:logistic', 'silent': 1, 'subsample': 0.65}\n",
            "\tScore 0.04585755336617021\n",
            "\n",
            "\n",
            "Training with params : \n",
            "{'colsample_bytree': 0.75, 'eta': 0.42500000000000004, 'eval_metric': 'auc', 'gamma': 1.0, 'max_depth': 8, 'min_child_weight': 2.0, 'n_estimators': 920.0, 'nthread': 6, 'objective': 'binary:logistic', 'silent': 1, 'subsample': 1.0}\n",
            "\tScore 0.047478246391460435\n",
            "\n",
            "\n",
            "Training with params : \n",
            "{'colsample_bytree': 0.9, 'eta': 0.025, 'eval_metric': 'auc', 'gamma': 0.9, 'max_depth': 11, 'min_child_weight': 3.0, 'n_estimators': 575.0, 'nthread': 6, 'objective': 'binary:logistic', 'silent': 1, 'subsample': 0.8}\n",
            "\tScore 0.04425536807413322\n",
            "\n",
            "\n",
            "Training with params : \n",
            "{'colsample_bytree': 0.7000000000000001, 'eta': 0.17500000000000002, 'eval_metric': 'auc', 'gamma': 0.9500000000000001, 'max_depth': 5, 'min_child_weight': 1.0, 'n_estimators': 807.0, 'nthread': 6, 'objective': 'binary:logistic', 'silent': 1, 'subsample': 0.75}\n",
            "\tScore 0.045342813003263704\n",
            "\n",
            "\n",
            "Training with params : \n",
            "{'colsample_bytree': 0.8, 'eta': 0.45, 'eval_metric': 'auc', 'gamma': 0.7000000000000001, 'max_depth': 12, 'min_child_weight': 4.0, 'n_estimators': 738.0, 'nthread': 6, 'objective': 'binary:logistic', 'silent': 1, 'subsample': 0.8500000000000001}\n",
            "\tScore 0.050611068005489285\n",
            "\n",
            "\n",
            "Training with params : \n",
            "{'colsample_bytree': 0.65, 'eta': 0.275, 'eval_metric': 'auc', 'gamma': 0.8, 'max_depth': 13, 'min_child_weight': 6.0, 'n_estimators': 646.0, 'nthread': 6, 'objective': 'binary:logistic', 'silent': 1, 'subsample': 0.7000000000000001}\n",
            "\tScore 0.046900411397808005\n",
            "\n",
            "\n",
            "Training with params : \n",
            "{'colsample_bytree': 0.75, 'eta': 0.125, 'eval_metric': 'auc', 'gamma': 0.8500000000000001, 'max_depth': 7, 'min_child_weight': 2.0, 'n_estimators': 976.0, 'nthread': 6, 'objective': 'binary:logistic', 'silent': 1, 'subsample': 0.9500000000000001}\n",
            "\tScore 0.04486574844967535\n",
            "\n",
            "\n",
            "Training with params : \n",
            "{'colsample_bytree': 0.7000000000000001, 'eta': 0.05, 'eval_metric': 'auc', 'gamma': 1.0, 'max_depth': 11, 'min_child_weight': 1.0, 'n_estimators': 942.0, 'nthread': 6, 'objective': 'binary:logistic', 'silent': 1, 'subsample': 1.0}\n",
            "\tScore 0.041553465932435\n",
            "\n",
            "\n",
            "Training with params : \n",
            "{'colsample_bytree': 0.65, 'eta': 0.025, 'eval_metric': 'auc', 'gamma': 1.0, 'max_depth': 11, 'min_child_weight': 1.0, 'n_estimators': 896.0, 'nthread': 6, 'objective': 'binary:logistic', 'silent': 1, 'subsample': 1.0}\n",
            "\tScore 0.041106028432088736\n",
            "\n",
            "\n",
            "Training with params : \n",
            "{'colsample_bytree': 0.65, 'eta': 0.07500000000000001, 'eval_metric': 'auc', 'gamma': 1.0, 'max_depth': 11, 'min_child_weight': 1.0, 'n_estimators': 897.0, 'nthread': 6, 'objective': 'binary:logistic', 'silent': 1, 'subsample': 0.9500000000000001}\n",
            "\tScore 0.042360259920469275\n",
            "\n",
            "\n",
            "Training with params : \n",
            "{'colsample_bytree': 0.6000000000000001, 'eta': 0.05, 'eval_metric': 'auc', 'gamma': 0.9500000000000001, 'max_depth': 11, 'min_child_weight': 1.0, 'n_estimators': 998.0, 'nthread': 6, 'objective': 'binary:logistic', 'silent': 1, 'subsample': 0.9500000000000001}\n",
            "\tScore 0.04166794451036094\n",
            "\n",
            "\n",
            "Training with params : \n",
            "{'colsample_bytree': 0.65, 'eta': 0.025, 'eval_metric': 'auc', 'gamma': 1.0, 'max_depth': 11, 'min_child_weight': 1.0, 'n_estimators': 862.0, 'nthread': 6, 'objective': 'binary:logistic', 'silent': 1, 'subsample': 1.0}\n",
            "\tScore 0.041082598174372854\n",
            "\n",
            "\n",
            "Training with params : \n",
            "{'colsample_bytree': 0.55, 'eta': 0.1, 'eval_metric': 'auc', 'gamma': 0.9500000000000001, 'max_depth': 6, 'min_child_weight': 1.0, 'n_estimators': 692.0, 'nthread': 6, 'objective': 'binary:logistic', 'silent': 1, 'subsample': 1.0}\n",
            "\tScore 0.04377193229296821\n",
            "\n",
            "\n",
            "Training with params : \n",
            "{'colsample_bytree': 0.6000000000000001, 'eta': 0.07500000000000001, 'eval_metric': 'auc', 'gamma': 0.9, 'max_depth': 1, 'min_child_weight': 2.0, 'n_estimators': 852.0, 'nthread': 6, 'objective': 'binary:logistic', 'silent': 1, 'subsample': 0.9}\n",
            "\tScore 0.07117104837251113\n",
            "\n",
            "\n",
            "Training with params : \n",
            "{'colsample_bytree': 0.65, 'eta': 0.025, 'eval_metric': 'auc', 'gamma': 1.0, 'max_depth': 11, 'min_child_weight': 1.0, 'n_estimators': 775.0, 'nthread': 6, 'objective': 'binary:logistic', 'silent': 1, 'subsample': 1.0}\n",
            "\tScore 0.041106276059839936\n",
            "\n",
            "\n",
            "Training with params : \n",
            "{'colsample_bytree': 0.5, 'eta': 0.125, 'eval_metric': 'auc', 'gamma': 1.0, 'max_depth': 10, 'min_child_weight': 1.0, 'n_estimators': 742.0, 'nthread': 6, 'objective': 'binary:logistic', 'silent': 1, 'subsample': 0.9}\n",
            "\tScore 0.04146496965031271\n",
            "\n",
            "\n",
            "Training with params : \n",
            "{'colsample_bytree': 0.55, 'eta': 0.225, 'eval_metric': 'auc', 'gamma': 0.9, 'max_depth': 2, 'min_child_weight': 2.0, 'n_estimators': 812.0, 'nthread': 6, 'objective': 'binary:logistic', 'silent': 1, 'subsample': 1.0}\n",
            "\tScore 0.0457082583555536\n",
            "\n",
            "\n",
            "Training with params : \n",
            "{'colsample_bytree': 0.65, 'eta': 0.025, 'eval_metric': 'auc', 'gamma': 1.0, 'max_depth': 11, 'min_child_weight': 1.0, 'n_estimators': 780.0, 'nthread': 6, 'objective': 'binary:logistic', 'silent': 1, 'subsample': 0.9500000000000001}\n",
            "\tScore 0.04165514634221792\n",
            "\n",
            "\n",
            "Training with params : \n",
            "{'colsample_bytree': 0.6000000000000001, 'eta': 0.05, 'eval_metric': 'auc', 'gamma': 0.9500000000000001, 'max_depth': 3, 'min_child_weight': 2.0, 'n_estimators': 716.0, 'nthread': 6, 'objective': 'binary:logistic', 'silent': 1, 'subsample': 1.0}\n",
            "\tScore 0.0454705503702084\n",
            "\n",
            "\n",
            "Training with params : \n",
            "{'colsample_bytree': 0.6000000000000001, 'eta': 0.025, 'eval_metric': 'auc', 'gamma': 0.8500000000000001, 'max_depth': 11, 'min_child_weight': 1.0, 'n_estimators': 613.0, 'nthread': 6, 'objective': 'binary:logistic', 'silent': 1, 'subsample': 0.8500000000000001}\n",
            "\tScore 0.041516524753422865\n",
            "\n",
            "\n",
            "Training with params : \n",
            "{'colsample_bytree': 0.55, 'eta': 0.4, 'eval_metric': 'auc', 'gamma': 0.9500000000000001, 'max_depth': 4, 'min_child_weight': 2.0, 'n_estimators': 552.0, 'nthread': 6, 'objective': 'binary:logistic', 'silent': 1, 'subsample': 0.9}\n",
            "\tScore 0.0462029701384591\n",
            "\n",
            "\n",
            "Training with params : \n",
            "{'colsample_bytree': 0.65, 'eta': 0.07500000000000001, 'eval_metric': 'auc', 'gamma': 1.0, 'max_depth': 9, 'min_child_weight': 1.0, 'n_estimators': 476.0, 'nthread': 6, 'objective': 'binary:logistic', 'silent': 1, 'subsample': 0.9500000000000001}\n",
            "\tScore 0.04189575199840102\n",
            "\n",
            "\n",
            "Training with params : \n",
            "{'colsample_bytree': 0.5, 'eta': 0.1, 'eval_metric': 'auc', 'gamma': 0.9, 'max_depth': 11, 'min_child_weight': 2.0, 'n_estimators': 859.0, 'nthread': 6, 'objective': 'binary:logistic', 'silent': 1, 'subsample': 0.9500000000000001}\n",
            "\tScore 0.04177013062394519\n",
            "\n",
            "\n",
            "Training with params : \n",
            "{'colsample_bytree': 0.65, 'eta': 0.15000000000000002, 'eval_metric': 'auc', 'gamma': 1.0, 'max_depth': 13, 'min_child_weight': 1.0, 'n_estimators': 915.0, 'nthread': 6, 'objective': 'binary:logistic', 'silent': 1, 'subsample': 1.0}\n",
            "\tScore 0.042284436654948375\n",
            "\n",
            "\n",
            "Training with params : \n",
            "{'colsample_bytree': 0.7000000000000001, 'eta': 0.5, 'eval_metric': 'auc', 'gamma': 0.9500000000000001, 'max_depth': 8, 'min_child_weight': 2.0, 'n_estimators': 825.0, 'nthread': 6, 'objective': 'binary:logistic', 'silent': 1, 'subsample': 0.8500000000000001}\n",
            "\tScore 0.05021243167380762\n",
            "\n",
            "\n",
            "Training with params : \n",
            "{'colsample_bytree': 0.6000000000000001, 'eta': 0.2, 'eval_metric': 'auc', 'gamma': 0.9, 'max_depth': 12, 'min_child_weight': 3.0, 'n_estimators': 882.0, 'nthread': 6, 'objective': 'binary:logistic', 'silent': 1, 'subsample': 1.0}\n",
            " 33%|███▎      | 83/250 [2:46:39<6:54:38, 148.98s/it, best loss: 0.041082598174372854]"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "rEZqU7YXd4KU",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}