Signed_or_not_notext.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Signed_or_not_notext.ipynb",
      "version": "0.3.2",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/juanluisrosaramos/document_classification/blob/master/Signed_or_not_notext.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "metadata": {
        "id": "TjEGo-CGQpZv",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "#from google.colab import auth\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np # linear algebra\n",
        "from __future__ import print_function\n",
        "import pandas as pd\n",
        "from scipy.stats import spearmanr\n",
        "import warnings\n",
        "warnings.filterwarnings(\"ignore\")\n",
        "from sklearn import ensemble\n",
        "\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "OtCczrhH2v8O",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Descriptive analysis on the Data"
      ]
    },
    {
      "metadata": {
        "id": "ZcRl43dsjoB8",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "Download csv and create pandas frames with Train data and Test data.\n",
        "\n",
        "*   Train rows and columns :  (132637, 56)\n",
        "*   Test rows and columns :  (10000, 55)\n",
        "*   Target:  Binary with no outliers in the target distribution\n",
        "\n",
        "\n"
      ]
    },
    {
      "metadata": {
        "id": "kCjYbc_dRPtl",
        "colab_type": "code",
        "outputId": "99e7c9f6-62ee-4217-c0ea-522bfa2d93bb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 4403
        }
      },
      "cell_type": "code",
      "source": [
        "#train_df = pd.read_csv(\"train.csv\", sep=\",\")\n",
        "#test_df = pd.read_csv(\"test.csv\", sep=\",\")\n",
        "\n",
        "def explore_dataset(df):\n",
        "  print ('\\n****** dtypes *******\\n',df.dtypes) \n",
        "  print ('\\n****** Floats values example *******\\n',df.select_dtypes(['float64']).loc[65,:])\n",
        "  print ('\\n****** Integers values example *******\\n',df.select_dtypes(['int64']).loc[65,:])\n",
        "  print ('\\n****** Object values example *******\\n',df.select_dtypes(['object']).loc[5,:])\n",
        "  index = df.index\n",
        "  print ('\\n****** Row numbers *******\\n',index)\n",
        "  #df.iloc[2:115, :5]\n",
        "  #print ('\\n****** Head *******\\n',df.head())\n",
        "  print ('\\n****** Shape *******\\n',df.shape)\n",
        "\n",
        "data = [train_df, test_df]\n",
        "\n",
        "for dataset in data:\n",
        "  explore_dataset(dataset)\n"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            "****** dtypes *******\n",
            " Unnamed: 0                                  int64\n",
            "id                                          int64\n",
            "Account_age_days                          float64\n",
            "Country_CAT                                 int64\n",
            "Currency_CAT                                int64\n",
            "Day_of_the_month                            int64\n",
            "Day_of_the_week                             int64\n",
            "Dimensions_height                         float64\n",
            "Dimensions_width                          float64\n",
            "Expire_time_days                          float64\n",
            "First_pending_reminders                   float64\n",
            "Has_body                                    int64\n",
            "Has_protection                            float64\n",
            "Has_signature_position                      int64\n",
            "Has_signer_name                             int64\n",
            "Has_signer_phone                            int64\n",
            "Has_subject                                 int64\n",
            "Has_template                                int64\n",
            "Has_terms_and_conditions                    int64\n",
            "Highest_page                              float64\n",
            "In_person                                 float64\n",
            "License_tier_CAT                            int64\n",
            "Limit_quantity                            float64\n",
            "Lowest_page                               float64\n",
            "Method_CAT                                  int64\n",
            "Month                                       int64\n",
            "Non_customer_enterprise_signer              int64\n",
            "Num_of_pending_reminders                    int64\n",
            "Num_of_required_page                        int64\n",
            "Num_of_signatures                         float64\n",
            "Num_of_widgets                            float64\n",
            "Original_sender_mail_domain_CAT             int64\n",
            "Original_sender_mail_region_CAT             int64\n",
            "Original_sender_mail_username_CAT           int64\n",
            "Original_sender_vs_signer_same_address    float64\n",
            "Original_sender_vs_signer_same_domain     float64\n",
            "Original_size                             float64\n",
            "Pages                                     float64\n",
            "Paid_per_year                             float64\n",
            "Part                                        int64\n",
            "Raw_text                                   object\n",
            "Repaired                                  float64\n",
            "Required_audio                              int64\n",
            "Required_photo_ids                          int64\n",
            "Required_photos                             int64\n",
            "Seat_admin_role                             int64\n",
            "Signer_mail_domain_CAT                      int64\n",
            "Signer_mail_region_CAT                      int64\n",
            "Signer_mail_username_CAT                    int64\n",
            "Size                                        int64\n",
            "Source_CAT                                  int64\n",
            "Time_of_the_day_seconds                     int64\n",
            "Type_CAT                                    int64\n",
            "User_age_days                             float64\n",
            "Zipcode                                   float64\n",
            "Target_sign                                 int64\n",
            "dtype: object\n",
            "\n",
            "****** Floats values example *******\n",
            " Account_age_days                              825.0\n",
            "Dimensions_height                             842.0\n",
            "Dimensions_width                              595.0\n",
            "Expire_time_days                                NaN\n",
            "First_pending_reminders                         NaN\n",
            "Has_protection                                  0.0\n",
            "Highest_page                                    NaN\n",
            "In_person                                       1.0\n",
            "Limit_quantity                                  NaN\n",
            "Lowest_page                                     NaN\n",
            "Num_of_signatures                               NaN\n",
            "Num_of_widgets                                  NaN\n",
            "Original_sender_vs_signer_same_address          0.0\n",
            "Original_sender_vs_signer_same_domain           0.0\n",
            "Original_size                             1244481.0\n",
            "Pages                                          16.0\n",
            "Paid_per_year                                   NaN\n",
            "Repaired                                        1.0\n",
            "User_age_days                                 298.0\n",
            "Zipcode                                         NaN\n",
            "Name: 65, dtype: float64\n",
            "\n",
            "****** Integers values example *******\n",
            " Unnamed: 0                              5265\n",
            "id                                      5265\n",
            "Country_CAT                                0\n",
            "Currency_CAT                               0\n",
            "Day_of_the_month                           3\n",
            "Day_of_the_week                            0\n",
            "Has_body                                   1\n",
            "Has_signature_position                     0\n",
            "Has_signer_name                            1\n",
            "Has_signer_phone                           0\n",
            "Has_subject                                0\n",
            "Has_template                               0\n",
            "Has_terms_and_conditions                   1\n",
            "License_tier_CAT                           5\n",
            "Method_CAT                                 1\n",
            "Month                                      9\n",
            "Non_customer_enterprise_signer             0\n",
            "Num_of_pending_reminders                   0\n",
            "Num_of_required_page                       0\n",
            "Original_sender_mail_domain_CAT            8\n",
            "Original_sender_mail_region_CAT            0\n",
            "Original_sender_mail_username_CAT          7\n",
            "Part                                       0\n",
            "Required_audio                             0\n",
            "Required_photo_ids                         0\n",
            "Required_photos                            0\n",
            "Seat_admin_role                            1\n",
            "Signer_mail_domain_CAT                     9\n",
            "Signer_mail_region_CAT                     0\n",
            "Signer_mail_username_CAT                  41\n",
            "Size                                 1338105\n",
            "Source_CAT                                 0\n",
            "Time_of_the_day_seconds                44017\n",
            "Type_CAT                                   0\n",
            "Target_sign                                0\n",
            "Name: 65, dtype: int64\n",
            "\n",
            "****** Object values example *******\n",
            " Raw_text    de marca a sucursal en de sucursal en de defen...\n",
            "Name: 5, dtype: object\n",
            "\n",
            "****** Row numbers *******\n",
            " RangeIndex(start=0, stop=132637, step=1)\n",
            "\n",
            "****** Shape *******\n",
            " (132637, 56)\n",
            "\n",
            "****** dtypes *******\n",
            " Unnamed: 0                                  int64\n",
            "id                                          int64\n",
            "Account_age_days                          float64\n",
            "Country_CAT                                 int64\n",
            "Currency_CAT                                int64\n",
            "Day_of_the_month                            int64\n",
            "Day_of_the_week                             int64\n",
            "Dimensions_height                         float64\n",
            "Dimensions_width                          float64\n",
            "Expire_time_days                          float64\n",
            "First_pending_reminders                   float64\n",
            "Has_body                                    int64\n",
            "Has_protection                            float64\n",
            "Has_signature_position                      int64\n",
            "Has_signer_name                             int64\n",
            "Has_signer_phone                            int64\n",
            "Has_subject                                 int64\n",
            "Has_template                                int64\n",
            "Has_terms_and_conditions                    int64\n",
            "Highest_page                              float64\n",
            "In_person                                 float64\n",
            "License_tier_CAT                            int64\n",
            "Limit_quantity                            float64\n",
            "Lowest_page                               float64\n",
            "Method_CAT                                  int64\n",
            "Month                                       int64\n",
            "Non_customer_enterprise_signer              int64\n",
            "Num_of_pending_reminders                    int64\n",
            "Num_of_required_page                        int64\n",
            "Num_of_signatures                         float64\n",
            "Num_of_widgets                            float64\n",
            "Original_sender_mail_domain_CAT             int64\n",
            "Original_sender_mail_region_CAT             int64\n",
            "Original_sender_mail_username_CAT           int64\n",
            "Original_sender_vs_signer_same_address    float64\n",
            "Original_sender_vs_signer_same_domain     float64\n",
            "Original_size                             float64\n",
            "Pages                                     float64\n",
            "Paid_per_year                             float64\n",
            "Part                                        int64\n",
            "Raw_text                                   object\n",
            "Repaired                                  float64\n",
            "Required_audio                              int64\n",
            "Required_photo_ids                          int64\n",
            "Required_photos                             int64\n",
            "Seat_admin_role                             int64\n",
            "Signer_mail_domain_CAT                      int64\n",
            "Signer_mail_region_CAT                      int64\n",
            "Signer_mail_username_CAT                    int64\n",
            "Size                                        int64\n",
            "Source_CAT                                  int64\n",
            "Time_of_the_day_seconds                     int64\n",
            "Type_CAT                                    int64\n",
            "User_age_days                             float64\n",
            "Zipcode                                   float64\n",
            "dtype: object\n",
            "\n",
            "****** Floats values example *******\n",
            " Account_age_days                             577.00\n",
            "Dimensions_height                            841.89\n",
            "Dimensions_width                             595.28\n",
            "Expire_time_days                                NaN\n",
            "First_pending_reminders                    86400.00\n",
            "Has_protection                                 0.00\n",
            "Highest_page                                   0.00\n",
            "In_person                                      0.00\n",
            "Limit_quantity                                  NaN\n",
            "Lowest_page                                    0.00\n",
            "Num_of_signatures                              1.00\n",
            "Num_of_widgets                                 1.00\n",
            "Original_sender_vs_signer_same_address         0.00\n",
            "Original_sender_vs_signer_same_domain          0.00\n",
            "Original_size                             388015.00\n",
            "Pages                                          1.00\n",
            "Paid_per_year                             163368.00\n",
            "Repaired                                       1.00\n",
            "User_age_days                                577.00\n",
            "Zipcode                                         NaN\n",
            "Name: 65, dtype: float64\n",
            "\n",
            "****** Integers values example *******\n",
            " Unnamed: 0                            76121\n",
            "id                                    76121\n",
            "Country_CAT                               1\n",
            "Currency_CAT                              1\n",
            "Day_of_the_month                         18\n",
            "Day_of_the_week                           1\n",
            "Has_body                                  1\n",
            "Has_signature_position                    0\n",
            "Has_signer_name                           1\n",
            "Has_signer_phone                          0\n",
            "Has_subject                               1\n",
            "Has_template                              1\n",
            "Has_terms_and_conditions                  0\n",
            "License_tier_CAT                          4\n",
            "Method_CAT                                2\n",
            "Month                                     9\n",
            "Non_customer_enterprise_signer            0\n",
            "Num_of_pending_reminders                 89\n",
            "Num_of_required_page                      0\n",
            "Original_sender_mail_domain_CAT         119\n",
            "Original_sender_mail_region_CAT           1\n",
            "Original_sender_mail_username_CAT       122\n",
            "Part                                      0\n",
            "Required_audio                            0\n",
            "Required_photo_ids                        0\n",
            "Required_photos                           0\n",
            "Seat_admin_role                           1\n",
            "Signer_mail_domain_CAT                    2\n",
            "Signer_mail_region_CAT                    0\n",
            "Signer_mail_username_CAT              29221\n",
            "Size                                 438300\n",
            "Source_CAT                                1\n",
            "Time_of_the_day_seconds               30725\n",
            "Type_CAT                                  0\n",
            "Name: 65, dtype: int64\n",
            "\n",
            "****** Object values example *******\n",
            " Raw_text    de zona general de plataforma a cuadrante mese...\n",
            "Name: 5, dtype: object\n",
            "\n",
            "****** Row numbers *******\n",
            " RangeIndex(start=0, stop=10000, step=1)\n",
            "\n",
            "****** Shape *******\n",
            " (10000, 55)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "a9OhDHM3-BSt",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Let's explore the target field.\n",
        "It's a binary called Target_sign with no outliers and 67% of signed documents."
      ]
    },
    {
      "metadata": {
        "id": "ps93N6T7-GWC",
        "colab_type": "code",
        "outputId": "2422b1c9-7361-4164-a98b-1f7c64196e19",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 396
        }
      },
      "cell_type": "code",
      "source": [
        "count_Class=pd.value_counts(train_df['Target_sign'], sort= True)\n",
        "print (count_Class)\n",
        "count_Class.plot(kind='pie',  autopct='%1.0f%%')\n",
        "plt.title('Pie chart')\n",
        "plt.show()\n"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "1    89150\n",
            "0    43487\n",
            "Name: Target_sign, dtype: int64\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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AXV1dvsgmBE8fk0g5s+pxqr9FdAwiISYkjxMdQRFDFu1LL72E9evXo6CgALt2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\niIgUxKIlIiJSEIuWiIhIQSxaIiIiBf1/w43Un1ourwAAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f50cb8f2eb8>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "UIJEbW33j4sv",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Missing values\n",
        "Let's look for missing values in the columns the percentages against the total values of the column"
      ]
    },
    {
      "metadata": {
        "id": "FkwnXU41RqKQ",
        "colab_type": "code",
        "outputId": "65d3d720-14e3-414c-ff3f-f4a05a646cde",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 595
        }
      },
      "cell_type": "code",
      "source": [
        "def missing_values(df):\n",
        "  #Looking for missing values \n",
        "  total = df.isnull().sum().sort_values(ascending=True)\n",
        "  percent_1 = df.isnull().sum()/df.isnull().count()*100\n",
        "  percent_2 = (round(percent_1, 1)).sort_values(ascending=False)\n",
        "  missing_data = pd.concat([total, percent_2], axis=1, keys=['Total', '%'])\n",
        "  #Keep only when values > 0\n",
        "  missing_data = missing_data[(missing_data > 0).any(axis=1)]\n",
        "  return missing_data\n",
        " \n",
        "for dataset in data:\n",
        "  dataset = missing_values(dataset)\n",
        "  print('****** Dataset missing values *********\\n',dataset.head(50))\n",
        "  \n",
        "  \n",
        "  "
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "****** Dataset missing values *********\n",
            "                                          Total      %\n",
            "Dimensions_height                           15    0.0\n",
            "Dimensions_width                            15    0.0\n",
            "Expire_time_days                        101546   76.6\n",
            "First_pending_reminders                  79071   59.6\n",
            "Highest_page                             94680   71.4\n",
            "In_person                                   23    0.0\n",
            "Limit_quantity                          132637  100.0\n",
            "Lowest_page                              94680   71.4\n",
            "Num_of_signatures                        94680   71.4\n",
            "Num_of_widgets                           94680   71.4\n",
            "Original_sender_vs_signer_same_address    2144    1.6\n",
            "Original_sender_vs_signer_same_domain     2144    1.6\n",
            "Original_size                                2    0.0\n",
            "Pages                                       13    0.0\n",
            "Paid_per_year                           115209   86.9\n",
            "Repaired                                  3817    2.9\n",
            "Zipcode                                 132637  100.0\n",
            "****** Dataset missing values *********\n",
            "                                         Total      %\n",
            "Expire_time_days                         7578   75.8\n",
            "First_pending_reminders                  5925   59.2\n",
            "Highest_page                             7182   71.8\n",
            "In_person                                   4    0.0\n",
            "Limit_quantity                          10000  100.0\n",
            "Lowest_page                              7182   71.8\n",
            "Num_of_signatures                        7182   71.8\n",
            "Num_of_widgets                           7182   71.8\n",
            "Original_sender_vs_signer_same_address    178    1.8\n",
            "Original_sender_vs_signer_same_domain     178    1.8\n",
            "Paid_per_year                            8734   87.3\n",
            "Repaired                                  309    3.1\n",
            "Zipcode                                 10000  100.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "Fl0axBzEkbn6",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Remove columns\n",
        "The similar values are founded in the Train and Test partition. Taking into account the descriptive name of the field  and the percentage of missing fields I decide to eliminate columns with a percentage of missing greater than 70%.\n",
        "\n",
        "So, we drop those 8 columns: **Zipcode, Paid_per_year, Num_of_widgets, Num_of_signatures, Lowest_page, Limit_quantity, Highest_page, Expire_time_days**\n",
        "\n",
        "At that point I decide to \"divide and conquer\" the problem and make an analysis of the raw text in other notebook to see if it's useful for the final classification. So We will also remove the column **Raw_text ** and will not use the text for this experiments."
      ]
    },
    {
      "metadata": {
        "id": "GYIXHEvXYpLN",
        "colab_type": "code",
        "outputId": "c1f9e71c-3976-4649-d5cf-e556dacc458c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 119
        }
      },
      "cell_type": "code",
      "source": [
        "#Function that return fields with % of missing \n",
        "def missing_values(df, percentage):\n",
        "    columns = df.columns\n",
        "    percent_missing = df.isnull().sum() * 100 / len(df)\n",
        "    missing_value_df = pd.DataFrame({'column_name': columns,\n",
        "                                 'percent_missing': percent_missing})\n",
        "\n",
        "    missing_drop = list(missing_value_df[missing_value_df.percent_missing>percentage].column_name)\n",
        "    print('Columns to work with:',missing_drop)\n",
        "    return missing_drop    \n",
        "    \n",
        "#Remove columns of both datasets\n",
        "for dataset in data:\n",
        "   print(\"Rows and columns:\", dataset.shape)\n",
        "   dataset.drop(['Raw_text'], axis=1, inplace=True)   \n",
        "   dataset.drop(missing_values(dataset, 70), axis=1, inplace=True)\n",
        "  \n",
        "print(\"Train rows and columns after removing : \", train_df.shape)\n",
        "print(\"Test rows and columns after removing : \", test_df.shape)"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Rows and columns: (132637, 56)\n",
            "Columns to work with: ['Expire_time_days', 'Highest_page', 'Limit_quantity', 'Lowest_page', 'Num_of_signatures', 'Num_of_widgets', 'Paid_per_year', 'Zipcode']\n",
            "Rows and columns: (10000, 55)\n",
            "Columns to work with: ['Expire_time_days', 'Highest_page', 'Limit_quantity', 'Lowest_page', 'Num_of_signatures', 'Num_of_widgets', 'Paid_per_year', 'Zipcode']\n",
            "Train rows and columns after removing :  (132637, 47)\n",
            "Test rows and columns after removing :  (10000, 46)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "z8yBm7mUpXwh",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Imputation\n",
        "Let's work with missing values replacement in low percentage fields. After observing their mean and std I decide to replace them with the mean value of each column (dataset.fillna(dataset.mean()))."
      ]
    },
    {
      "metadata": {
        "id": "gnHNaAHHaOap",
        "colab_type": "code",
        "outputId": "656bbcea-b1fe-4516-e5b1-4a7b99426669",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1120
        }
      },
      "cell_type": "code",
      "source": [
        "for dataset in data:   \n",
        "   for field in missing_values(dataset, 2):\n",
        "    print ('\\n*******',field,'**********\\n')\n",
        "    print (dataset[field].describe())    \n",
        "    #Replace values with mean\n",
        "    dataset.fillna(dataset.mean(), inplace=True)\n",
        "\n",
        "train_df.head()\n",
        "#print(test_df.head())\n"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Columns to work with: ['First_pending_reminders', 'Repaired']\n",
            "\n",
            "******* First_pending_reminders **********\n",
            "\n",
            "count    5.356600e+04\n",
            "mean     6.077132e+05\n",
            "std      8.619469e+05\n",
            "min      0.000000e+00\n",
            "25%      8.640000e+04\n",
            "50%      6.048000e+05\n",
            "75%      6.048000e+05\n",
            "max      4.000000e+06\n",
            "Name: First_pending_reminders, dtype: float64\n",
            "\n",
            "******* Repaired **********\n",
            "\n",
            "count    132637.000000\n",
            "mean          0.994636\n",
            "std           0.071985\n",
            "min           0.000000\n",
            "25%           1.000000\n",
            "50%           1.000000\n",
            "75%           1.000000\n",
            "max           1.000000\n",
            "Name: Repaired, dtype: float64\n",
            "Columns to work with: ['First_pending_reminders', 'Repaired']\n",
            "\n",
            "******* First_pending_reminders **********\n",
            "\n",
            "count    4.075000e+03\n",
            "mean     5.932275e+05\n",
            "std      8.361368e+05\n",
            "min      0.000000e+00\n",
            "25%      8.640000e+04\n",
            "50%      6.048000e+05\n",
            "75%      6.048000e+05\n",
            "max      4.000000e+06\n",
            "Name: First_pending_reminders, dtype: float64\n",
            "\n",
            "******* Repaired **********\n",
            "\n",
            "count    10000.000000\n",
            "mean         0.994841\n",
            "std          0.070532\n",
            "min          0.000000\n",
            "25%          1.000000\n",
            "50%          1.000000\n",
            "75%          1.000000\n",
            "max          1.000000\n",
            "Name: Repaired, dtype: float64\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
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              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "    }\n",
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              "</style>\n",
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              "      <th></th>\n",
              "      <th>Unnamed: 0</th>\n",
              "      <th>id</th>\n",
              "      <th>Account_age_days</th>\n",
              "      <th>Country_CAT</th>\n",
              "      <th>Currency_CAT</th>\n",
              "      <th>Day_of_the_month</th>\n",
              "      <th>Day_of_the_week</th>\n",
              "      <th>Dimensions_height</th>\n",
              "      <th>Dimensions_width</th>\n",
              "      <th>First_pending_reminders</th>\n",
              "      <th>...</th>\n",
              "      <th>Seat_admin_role</th>\n",
              "      <th>Signer_mail_domain_CAT</th>\n",
              "      <th>Signer_mail_region_CAT</th>\n",
              "      <th>Signer_mail_username_CAT</th>\n",
              "      <th>Size</th>\n",
              "      <th>Source_CAT</th>\n",
              "      <th>Time_of_the_day_seconds</th>\n",
              "      <th>Type_CAT</th>\n",
              "      <th>User_age_days</th>\n",
              "      <th>Target_sign</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
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              "      <td>76666</td>\n",
              "      <td>76666</td>\n",
              "      <td>0.0</td>\n",
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              "      <td>0</td>\n",
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              "      <td>1</td>\n",
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              "      <td>595.0</td>\n",
              "      <td>86400.000000</td>\n",
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              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
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              "      <td>0</td>\n",
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              "      <td>607713.164377</td>\n",
              "      <td>...</td>\n",
              "      <td>1</td>\n",
              "      <td>9</td>\n",
              "      <td>0</td>\n",
              "      <td>41</td>\n",
              "      <td>119095</td>\n",
              "      <td>0</td>\n",
              "      <td>21839</td>\n",
              "      <td>0</td>\n",
              "      <td>320.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>114228</td>\n",
              "      <td>114228</td>\n",
              "      <td>848.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>842.0</td>\n",
              "      <td>595.0</td>\n",
              "      <td>607713.164377</td>\n",
              "      <td>...</td>\n",
              "      <td>1</td>\n",
              "      <td>9</td>\n",
              "      <td>0</td>\n",
              "      <td>41</td>\n",
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              "      <td>23115</td>\n",
              "      <td>0</td>\n",
              "      <td>321.0</td>\n",
              "      <td>1</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1449</td>\n",
              "      <td>1449</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>6</td>\n",
              "      <td>842.0</td>\n",
              "      <td>595.0</td>\n",
              "      <td>607713.164377</td>\n",
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              "      <td>0</td>\n",
              "      <td>41</td>\n",
              "      <td>1710912</td>\n",
              "      <td>0</td>\n",
              "      <td>33786</td>\n",
              "      <td>0</td>\n",
              "      <td>297.0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 47 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "   Unnamed: 0      id  Account_age_days  Country_CAT  Currency_CAT  \\\n",
              "0       11461   11461             826.0            0             0   \n",
              "1       76666   76666               0.0            0             0   \n",
              "2      105984  105984             847.0            0             0   \n",
              "3      114228  114228             848.0            0             0   \n",
              "4        1449    1449             824.0            0             0   \n",
              "\n",
              "   Day_of_the_month  Day_of_the_week  Dimensions_height  Dimensions_width  \\\n",
              "0                 4                1              842.0             595.0   \n",
              "1                18                1              842.0             595.0   \n",
              "2                25                1              842.0             595.0   \n",
              "3                26                2              842.0             595.0   \n",
              "4                 2                6              842.0             595.0   \n",
              "\n",
              "   First_pending_reminders     ...       Seat_admin_role  \\\n",
              "0            607713.164377     ...                     1   \n",
              "1             86400.000000     ...                     1   \n",
              "2            607713.164377     ...                     1   \n",
              "3            607713.164377     ...                     1   \n",
              "4            607713.164377     ...                     1   \n",
              "\n",
              "   Signer_mail_domain_CAT  Signer_mail_region_CAT  Signer_mail_username_CAT  \\\n",
              "0                       9                       0                        41   \n",
              "1                       2                       0                     29497   \n",
              "2                       9                       0                        41   \n",
              "3                       9                       0                        41   \n",
              "4                       9                       0                        41   \n",
              "\n",
              "      Size  Source_CAT  Time_of_the_day_seconds  Type_CAT  User_age_days  \\\n",
              "0   119111           0                    45348         0          299.0   \n",
              "1    41961           1                    32622         0            0.0   \n",
              "2   119095           0                    21839         0          320.0   \n",
              "3   118907           0                    23115         0          321.0   \n",
              "4  1710912           0                    33786         0          297.0   \n",
              "\n",
              "   Target_sign  \n",
              "0            0  \n",
              "1            1  \n",
              "2            0  \n",
              "3            1  \n",
              "4            1  \n",
              "\n",
              "[5 rows x 47 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "metadata": {
        "id": "TXs7uu7Jp_Wc",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Constant values\n",
        "Let's explore constant values that are columns with the same value in each row. I decide to drop those columns."
      ]
    },
    {
      "metadata": {
        "id": "UusgdL4g96Bh",
        "colab_type": "code",
        "outputId": "a9b39d52-cf85-435b-cf35-4e2cbaa749e7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 255
        }
      },
      "cell_type": "code",
      "source": [
        "def detect_uniques(df):\n",
        "  unique_df = df.nunique().reset_index()\n",
        "  unique_df.columns = [\"col_name\", \"unique_count\"]\n",
        "  constant_df = unique_df[unique_df[\"unique_count\"]==1]\n",
        "  print(constant_df)  \n",
        "  return(constant_df.col_name.tolist())\n",
        "\n",
        "\n",
        "print(train_df.shape)\n",
        "\n",
        "for dataset in data:\n",
        "   print(\"Rows and columns:\", dataset.shape)   \n",
        "   dataset.drop(detect_uniques(dataset), axis=1, inplace=True)  \n",
        "\n",
        "print(train_df.shape)"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(132637, 47)\n",
            "Rows and columns: (132637, 47)\n",
            "                                  col_name  unique_count\n",
            "21                                   Month             1\n",
            "28  Original_sender_vs_signer_same_address             1\n",
            "29   Original_sender_vs_signer_same_domain             1\n",
            "Rows and columns: (10000, 46)\n",
            "                                  col_name  unique_count\n",
            "21                                   Month             1\n",
            "28  Original_sender_vs_signer_same_address             1\n",
            "29   Original_sender_vs_signer_same_domain             1\n",
            "34                          Required_audio             1\n",
            "35                      Required_photo_ids             1\n",
            "(132637, 44)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "7h0_C3_uDRT2",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "I detected that in the previous step more columns where dropped in the Test partition than in the Train. Let's explore those fields"
      ]
    },
    {
      "metadata": {
        "id": "SYI3U0uxDX-J",
        "colab_type": "code",
        "outputId": "f288454c-f79a-4a69-be41-88741c94576a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 323
        }
      },
      "cell_type": "code",
      "source": [
        "print(train_df['Required_audio'].describe())\n",
        "print(train_df['Required_photo_ids'].describe())\n"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "count    132637.000000\n",
            "mean          0.000060\n",
            "std           0.007766\n",
            "min           0.000000\n",
            "25%           0.000000\n",
            "50%           0.000000\n",
            "75%           0.000000\n",
            "max           1.000000\n",
            "Name: Required_audio, dtype: float64\n",
            "count    132637.000000\n",
            "mean          0.000053\n",
            "std           0.007265\n",
            "min           0.000000\n",
            "25%           0.000000\n",
            "50%           0.000000\n",
            "75%           0.000000\n",
            "max           1.000000\n",
            "Name: Required_photo_ids, dtype: float64\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "bbdqxjltDvub",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "Those fields are practically always constant with a 0 value so we are going to drop them"
      ]
    },
    {
      "metadata": {
        "id": "8u8pUpKjD3y6",
        "colab_type": "code",
        "outputId": "96138608-7c64-4a12-db2d-38ad4339a594",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "cell_type": "code",
      "source": [
        "train_df.drop('Required_audio', axis=1, inplace=True)\n",
        "train_df.drop('Required_photo_ids', axis=1, inplace=True)\n",
        "print(train_df.shape)\n",
        "print(test_df.shape)\n"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(132637, 42)\n",
            "(10000, 41)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "VDSUh4tQ6qZt",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "I will have a new look to the fields of the dataset and detect some fields with nearly constant values (Has_*)\n"
      ]
    },
    {
      "metadata": {
        "id": "KlmaYHu-gcFt",
        "colab_type": "code",
        "outputId": "a0b0ce99-4dca-4dcd-b742-a6415f6833ed",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1351
        }
      },
      "cell_type": "code",
      "source": [
        "\n",
        "def create_description_table(df, descriptions, round_num=2):\n",
        "    df_desc = df.dtypes.to_frame(name='Data Type')\n",
        "    #df_desc['Description'] = descriptions\n",
        "    #df_desc['Missing Values'] = df.isnull().sum()\n",
        "    df_desc['Mean'] = df.select_dtypes('number').mean().round(round_num)\n",
        "    df_desc['Most Common'] = df.apply(lambda x: x.value_counts().index[0])\n",
        "    df_desc['Most Common Ct'] = df.apply(lambda x: x.value_counts().iloc[0])\n",
        "    df_desc['Unique Values'] = df.nunique()\n",
        "    return df_desc\n",
        "  \n",
        "create_description_table(train_df,'Description')\n",
        "\n",
        "\n"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "    .dataframe tbody tr th:only-of-type {\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Data Type</th>\n",
              "      <th>Mean</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Unnamed: 0</th>\n",
              "      <td>int64</td>\n",
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              "      <td>1.662700e+04</td>\n",
              "      <td>1</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>id</th>\n",
              "      <td>int64</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>Account_age_days</th>\n",
              "      <td>float64</td>\n",
              "      <td>637.81</td>\n",
              "      <td>8.410000e+02</td>\n",
              "      <td>4644</td>\n",
              "      <td>1255</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Country_CAT</th>\n",
              "      <td>int64</td>\n",
              "      <td>0.14</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>115209</td>\n",
              "      <td>13</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Currency_CAT</th>\n",
              "      <td>int64</td>\n",
              "      <td>0.13</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>115209</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Day_of_the_month</th>\n",
              "      <td>int64</td>\n",
              "      <td>16.47</td>\n",
              "      <td>2.700000e+01</td>\n",
              "      <td>9549</td>\n",
              "      <td>28</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Day_of_the_week</th>\n",
              "      <td>int64</td>\n",
              "      <td>2.11</td>\n",
              "      <td>2.000000e+00</td>\n",
              "      <td>28430</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Dimensions_height</th>\n",
              "      <td>float64</td>\n",
              "      <td>853.12</td>\n",
              "      <td>8.420000e+02</td>\n",
              "      <td>94629</td>\n",
              "      <td>100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Dimensions_width</th>\n",
              "      <td>float64</td>\n",
              "      <td>595.06</td>\n",
              "      <td>5.950000e+02</td>\n",
              "      <td>95277</td>\n",
              "      <td>102</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>First_pending_reminders</th>\n",
              "      <td>float64</td>\n",
              "      <td>607713.16</td>\n",
              "      <td>6.077132e+05</td>\n",
              "      <td>79071</td>\n",
              "      <td>16</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Has_body</th>\n",
              "      <td>int64</td>\n",
              "      <td>0.80</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>106575</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Has_protection</th>\n",
              "      <td>float64</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>132463</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Has_signature_position</th>\n",
              "      <td>int64</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>132588</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Has_signer_name</th>\n",
              "      <td>int64</td>\n",
              "      <td>1.00</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>132609</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Has_signer_phone</th>\n",
              "      <td>int64</td>\n",
              "      <td>0.03</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>128508</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Has_subject</th>\n",
              "      <td>int64</td>\n",
              "      <td>0.28</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>95465</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Has_template</th>\n",
              "      <td>int64</td>\n",
              "      <td>0.29</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>94673</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Has_terms_and_conditions</th>\n",
              "      <td>int64</td>\n",
              "      <td>0.78</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>103339</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>In_person</th>\n",
              "      <td>float64</td>\n",
              "      <td>0.53</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>70098</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>License_tier_CAT</th>\n",
              "      <td>int64</td>\n",
              "      <td>4.64</td>\n",
              "      <td>5.000000e+00</td>\n",
              "      <td>110400</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Method_CAT</th>\n",
              "      <td>int64</td>\n",
              "      <td>1.42</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>70058</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Non_customer_enterprise_signer</th>\n",
              "      <td>int64</td>\n",
              "      <td>0.06</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>125074</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Num_of_pending_reminders</th>\n",
              "      <td>int64</td>\n",
              "      <td>7.69</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>79071</td>\n",
              "      <td>23</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Num_of_required_page</th>\n",
              "      <td>int64</td>\n",
              "      <td>0.16</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>118719</td>\n",
              "      <td>16</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Original_sender_mail_domain_CAT</th>\n",
              "      <td>int64</td>\n",
              "      <td>53.87</td>\n",
              "      <td>8.000000e+00</td>\n",
              "      <td>60174</td>\n",
              "      <td>696</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Original_sender_mail_region_CAT</th>\n",
              "      <td>int64</td>\n",
              "      <td>0.33</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>103681</td>\n",
              "      <td>32</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Original_sender_mail_username_CAT</th>\n",
              "      <td>int64</td>\n",
              "      <td>67.29</td>\n",
              "      <td>7.000000e+00</td>\n",
              "      <td>60174</td>\n",
              "      <td>849</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Original_size</th>\n",
              "      <td>float64</td>\n",
              "      <td>536322.63</td>\n",
              "      <td>2.250130e+06</td>\n",
              "      <td>1928</td>\n",
              "      <td>52641</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Pages</th>\n",
              "      <td>float64</td>\n",
              "      <td>7.21</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>48103</td>\n",
              "      <td>124</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Part</th>\n",
              "      <td>int64</td>\n",
              "      <td>0.20</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>121517</td>\n",
              "      <td>40</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Repaired</th>\n",
              "      <td>float64</td>\n",
              "      <td>0.99</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>128129</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Required_photos</th>\n",
              "      <td>int64</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>132478</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Seat_admin_role</th>\n",
              "      <td>int64</td>\n",
              "      <td>0.90</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>119263</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Signer_mail_domain_CAT</th>\n",
              "      <td>int64</td>\n",
              "      <td>266.85</td>\n",
              "      <td>9.000000e+00</td>\n",
              "      <td>60175</td>\n",
              "      <td>4981</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Signer_mail_region_CAT</th>\n",
              "      <td>int64</td>\n",
              "      <td>0.97</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>110992</td>\n",
              "      <td>107</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Signer_mail_username_CAT</th>\n",
              "      <td>int64</td>\n",
              "      <td>10745.39</td>\n",
              "      <td>4.100000e+01</td>\n",
              "      <td>60174</td>\n",
              "      <td>41484</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Size</th>\n",
              "      <td>int64</td>\n",
              "      <td>539983.86</td>\n",
              "      <td>2.280520e+06</td>\n",
              "      <td>1928</td>\n",
              "      <td>57843</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Source_CAT</th>\n",
              "      <td>int64</td>\n",
              "      <td>0.26</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>103191</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Time_of_the_day_seconds</th>\n",
              "      <td>int64</td>\n",
              "      <td>41131.11</td>\n",
              "      <td>3.947000e+04</td>\n",
              "      <td>44</td>\n",
              "      <td>45649</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Type_CAT</th>\n",
              "      <td>int64</td>\n",
              "      <td>0.14</td>\n",
              "      <td>0.000000e+00</td>\n",
              "      <td>113684</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>User_age_days</th>\n",
              "      <td>float64</td>\n",
              "      <td>366.29</td>\n",
              "      <td>3.140000e+02</td>\n",
              "      <td>4755</td>\n",
              "      <td>1258</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Target_sign</th>\n",
              "      <td>int64</td>\n",
              "      <td>0.67</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>89150</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                  Data Type       Mean   Most Common  \\\n",
              "Unnamed: 0                            int64   71309.99  1.662700e+04   \n",
              "id                                    int64   71309.99  1.662700e+04   \n",
              "Account_age_days                    float64     637.81  8.410000e+02   \n",
              "Country_CAT                           int64       0.14  0.000000e+00   \n",
              "Currency_CAT                          int64       0.13  0.000000e+00   \n",
              "Day_of_the_month                      int64      16.47  2.700000e+01   \n",
              "Day_of_the_week                       int64       2.11  2.000000e+00   \n",
              "Dimensions_height                   float64     853.12  8.420000e+02   \n",
              "Dimensions_width                    float64     595.06  5.950000e+02   \n",
              "First_pending_reminders             float64  607713.16  6.077132e+05   \n",
              "Has_body                              int64       0.80  1.000000e+00   \n",
              "Has_protection                      float64       0.00  0.000000e+00   \n",
              "Has_signature_position                int64       0.00  0.000000e+00   \n",
              "Has_signer_name                       int64       1.00  1.000000e+00   \n",
              "Has_signer_phone                      int64       0.03  0.000000e+00   \n",
              "Has_subject                           int64       0.28  0.000000e+00   \n",
              "Has_template                          int64       0.29  0.000000e+00   \n",
              "Has_terms_and_conditions              int64       0.78  1.000000e+00   \n",
              "In_person                           float64       0.53  1.000000e+00   \n",
              "License_tier_CAT                      int64       4.64  5.000000e+00   \n",
              "Method_CAT                            int64       1.42  1.000000e+00   \n",
              "Non_customer_enterprise_signer        int64       0.06  0.000000e+00   \n",
              "Num_of_pending_reminders              int64       7.69  0.000000e+00   \n",
              "Num_of_required_page                  int64       0.16  0.000000e+00   \n",
              "Original_sender_mail_domain_CAT       int64      53.87  8.000000e+00   \n",
              "Original_sender_mail_region_CAT       int64       0.33  0.000000e+00   \n",
              "Original_sender_mail_username_CAT     int64      67.29  7.000000e+00   \n",
              "Original_size                       float64  536322.63  2.250130e+06   \n",
              "Pages                               float64       7.21  1.000000e+00   \n",
              "Part                                  int64       0.20  0.000000e+00   \n",
              "Repaired                            float64       0.99  1.000000e+00   \n",
              "Required_photos                       int64       0.00  0.000000e+00   \n",
              "Seat_admin_role                       int64       0.90  1.000000e+00   \n",
              "Signer_mail_domain_CAT                int64     266.85  9.000000e+00   \n",
              "Signer_mail_region_CAT                int64       0.97  0.000000e+00   \n",
              "Signer_mail_username_CAT              int64   10745.39  4.100000e+01   \n",
              "Size                                  int64  539983.86  2.280520e+06   \n",
              "Source_CAT                            int64       0.26  0.000000e+00   \n",
              "Time_of_the_day_seconds               int64   41131.11  3.947000e+04   \n",
              "Type_CAT                              int64       0.14  0.000000e+00   \n",
              "User_age_days                       float64     366.29  3.140000e+02   \n",
              "Target_sign                           int64       0.67  1.000000e+00   \n",
              "\n",
              "                                   Most Common Ct  Unique Values  \n",
              "Unnamed: 0                                      1         132637  \n",
              "id                                              1         132637  \n",
              "Account_age_days                             4644           1255  \n",
              "Country_CAT                                115209             13  \n",
              "Currency_CAT                               115209              2  \n",
              "Day_of_the_month                             9549             28  \n",
              "Day_of_the_week                             28430              7  \n",
              "Dimensions_height                           94629            100  \n",
              "Dimensions_width                            95277            102  \n",
              "First_pending_reminders                     79071             16  \n",
              "Has_body                                   106575              2  \n",
              "Has_protection                             132463              2  \n",
              "Has_signature_position                     132588              2  \n",
              "Has_signer_name                            132609              2  \n",
              "Has_signer_phone                           128508              2  \n",
              "Has_subject                                 95465              2  \n",
              "Has_template                                94673              2  \n",
              "Has_terms_and_conditions                   103339              2  \n",
              "In_person                                   70098              3  \n",
              "License_tier_CAT                           110400              6  \n",
              "Method_CAT                                  70058              3  \n",
              "Non_customer_enterprise_signer             125074              2  \n",
              "Num_of_pending_reminders                    79071             23  \n",
              "Num_of_required_page                       118719             16  \n",
              "Original_sender_mail_domain_CAT             60174            696  \n",
              "Original_sender_mail_region_CAT            103681             32  \n",
              "Original_sender_mail_username_CAT           60174            849  \n",
              "Original_size                                1928          52641  \n",
              "Pages                                       48103            124  \n",
              "Part                                       121517             40  \n",
              "Repaired                                   128129              3  \n",
              "Required_photos                            132478              2  \n",
              "Seat_admin_role                            119263              2  \n",
              "Signer_mail_domain_CAT                      60175           4981  \n",
              "Signer_mail_region_CAT                     110992            107  \n",
              "Signer_mail_username_CAT                    60174          41484  \n",
              "Size                                         1928          57843  \n",
              "Source_CAT                                 103191              5  \n",
              "Time_of_the_day_seconds                        44          45649  \n",
              "Type_CAT                                   113684              2  \n",
              "User_age_days                                4755           1258  \n",
              "Target_sign                                 89150              2  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "metadata": {
        "id": "PK-W3vINFVON",
        "colab_type": "code",
        "outputId": "c501e554-6140-4d70-8ea4-3255ce5a1160",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 170
        }
      },
      "cell_type": "code",
      "source": [
        "train_df['Has_signer_name'].describe()"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count    132637.000000\n",
              "mean          0.999789\n",
              "std           0.014528\n",
              "min           0.000000\n",
              "25%           1.000000\n",
              "50%           1.000000\n",
              "75%           1.000000\n",
              "max           1.000000\n",
              "Name: Has_signer_name, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "metadata": {
        "id": "iQbs9OC4LH6B",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "After analyzing those fields and taking into account the mean and standard deviation and the difference between max and min number we consider it nearly constants or bringing no value to the final result so we drop it. Index and Unnamed: 0 that has the same values as index I will also drop them"
      ]
    },
    {
      "metadata": {
        "id": "BDhzoPoiLSAz",
        "colab_type": "code",
        "outputId": "4da9522a-879b-4d25-bdb3-955fa7cf96d5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "cell_type": "code",
      "source": [
        "\n",
        "drop_columns = ['Has_body','Has_protection','Has_signature_position','Has_signer_name','Has_signer_phone','Unnamed: 0','id']\n",
        "train_df.drop(drop_columns, axis=1, inplace=True)\n",
        "test_df.drop(drop_columns, axis=1, inplace=True)\n",
        "print(train_df.shape)\n",
        "print(test_df.shape)\n",
        "\n",
        "\n",
        "\n"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(132637, 35)\n",
            "(10000, 34)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "PgMsSxH5MiZw",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Analytical study of the dataset\n",
        "We move to study relations between features. To look of there are any linear correlations, we start applying a Spearman's rank correlation . Apparently there are no strong linear correlations. Spearman coefficients no bigger than 0,1"
      ]
    },
    {
      "metadata": {
        "id": "nKG3v4ljM0sG",
        "colab_type": "code",
        "outputId": "76fbfa6c-d968-472b-d658-56d6fa2fa3c9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 634
        }
      },
      "cell_type": "code",
      "source": [
        "labels = []\n",
        "values = []\n",
        "for col in train_df.columns:\n",
        "    if col not in [\"Limit_quantity\",\"id\",\"Zipcode\",\"Target_sign\",\"Paid_per_year,Part\",\"Raw_text\"]:\n",
        "        labels.append(col)\n",
        "        #print (col)\n",
        "        values.append(spearmanr(train_df[col].values, train_df[\"Target_sign\"].values)[0])\n",
        "corr_df = pd.DataFrame({'col_labels':labels, 'corr_values':values})\n",
        "corr_df = corr_df.sort_values(by='corr_values')\n",
        " \n",
        "corr_df = corr_df[(corr_df['corr_values']>0.1) | (corr_df['corr_values']<-0.1)]\n",
        "ind = np.arange(corr_df.shape[0])\n",
        "width = 0.3\n",
        "fig, ax = plt.subplots(figsize=(6,4))\n",
        "#fig, ax = plt.subplots(figsize=(12,30))\n",
        "rects = ax.barh(ind, np.array(corr_df.corr_values.values), color='b')\n",
        "ax.set_yticks(ind)\n",
        "ax.set_yticklabels(corr_df.col_labels.values, rotation='horizontal')\n",
        "ax.set_xlabel(\"Correlation coefficient\")\n",
        "ax.set_title(\"Correlation coefficient of the variables\")\n",
        "plt.show()\n",
        "\n",
        "import seaborn as sns\n",
        "\n",
        "cols_to_use = corr_df[(corr_df['corr_values']>0.11) | (corr_df['corr_values']<-0.11)].col_labels.tolist()\n",
        "\n",
        "temp_df = train_df[cols_to_use]\n",
        "corrmat = temp_df.corr(method='spearman')\n",
        "f, ax = plt.subplots(figsize=(4, 4))\n",
        "\n",
        "# Draw the heatmap using seaborn\n",
        "sns.heatmap(corrmat, vmax=1., square=True, cmap=\"YlGnBu\", annot=True)\n",
        "plt.title(\"Important variables correlation map\", fontsize=15)\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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//fQTAC+99BKNGjWyeGQiInIt3G70DiMjI9m1axeXLl0iKCiIrl278uOPP/LP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\nvgAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f84731c5dd8>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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hRHYWplXFpYgLIUR2JnYCLCniQgiRzZMOp3/WSBEXQojsTKs3RYq4EEIYkZa4\nEEKYMOkTF0IIE5aPiz08S6SIizxVnzFS7QjF5m//5WpHKFbLrvirHeGZVViH3RcXKeJCCJGd9IkL\nIYQJk5a4EEKYMNOq4VLEhRAiO62MExdCCNMlRVwIIUyYRnZsCiGE6TKxGi5FXAghspMiLoQQJkwj\nfeJCCGG6TOyaEFLEhRAiO+lOEUIIEyZFXAghTJgMMRRCCBMmOzaFEMKEmVhDXIq4EEJkJ4fdCyGE\nCTOxM9FKERdCiOykO0UIIUyYFHEhhDBhGhPrT5EiLoQQ2UhLXAghTJiMThFCCBNmYr0pUsRF8WlS\nsTQTGtfAzsqCaynpTP79HHH3dDnmG1qvCmO9qvPWznBOxN0FwM+tEr6ulQzzWGo1lLS2pNn6kGLL\nX9gsLS2YPbEf/3mnC7UajyL2xm21IxWp1NQ0pk//gl27DnLmzDa14+RJulOEyIWtpZaFbV15Z3cE\nZ26lMPCVSsxsUZuReyON5pvRvBYWWg230zKMpq+LvMa6yGuG+8PqVaGcrXWxZC8qm78dz5/hl9SO\nUWx8fSfQtm0jtWM8kakddm9icYWpalKxNFeT73PmVgoAgedv0LxyGeysLIzm23YxjumHLpCZpeS5\nrHI2Vvi+XInlp6KLNHNRm7/kJ2Yv+lHtGMVm1qxRvPlmR7VjPJFGk//bk8ydOxcfHx98fX0JDw83\neuzIkSP06dMHHx8fvvjii6fO+8QifvXqVXr37m00benSpQQEBDz1Sh/at28fOl3On9PPqtTUVNq1\na1ds62vSpAkAc+bMISYmhpSUFA4dOgTA119/zcmTJ4sty79VvZQtV+7eN9y/l5lFUnoGL5W0MZrv\n1M3kJy5rSL0q/HThBsk6faHnLE6hJy6oHaFYeXi4qh0hX7RaTb5vj3Ps2DGio6PZuHEjc+bMYc6c\nOUaPz549m6VLl7JhwwYOHz7MxYsXny7vUz2rkKxevZqMjIwnz/icmzJlClWrViUyMpLDhw8D8M47\n7+Dh4aFysvyztbRAp88ymnY/MwtbS4s8npE7BysLetR+ke/PXHvyzEI8hcJqiYeEhODt7Q2As7Mz\nSUlJpKQ8+CUaExNDqVKlqFixIlqtltatWxMS8nT7d/5Vn/i8efMIDw8nPT2dfv360bdvXw4dOsTn\nn3+OjY0N5cqVY8GCBVhZWeV47tatWzl16hRvv/02q1evZvPmzfz8889otVq8vb0ZOnQoS5cuJSYm\nhqtXr/L+++8TEBCAhYUFZ86cYcSIERw8eJCzZ88yYcIEvL29mT17NhEREej1evr165fjF8RDKSkp\njB8/nnv37nH//n2mTZuGu7vKQkz6AAAgAElEQVQ77du3x8fHh/3796PT6Vi1ahUA77//Punp6TRs\n2PCJr8ns2bMJDw/HwsICf39/XFxc+OSTTzhx4gR6vZ4BAwbQs2dP/Pz8aN68OUePHuXOnTt89dVX\nODk5MX78eG7cuEG9evUMy/Tz82PatGnMmjWLlJQUqlevzsmTJ+nYsSMtW7Zk+vTpxMTEoNPpGDNm\nDC1btsx1W+7evcv//vc/tFoter2eTz/9lMqVKz/lX79g7mXqsX7kulc2lhbcyyhYa7pNtXKE30wm\nMT2zMOOJIhAQEERAQBAA48cPpn37Zionyp/C2rGZkJCAm5ub4X7ZsmWJj4/HwcGB+Ph4ypYta/RY\nTEzMU60nX0X88uXL+Pn5Ge7HxsYydOhQKleuzKRJk7h//z7e3t707duXgIAAJk6ciJeXF3v27CEx\nMZHy5cvnWGbPnj1ZsmQJ33zzDXFxcQQHB7NhwwYA+vXrR6dOnQDIyMjg+++/JzQ0lLNnzxIcHMzx\n48f54IMP2LdvH2FhYaxbtw4vLy8OHDjAL7/8QkZGBj/99FOe2xMfH0/fvn3x9vYmJCSEb775hqVL\nl6LX66lZsybDhw9n3LhxHD16lLi4OGrXrs3kyZPZuXMnO3bsyHO5R44c4caNG2zatInjx4+zc+dO\nkpKSuHDhAj/88AP37t2je/fuhm9nBwcH1qxZw4IFC9izZw81atQgMzOTjRs3GrYru2HDhnHhwgV8\nfHwMXSk7duzA2tqagIAA4uLiGDRoELt37851W2JiYmjevDmjRo0iMjKS+Pj4YivilxPv8XqNf94H\nDlYWlCphSfTdtAItp03Vsvx+1bxHcZiLgQO7MnBgV7VjFFhRDTFUlLz38/wb+SriNWrUMCooS5cu\nBSApKQlfX1+srKy4c+cOAJ06dWLGjBl069aNLl265FrAH3X69Gmio6MZNGgQ8KDvOTY2FgB3d3fD\nfK6urlhbW1O+fHmqV6+OnZ0d5cqVIzk5mdKlS1O9enVGjhxJp06d6NmzZ57re+GFF/jyyy9ZuXIl\nOp0OOzs7w2NeXl4AVKhQgeTkZKKiomjU6MEe9caNGz92OyIjI/H09ASgUaNGNGrUiFWrVhmeb2dn\nR61atYiOjs6xrsTERC5evGjoIqlfvz42Nja5rMVYRESEoe/8xRdfxNramsTExFy3pUWLFowePZrk\n5GQ6duxYrN0xodeTmN3KBs8XS3Ii7i6D61bhwJVbpGVmPfnJ2dQpa8/Gv64XUUohCq+IOzk5kZCQ\nYLh/8+ZNQz189LG4uDicnJyeaj1P3SeemJjI0aNHWbduHevWrcPa+sFwr549e7J27VrKlCnDyJEj\niYqKeuKyrKysaNOmjWFZP//8s6HwZe+KsbS0zPX/D3377beMHj2av/76ixEjRuS5vjVr1vDiiy+y\nYcMGZs6cafSYhcU/fbSKoqAoCtr/P4QrK+vxBcfCwiLHPI9e6ikjI8OwvMetKz/ry/7ch3Q6XZ7L\nd3FxYdu2bXh5ebFo0SK2bt2ar+UXhnR9FuP3n2Vas1oE921EfSdHPgq5iJOdNdt7/9NNtb13Q3a8\n4YWTvTWftnFlxxte1HvB0fB4BfsSxKeZzs7wvDi9UIpTvy7g1K8LANi9aRqnfl1ApRfLqJysaERG\nXqRTpxG89dZU9PosOnUaQadOeX9G1aTVKPm+PU6LFi3YvXs38KCB5+TkhIODAwBVqlQhJSWFq1ev\nkpmZyf79+2nRosVT5f1XfeIVKlTAysqKffv2odfr0el0fPPNNwwcOBAfHx9u3bpFVFQUzs7OuT5f\no9Gg1+txc3NjwYIFpKWlYWNjw5w5c/jggw8KlOXq1av8+uuvDBo0CDc3tzz7wwHu3LlDnTp1AAzd\nL3mpUaMGERERdOzYkdDQ0MdmqFevHl9//TXDhw/nzJkzbN68mS5durB8+XLeeecdUlNTuXLlCi+9\n9FKe63rYXXPixIkcI3e0Wi2ZmcZ9wfXq1SM0NJQuXbpw/fp1tFotJUuWzHX5O3bsoGrVqnh7e1O6\ndGmCg4Mf+4ulsB2/kUSvrSdyTO8e+Geu/89No3VHCj2XGm4mJNGgXcHe46bMza0WwcFfqR0jXywL\nqSXu6emJm5sbvr6+aDQaZsyYQWBgII6OjrRv356ZM2cyfvx4ADp37kyNGjWeLu/TBnR0dCQ6OpqB\nAwfi7e1NmzZtmDlzJo0aNWLIkCGULFmSkiVLMmTIkDyX0bhxY/r378/atWsZNGgQAwYMwMLCAm9v\n73x1JWTn5OTEyZMn2blzJ1ZWVrzxxht5ztujRw8+/PBDgoODGTBgAEFBQWzZsiXXeXv27MmoUaMY\nPHjwE3dsNmrUiH379tG/f38AZsyYQZ06dahbty4DBgwgMzOT8ePHG3XfZPfqq6+yZcsWBg4ciKur\nKy+++KLR46+88goLFiygQoUKhmldunTh2LFj+Pn5kZGRwaxZs/LMV716dWbMmIGdnR0WFhZMnTr1\nsdsjxPPoSS3sgni0Merq+s8wy0aNGrFx48Z/vQ6NUlS97cLkvbzyd7UjFJu//ZerHaFYpV3xVztC\nMXPJ95w9fjmY73m3ebd6mjCFqsgPu9+3bx+rV6/OMX3QoEG0b9++SNc9c+bMXPvkv/nmmwK39LNb\ntmxZrl0rc+fOpWrVqk+9XCGE+kztMHZpiYs8SUvcfElLPG9v7Mt/S3zLa89BS1wIIUyJphD7xIuD\nFHEhhMimsEanFBcp4kIIkU1hjk4pDlLEhRAiG7myjxBCmDBTG50iRVwIIbKRlrgQQpgwS630iQsh\nhMmS7hQhhDBhMjpFCCFMmPSJCyGECZMiLoQQJkz6xIUQwoTJ6BQhhDBh0hIXQggTJn3iQghhwuRU\ntEIIYcKkJS6EECbMUlriQghhuqQlLoQQJkyKuBBCmDALtQMUkBRxIYTIRk6AJczGa6/o1Y5QbJZd\n8Vc7QrGyrTZD7QjFKu3KhnzPK90pQghhwqxM7JBNKeJCCJGNtMSFEMKESZ+4EEKYMGmJCyGECZMh\nhkIIYcKkJS6EECbMSi4KIYQQpkta4kIIYcKKsohnZGQwceJErl27hoWFBfPmzaNq1aq5zvvf//4X\na2tr5s+f/9hlmtiwdiGEKFpaTf5vBRUUFETJkiXZsGEDI0aMYOHChbnOd/jwYa5cuZK/vAWPIYQQ\n5stCo+T7VlAhISG0b98egObNm3PixIkc8+h0OpYvX87IkSPztUzpThFCiGwsi7A7JSEhgbJlywKg\n1WrRaDTodDqsra0N86xYsYJ+/frh4OCQr2VKERdCiGwKq0988+bNbN682WhaWFiY0X1FMW7N//33\n30RERPD+++8TGhqar/VIERdCiGyeppskN3379qVv375G0yZOnEh8fDyurq5kZGSgKIpRK/zAgQNc\nu3aNN998k5SUFG7fvs0333zD22+/ned6pIgLIUQ2RTk6pUWLFgQHB9OqVSv2799PkyZNjB5/6623\neOuttwAIDQ3lp59+emwBB9mxKYQQRopydErnzp3JysqiX79+rF+/nvHjxwPw9ddfc/LkyafKKy1x\nIYTIpihb4g/Hhj/qnXfeyTGtSZMmOVrquZEiLoQQ2chh90IIYcJMrY9ZirgQQmQj504RQggTZiFF\nXAghTJdcnk2Ix7hx9DiXft6FotfjULkSrwwbhJWdbY75sjL1XNj8E1d2/0KrRfOwKVsGgPMbtxB/\nMvyf+XQ6rBwdaeo/udi2oTClpqYxffoX7Np1kDNntqkdp0hZWlowe2I//vNOF2o1HkXsjdtqR8qV\ndKcIkYe0W7f5a/1GmsycjG25spzb8CNRW7bi6tcvx7ynlnxJqRrVc0x38XkDF583DPfPrv0e+4oV\nizB10fL1nUDbto3UjlEsNn87nj/DL6kd44mK8twpRcHUdsQKExZ/IoyyL7tiW+7BCYAqv9qcuOM5\nz+IGULN7F5x7dXvs8lKuxnLnrwtUafdqoWctLrNmjeLNNzuqHaNYzF/yE7MX/ah2jCfSaPJ/exZI\nS/wJ1q9fz7Zt27C2tub+/fv897//pXnz5qrlSUhIYPbs2Vy5cgWtVstLL73EjBkzKFmyJADp6em0\naNGC0aNHGw7fnT9/PpGRkcTHx5OWlka1atUoVaoUy5YtK9bs927EYef0guG+nVN5dHeTyUhNxcre\n3mje0rVqPnF5UVt3UL1zB7QWpnZp2394eLhy9Wqc2jGKReiJC2pHyJdnpDbnmxTxx7h69SqbNm3i\nxx9/xMrKir///pupU6eqWsQnTJhAr1696NbtQSv122+/xd/f33By+QMHDvDCCy+wc+dOQxGfOHEi\nAIGBgVy4cIEPP/xQlex6nQ7rko6G+1orK9Bo0KfrchTxJ7kXd5OkS5epN2JYYccUz7lnpYWdX1LE\nHyMlJYX09HQyMjKwsrKievXqBAQEcO7cOWbNmoVWq8Xe3p758+dz7tw51q9fz5IlS4AHh8yGhobi\n5+dH7dq1ARg7diwffPABKSkpODo6smjRIhRFYfLkySQlJaHX65k6dSqurq655omKiuLu3buGAg4w\nZMgQ7t+/b7gfFBTEmDFj+Pjjj4mJicnz0k/F5cov+4n55QAAWksLSpQqaXhMr8sARcGiRIkCL/dG\n6B84eTZAa2larfCAgCACAoIAGD9+MO3bN1M5kXiUqfUxSxF/DFdXV9zd3Xnttddo3bo1r776Kh06\ndGDOnDlMmDCB+vXrs3LlStauXfvYcxzUrl2bfv368dlnn9GyZUsGDRrE6tWrCQkJ4dy5c7Rq1Yq+\nffty8eJF5syZw6pVq3JdzuXLl3n55ZeNpllYWGD//63YlJQUjh8/zqeffsrp06fZuXMn7777buG9\nIE+hmndbqnm3BSBm3wHunPvnJ/W9uJtYly6Flb1dgZebEHaamj26FlrO4jJwYFcGDjS93M8TUxti\naGpfOsXuk08+ISAgAFdXV7799luGDBnCxYsXqV+/PvCgxX3mzJnHLsPd3R2AM2fO4OnpCTw45aS3\ntzcnT55kw4YN+Pn54e/vT3Jy8mOXpdfr83xs9+7dtGzZEhsbG7p27UpQUFBBNrXIlfesz+0zf5F6\n/QYAV3b/QoUmTzcyIzkmFvtKFQoznhCA7Ng0K4qioNPpcHZ2xtnZGT8/P15//XVu3bplmCcjI8Nw\nmaXsMjMzDf+3srICHrSas7KyjOazsrJi2rRpeHh4PDFPzZo1Wbx4cY7pERER1K1bl6CgIK5cuUKP\nHj2AB1cJuXjxIrVq1cr/RhchmzJlcB3Uj7AlX5GVpafkS9WoM9AHgKRLl4kK/BnPD8aQnnSXP+b9\ncwHZP+YvQqPV0vDDsdiUKUNGSipZOp1R14wpioy8yPjxC8jM1KPXZ9Gp0wgAgoO/UjlZ4XN6oRR7\nNk0z3N+9aRqZmXo695vDtbg7KibL6RmpzfkmRfwxfvzxR44fP87HH3+MRqMhOTmZrKwsmjZtysmT\nJ/Hw8OD48ePUrVsXBwcHbt68CcBff/1FampqjuXVrVuXo0eP4u7uzg8//ECJEiWoX78+v/zyCx4e\nHly8eJGDBw8yZMiQXPPUrFmTChUqsH79egYMGADAqlWrOHPmDBMmTODixYvs378fS8sHf9Zly5YR\nFBTE2LFji+gVKrgKjb2o0Ngrx/RSNWvg+cEYAEqUKkmL+f55LsPKwZ72q02/0Lm51TLLgp2bmwlJ\nNGj3gdox8kUO9jEjvXv35tKlS/Tt2xc7OzsyMzOZOnUqVapUwd/fH41GQ6lSpZg3bx52dnbY2dnh\n6+uLh4cHlStXzrG8wYMHM2HCBPz8/LC3t2fBggUATJo0if79+5OVlcWUKVMem+mzzz5j1qxZbNq0\nCTs7O1xdXZk9ezY//PADXbt2NRRwgF69ejF06NBnqogL8awzsRqORnn0Sp1C/L/RIfvVjlBsljXL\n+aVrzmyrzVA7QrFKu7Ih3/OeScz/vqRXSqu/k1pa4s+gZcuW5Xql67lz56o+ZFAIc2dqoz2kiD+D\nRo8ezejRo9WOIcRzSfrEhRDChJlYDZciLoQQ2WlM7GAfKeJCCJGNtMSFEMKEPStHYuaXFHEhhMhG\nrrEphBAmzMRquBRxIYTITrpThBDChJlYDZciLoQQ2cnBPkIIYcJM7aIQUsSFECIbE2uISxEXQojs\nZMemEEKYMBOr4VLEhRAiOzkVrRBCmDDpThFCCBOmMbG2uBRxIYTIRqMpuiKekZHBxIkTuXbtGhYW\nFsybNy/H1bo+++wzQkNDURQFb29v3n777ccu07S+coQQoshpCnArmKCgIEqWLMmGDRsYMWIECxcu\nNHr8/PnzhIaG8sMPP7BhwwYCAwOJj49/7DKliAshRDaaAvwrqJCQENq3bw9A8+bNOXHihNHjjo6O\npKeno9PpSE9PR6vVYmtr+9hlSneKEEIYKbo9mwkJCZQtWxYArVaLRqNBp9NhbW0NQMWKFenUqRNt\n27ZFr9czatQoHBwcHrtMKeJCCJFNYfWJb968mc2bNxtNCwsLM7qvKMaH+MfExLB3715++eUXMjMz\n8fX1pXPnzpQrVy7P9UgRF0KIbAprdErfvn3p27ev0bSJEycSHx+Pq6srGRkZKIpiaIUDnD59mvr1\n6xu6UOrUqcP58+dp1qxZnuuRIi7ytKxZW7UjiCKSdmWD2hGeWU/T151fLVq0IDg4mFatWrF//36a\nNGli9Hi1atVYs2YNWVlZ6PV6zp8/n2P0yqOkiAshhJGiG+/RuXNnjhw5Qr9+/bC2tmb+/PkAfP31\n1zRq1AgPDw9atGhB//79AejTpw9VqlR57DI1yqOdMkII8RxLzfwt3/PaW7YuwiT5Iy1xIYQwYlrH\n3UsRF0KIbDRYqB2hQKSICyFENkW5Y7MoSBEXQohsNCZ2GkMp4kIIYUSKuBBCmCxTOxWtaaUVZikl\nJYXLly8DcOzYMVavXs3t27dVTlW0rl+/Tnh4OADbtm1jzpw5XLp0SeVUhWvMmDFqR3hKRXcWw6Ig\nRVyobuzYsdy8eZMLFy7w8ccfU7ZsWSZNmqR2rCL1v//9DysrK06dOsWWLVvo1KkTc+bMUTtWoUpM\nTFQ7wlPRaLT5vj0LpDtFqE6n09GkSROWLFnCW2+9Rbdu3QgMDFQ7VpGysLDg5Zdf5uOPP2bw4ME0\nbNgQvV6vdqxCdeXKFT755JM8H58wYUIxpsk/U+tOkSIuVKfT6di+fTs7duxgy5YtXL16leTkZLVj\nFSm9Xs/y5cv59ddfGTt2LOHh4aSmpqodq1DZ2tpSu3ZttWM8hWejmyS/5LB7obqzZ8+yZcsWXnvt\nNZo1a8b69eupVq0arVq1Ujtakbl+/Tq7d++mZcuW1KpVi507d1K9enVeeeUVtaMVGj8/P9atW6d2\njALLzAp78kz/z1JbvwiT5DOD2gGE+PLLL+nWrRsNGzYEYMCAASonKnqlSpWiatWqREREEBERATy4\nNJc5FfG6deuqHeGpmNo4cWmJC9WdOHGCffv2cezYMWrXrk23bt0ee/5kc+Dj40OVKlVwcnIyTNNo\nNM9sP3FhiYqKIigoiODgYHbt2qV2nFxlKZH5nlercSvCJPkjLXGhOk9PTzw9PYEHJ8WfNWsWcXFx\nvPnmmwwdOhQ7OzuVExY+KyurHBfJNVexsbHs2LGDoKAgoqOjeffdd1m5cqXasR5DWuJCFEhaWhq/\n/vorO3fuJCEhgU6dOtG5c2cOHz7Mzz//zKpVq9SOWOhWrlxJrVq1aNiwIRYW/5xw6UkXxTUla9eu\nZefOncTFxfH666/TpUsXpkyZwtatW9WO9lgKZ/M9r4aXizBJ/khLXKiue/futG/fnjFjxlCnTh3D\n9N69e3Py5EkVkxWdjRs3kpmZaTRNo9Gwb98+lRIVvqVLl1K+fHkmTJjAa6+9hrW1tYn0N5vWEENp\niQvVZWVlkZaWRlJSEgAZGRn4+/vz3XffqZys6CUlJaHVanF0dFQ7SqHT6XQcOHCAoKAg/vjjD1q2\nbMnJkyfZs2ePiRRz0yAtcaG65cuXExgYSGJiIpUqVeLatWv4+PioHatIHTlyBH9/f0qUKEFGRgZa\nrZZZs2YZRuiYA2trazp06ECHDh1ISUlhz549JCQk0KZNG7p06WL2O3GLixRxobrff/+dffv2GcYV\nR0ZGEhwcrHasIrVkyRLWrVtnGJ1y/fp1xo8fz/fff69ysqLh4OBA79696d27NwkJCezcuVPtSGbD\ntDp/hFnSaDQoioJer+f+/fu4ubnx559/qh2rSFlZWRkNL6xYsSKWlubVpkpOTuZ///sfaWlphmmR\nkZF8+umn+Pr6qpjMvJjXu0aYpI4dO7JmzRq6detGjx49KFeunFmN0shNlSpV8Pf3p3HjxiiKwtGj\nR6lWrZrasQrVrFmzcHV1xcbGxjDNzc3NcM6YadOmqZjOfMiOTfFMuXbtGnfu3OGVV14x651fmZmZ\nBAUFERERgUajwd3dnc6dOxsNNzR1b775Jps2bcr1sQEDBrB+/fpiTmSepCUuVOPn5/fYQr127dpi\nTFM8wsLCqF+/PocPH6ZMmTJG54c5dOgQrVu3VjFd4crKysrzsXv37hVjEvMmRVyoZvr06QBs2rQJ\nJycnmjRpQlZWFqGhody9e1fldEUjNDSU+vXr57nj1pyKeNWqVdm5cyedO3c2mv7999/j4uKiUirz\nI90pQnWDBg3K0eoePnw43377rUqJit7mzZvp27ev0bRVq1YxZMgQlRIVvlu3bjFhwgSSk5NxdXUl\nKyuL8PBwKlasyKJFi7C3t1c7olmQlrhQnU6nY926dXh4eKDVajl9+rTZtsQPHz7MoUOHCA4ONlyS\nDh6cX3znzp1mVcTLlSvHypUruXTpEpcuXUKj0TBs2DBq1KhhmCc2NpbKlSurmNL0SUtcqC4uLo61\na9cSFRWFoijUrFkTPz8/KlWqpHa0QpeSkkJkZCQfffQRw4YNM0zXaDS4ubmZ6EUUnl5uv8JEwUgR\nF8+0UaNG8cUXX6gdo9A8bHleuHAh1526tWrVUiGVekz1whHPEulOEc80c+tWWbNmDZMnT2bWrFk5\nHtNoNM9dq9Sch5EWFyni4plmbh/yyZMnA0jrUxQaKeJCFKOmTZsavpgSExOxsbEhKysLnU5HhQoV\n2L9/v8oJi5f05v57UsSFKEZHjx4FYPbs2XTv3h13d3fgwSXqnseTQjVt2lTtCCZPToAlnmmlSpVS\nO0KRiIiIMBRweHCJunPnzqmYqOicP3+eoUOHGk4vvHr1aiIjH1zHctSoUWpGMwtSxIXqUlJS+Oqr\nr5gzZw7woLX6cIfm0qVL1YxWZCpUqMD777/Pd999x+rVqxk/fjwlS5ZUO1aR+Oijj5gyZQrW1tYA\ntGzZktmzZ6ucynxIEReqmzhxIiVLluT06dMA3L59m/Hjx6ucqmgtXLiQPn36AA/OMdK1a1eWLFmi\ncqqiYWlpibOzs+F+rVq10Gql9BQWeSWF6lJTU+nfvz9WVlYAdO7cmfv376ucqmjFx8cTHR1NWloa\n9+7dIzIykuXLl6sdq0g4Ojry448/kpaWRlhYGAsWLKBcuXJqxzIbUsSF6rKysrhy5Yph1Mbvv//+\n2DPgmYMRI0YQHx9P6dKlKVOmjOFmjubNm8fNmzcpU6YMK1asoGTJksybN0/tWGZDRqcI1U2bNo3p\n06cTERFBixYtcHV1zfVgGHNSunRps+8yekir1VKnTh3ee+89ALZu3Wp24//VJIfdC6GCr776CkdH\nRxo2bGh0WTZzPOx+xIgRNGvWjMGDBwOwceNGDhw4YLbdR8VNWuJCda1btyY+Ph4LCws0Gg16vZ7S\npUtTqlQpJk+eTMuWLdWOWOgOHz4MYHRecXM97D45OdlQwAF8fHwICgpSMZF5kSIuVPf666/TtGlT\nwwURDh06xIkTJ/D19eX99983yyK+bt06UlNTiY6ORqvVUr16daNrUZoTBwcHAgIC8PT0JCsri6NH\nj+Lo6Kh2LLMhOzaF6k6dOkWbNm3QaDRoNBpatWrFsWPHePHFF82273T79u306tWLpUuX8umnn9Kj\nRw/27t2rdqwisWDBAhISEvj8889ZtmwZ6enpfPLJJ2rHMhvSEheqq1ixIqNGjcLT0xOtVktERAT2\n9vbs2bPHLM8pDrB+/Xq2bduGra0t8GCY5bBhw2jfvr3KyQrf4sWLmTp1qtoxzJYUcaG6Tz/9lIMH\nDxIVFUVmZiYdOnSgYcOGWFlZ0a5dO7XjFQmtVmso4AD29vZGOzjNiaIobNy4EXd3d8OxAGCeO3HV\nYJ7vGmFyNBoNL7zwAvDgHOK+vr788ssvKqcqOp6enrz77rs0atQIRVEIDQ3Fy8tL7VhF4vz585w/\nf95oZ6a57sRVgwwxFKobPXo09vb2HDt2jHbt2hEaGsrw4cPp2bOn2tGK1B9//EFERAQA7u7ueHp6\nqpxImCJpiQvVJSUlsWzZMvz8/Jg2bRp3795lxowZZl3EL168yJEjRxgzZgwAs2bNwtHR0SyvsZn9\nHOqZmZmkpqZSpUoV9uzZo3Iy8yBFXKguIyOD2NhYLCwsuHz5MhUrVjS6Erw5mjFjBuPGjTPc79On\nD/7+/gQEBKiYqmg8PIf6Q3/99Rfbt29XKY35kSGGQnX/+c9/iIiI4L333uPtt9+mTZs2vPbaa2rH\nKlKZmZlGfeCvvPLKc3OVG1dXV06ePKl2DLMhLXGhumbNmhn+/+jOzGXLljF69OjijlTk3N3dGTNm\njOEAmNDQUKOLRJiTMWPGGI33v3nzJnZ2diomMi+yY1M80wYNGmS2oxhCQkKIjIzEwsKCevXqGVrm\nsbGxVK5cWeV0hefYsWOG/2s0GhwcHHB1dTXbA7mKm7TExTPNnNsYzZo1M/oV8tCkSZPM6ovL1dWV\nNWvWcPbsWbRaLXXr1qVatWrY29urHc0sSJ+4eKY9j601c/vi+vDDD3FwcGDUqFEMHz4crVbLpEmT\n1I5lNqQlLsQzxty+uPEm0BoAAAluSURBVFJTUxkyZIjhfoMGDXjrrbfUC2RmpCUunmnm1ip9HmVl\nZRmunwoPTnhm7lduKk5SxIXqcruKz9ixYwGey7PdmdsX1/Tp01mwYAEtW7bk5ZdfZvHixcyYMUPt\nWGZDulOEanbv3s2qVau4cOEC4eHhhumZmZlkZGQAD85w+Lxp2rSp2hEKRUhICF9++SXr1q3ju+++\nY8iQIdjb23P16lViY2NxdnZWO6JZkCGGQlU6nY758+czbNgwwzStVkv58uXN8qx+7dq1y7PPW6PR\nmNVJv958800WLFhAtWrV2LVrFytXrmTz5s0kJSUxevRoszw6VQ3m9ykRJsXa2pq3336bvXv3kpyc\nbNSVYI4H+QQFBaEoCitWrMDV1ZUmTZoYrnYTHR2tdrxCVaJECapVqwbA77//Tvfu3dFoNJQuXRoL\nCwuV05kP6RMXqhs5ciTx8fGULl2aMmXKGG7myM7ODnt7e06cOEHnzp0pV64c5cuXp1u3bvz5559q\nxytUOp2OrKws0tLS+O2334wus3fv3j0Vk5kXaYkL1ZUuXZrx48erHaNYWVtbM3/+fDw8PNBqtZw+\nfRq9Xq92rELVvXt3evfujU6no1WrVtSsWROdTse0adPM9tzpapA+caG6r776CkdHRxo2bGjUD27O\nV35JSUlh+/btREVFAVCjRg169OhhdhcQjo2NJTk5GVdXV8O0zZs388Ybb6DVSkdAYZAiLlTn5+eX\nY5q5X/nl3r17hISEkJycbDTdnM+hLoqGdKcI1a1bt07tCMVuyJAhVKlSBScnJ8M0cztSUxQPKeJC\ndc/jlV+srKxYuHCh2jGEGZAiLlT3PF75pW3btvz22280bNjQaLidra2tiqmEKZI+cfFM6tevHxs2\nbFA7RpHp0KEDmZmZRtM0Gg379u1TKZEwVdISF6p7Hq/8kltXUWBgoApJhKmTlrhQ3fN45ZfTp0/z\nzTffkJiYCDy4WHRCQgJ79+5VOZkwNdISF6p7Hq/8Mnv2bMaNG8eCBQuYOXMme/fupUGDBmrHEiZI\nRtsL1T2PV36xsbGhadOmWFtbU7duXcaNGycnhBJPRVriQnXP45VfbG1t2bdvH1WqVGHRokVUrVqV\n69evqx1LmCBpiQvVPXrll7CwMLO/8suCBQtwdnZm+vTpWFtbc+7cOT7++GPgwYmjhMgv2bEpVHf+\n/HnmzJljOI+Ii4sLU6ZMeW4vGjBo0CCzPuWAKFzSnSJU5+Liwrx586hUqRIAUVFRz20BB/O7PJso\nWtKdIlT3ySefsGTJEsP977777rm8tuZD5jy0UhQ+KeJCdadOnWL+/PmG+3PmzCEsLEzFREKYDini\nQnVZWVlcuHDBcD88PPy57lJ4nrddFJz0iQvVTZ8+nZkzZ/L333+j0WioVasWM2fOVDuWasz5Yhii\n8MnoFPFMuHbt2nO1Y3Pr1q1kZGTQo0cPRo4cSWJiIm+88Qb9+/dXO5owMdKdIlT36aefPnc7Njds\n2ECvXr0IDg6mTp06bNmyhd27d6sdS5ggKeJCdSdP/l979xMS1RqHcfyZwUZamC1MpFDCEDIpUKpN\nCmmTiBUKLqfJWrgQIzfBgC4ySBDDIBctKzBBKRBjTIXjJtq0KMlFBC1EMDIoxlEpbUa7i2hA7h/u\ndLvz87XvB9yc2TwLeXjP7z3nvNO/3cam3+9XVlaWJicnde7cOUnS2tqacSq4iBKHud9xY7OsrEyn\nT59WIpFQaWmpBgYGUuMkIB3MxGHu9evX6u7uTm1sHjhwQMePH1dbW5t1tP9VPB5Xbm6upO97Avn5\n+crK4lkDpIf/GJg7dOiQbt26pfHxcY2Njen9+/fy+7f3TeLU1JRGRka0vLy86a6D1+2RLkocZhYX\nFzU5OaloNKq5uTnV1tZqeXl5Wx+Q/ENvb6+6urqUl5dnHQWOo8RhprKyUkVFRYpEIqqqqpLf71dj\nY6N1rIwoLS1VRUWFsrOzraPAcZQ4zPT09Cgajaqzs1PV1dWqr6+3jpQxVVVVqqmp0f79+zedds84\nBeliYxPm4vG4JiYmFI1G9erVK4VCITU1NW3rNxdra2vV1dWlPXv2bLpeUlJilAiuosSxpXz48EHR\naFRjY2Pb+vT39vZ29fX18TQK/jNKHDBw+fJlvXnzRgcPHtw0Trl9+7ZhKriIZQBgIBQKbSpv4Gex\nEgcMnD9/ntPt8UtQ4oCBSCSiZDKpw4cPa8eOHanroVDIMBVcxDgFMFBYWChJWllZMU4C17ESB4ws\nLCxofn5eR48e1devXxUIBKwjwUGsxAED9+/f18TEhL58+aLR0VHdvHlT+fn5amlpsY4Gx2zvrwwB\nW5TneRoaGtKuXbskSR0dHfI8zzgVXESJAwbW19clST6fT9L3AyGSyaRlJDiKcQpg4OzZs7pw4YLm\n5uZ07do1PX/+XM3Nzdax4CA2NgEDyWRSCwsLmpmZUSAQUFlZmXbu3Kndu3dbR4NjGKcAGZRMJvX5\n82ddvHhReXl5qq6u1okTJ5STk6NwOGwdDw5inAJk0NOnT3Xv3j3NzMzozJkzqet+v1/Hjh0zTAZX\nMU4BDIyOjqqhoeEvf/M8T8FgMMOJ4CrGKYCBvytwiYMhkB5KHNhiuDlGOihxYIv58ew48G9Q4gDg\nMEoc2GIYpyAdlDhgYH19XZ8+fZIkzc7OyvM8ra2tSZIuXbpkGQ2OocQBA1evXtX09LTm5+d15coV\nvX37VpFIRJJUU1NjnA4uocQBAx8/flQwGNSTJ08UDofV2tqqpaUl61hwECUOGFhdXdWLFy/0+PFj\nBYNBLS0taXFx0ToWHMQbm4CBZ8+eaXBwUHV1dWpoaNCdO3e0d+9eNTY2WkeDYyhxYAtIJBK6fv26\nbty4YR0FjuEDWICBhw8fqr+/X7FYTIFAQBsbGzp58qR1LDiImThgYHh4WJ7nqby8XC9fvlRfX5/K\ny8utY8FBlDhgIBAIKDs7W4lEQhsbGzp16hRnbOKnME4BDBw5ckQPHjxQZWWlmpubVVBQkHrZB0gH\nG5tABg0ODv7pWiwWkyTl5uZyug/SxjgFyKBYLJb6u3v3bqrAJSkejxsmg6tYiQNGwuGwBgYGrGPA\ncazEASN8Nxy/AiUOAA5jnAJkUFNTk3w+n759+6bZ2VkVFxdL+v4NcZ/Pp0ePHhknhGsocSCD3r17\n94+/79u3L0NJsF1Q4gDgMGbiAOAwShwAHEaJA4DDKHEAcBglDgAO+wOW5Y08T0hxUgAAAABJRU5E\nrkJggg==\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f846f62d7f0>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "J_9bjV0_NsIS",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Feature Importance  \n",
        "We will use Extra trees model\n",
        "The red bars are the feature importances of the forest, along with their inter-trees variability. We clearly see fields containing time indication that has to have some relationship with the target.: Day_of_the_week, Time_of_the_day_seconds and Day_of_the_month\n",
        "\n"
      ]
    },
    {
      "metadata": {
        "id": "888ltjjlOLbH",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "train_X = train_df.drop(\"Target_sign\", axis=1)\n",
        "train_y = np.log1p(train_df[\"Target_sign\"].values)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "adKD4Qf2N4PF",
        "colab_type": "code",
        "outputId": "2d8d4b16-fad1-4204-a337-6bfec6680874",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 875
        }
      },
      "cell_type": "code",
      "source": [
        "model = ensemble.ExtraTreesRegressor(n_estimators=200, max_depth=20, max_features=0.5, n_jobs=-1, random_state=0)\n",
        "model.fit(train_X, train_y)\n",
        "\n",
        "## plot the importances ##\n",
        "feat_names = train_X.columns.values\n",
        "importances = model.feature_importances_\n",
        "std = np.std([tree.feature_importances_ for tree in model.estimators_], axis=0)\n",
        "indices = np.argsort(importances)[::-1][:20]\n",
        "\n",
        "plt.figure(figsize=(12,12))\n",
        "plt.title(\"Feature importances\")\n",
        "plt.bar(range(len(indices)), importances[indices], color=\"r\", yerr=std[indices], align=\"center\")\n",
        "plt.xticks(range(len(indices)), feat_names[indices], rotation='vertical')\n",
        "plt.xlim([-1, len(indices)])\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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SCGMAAEgijAEAIIkwBgCAJMIYAACSCGMAAEgijAEAIIkwBgCAJMIYAACSCGMAAEgijAEA\nIIkwBgCAJMIYAACSCGMAAEgijAEAIIkwBgCAJMIYAACSCGMAAEiyj2E8c+bMTJw4MXV1dVm8ePFO\n21555ZVcccUVmTBhwk6fnz17diZOnJizzz47P/nJTzpvxQAA0AXK27vDokWLsnz58jQ0NGTp0qWZ\nMmVKGhoa2rbPnj07Q4cOzTPPPNP2uV/+8pd55pln0tDQkJaWlpx11lkZM2ZM1xxBJ1hW7AUAAFB0\n7YbxwoULM2rUqCTJ4MGDs379+rS2tqaioiJJ8tnPfjbr1q3Lj370o7Z9jjvuuBx99NFJkoMOOiib\nN2/Otm3bUlZW1hXHAAAAb1i7Ydzc3Jza2tq221VVVWlqamoL44qKiqxbt26nfcrKytKnT58kyZ13\n3pmTTz653Sju169PysvfPOFcXV35lpr/Vjse8803/80xvzcfu/nmm1/c+bvTbhj/uUKhsM/3feCB\nB3LnnXfm5ptvbve+LS2bOrqUParutEfas6amjT12fkdVV1d26uOZb7755vf02eabb37vnb+3IG83\njGtqatLc3Nx2e82aNamubj/9fv7zn+eb3/xmbrzxxlRW9ryfCAAA4E+1+6oUI0aMyPz585MkS5Ys\nSU1NTdtlFHuycePGzJ49O9/61rfytre9rXNWCgAAXajdM8bDhg1LbW1t6urqUlJSkqlTp6axsTGV\nlZUZPXp0Lr300qxatSrPPfdc6uvrc+6552bTpk1paWnJZZdd1vY4s2bNyqGHHtqlBwMAAK/XPl1j\nPHny5J1uDxkypO3j66+/frf7TJw48Q0sCwAAupd3vgMAgAhjAABIIowBACCJMAYAgCTCGAAAkghj\nAABIIowBACCJMAYAgCTCGAAAkghjAABIIowBACCJMAYAgCTCGAAAkghjAABIIowBACCJMAYAgCTC\nGAAAkghjAABIIowBACCJMAYAgCTCGAAAkghjAABIIowBACCJMAYAgCTCGAAAkghjAABIIowBACCJ\nMAYAgCTCGAAAkghjAABIIowBACCJMAYAgCTCGAAAkghjAABIIowBACCJMAYAgCTCGAAAkghjAABI\nIowBACCJMAYAgCTCGAAAkghjAABIIowBACCJMAYAgCTCGAAAkghjAABIIowBACCJMAYAgCTCGAAA\nkghjAABIIowBACCJMAYAgCTCGAAAkghjAABIIowBACCJMAYAgCTCGAAAkghjAABIIowBACCJMAYA\ngCTCGAAAkghjAABIkpQXewEky4q9AAAAnDEGAIBEGAMAQBJhDAAASfYxjGfOnJmJEyemrq4uixcv\n3mnbK6+8kiuuuCITJkzY530AAKCnaTeMFy1alOXLl6ehoSEzZszIjBkzdto+e/bsDB06tEP7AABA\nT9NuGC9cuDCjRo1KkgwePDjr169Pa2tr2/bPfvazbdv3dR8AAOhp2n25tubm5tTW1rbdrqqqSlNT\nUyoqKpIkFRUVWbduXYf22Z1+/fqkvLyswwdQLNXVlW+p+W+14zHffPPfHPN787Gbb775xZ2/Ox1+\nHeNCodDhIfuyT0vLpg4/7p5Ud9oj7VlT08YeO7+jqqsrO/XxzDfffPN7+mzzzTe/987fW5C3eylF\nTU1Nmpub226vWbMm1dV7T7/Xsw8AABRTu2E8YsSIzJ8/P0myZMmS1NTU7PWSiNe7DwAAFFO7l1IM\nGzYstbW1qaurS0lJSaZOnZrGxsZUVlZm9OjRufTSS7Nq1ao899xzqa+vz7nnnpvx48fvsg/szvDh\nR6W0tCSPPfabYi8FAOjl9uka48mTJ+90e8iQIW0fX3/99fu0DwAA9GTe+Q4AACKMAQAgiTAGAIAk\nwhgAAJIIYwAASCKMAQAgiTAGAIAkwhgAAJIIYwAASCKMAQAgiTAGAIAkwhgAAJIIYwAASCKMAQAg\niTAGAIAkwhgAAJIIYwAASCKMAQAgiTAGAIAkwhgAAJIIYwAASCKMAQAgiTAGAIAkwhiKavjwozJw\n4MBiLwMAiDAGAIAkwhgAAJIIYwAASCKMAQAgiTAGAIAkwhgAAJIIYwAASCKMAQAgiTAGAIAkwhgA\nAJIIYwAASCKMAQAgiTAGAIAkwhgAAJIIYwAASCKMAQAgiTAGAIAkwhgAAJIIYwAASCKMAQAgiTAG\nAIAkwhgAAJIIYwAASCKMAQAgiTAGAIAkwhgAAJIIYwAASCKMAQAgiTAGAIAkwhgAAJIIYwAASCKM\nAQAgiTAGAIAkwhgAAJIIYwAASCKMAQAgiTAGAIAkwhgAAJIIYwAASCKMe73hw4/KwIEDi70MAICi\nE8YAABBhDAAASZLyfbnTzJkz89RTT6WkpCRTpkzJ0Ucf3bbtkUceyXXXXZeysrKcfPLJueiii/LS\nSy/liiuuyPr16/Pqq6/moosuykknndRlBwEAAG9Uu2G8aNGiLF++PA0NDVm6dGmmTJmShoaGtu3T\np0/PTTfdlIMPPjiTJk3K2LFj88tf/jKDBg3K5ZdfntWrV+eCCy7Ifffd16UHAgAAb0S7l1IsXLgw\no0aNSpIMHjw469evT2tra5JkxYoV6du3bw455JCUlpZm5MiRWbhwYfr165d169YlSTZs2JB+/fp1\n4SEAAMAb1+4Z4+bm5tTW1rbdrqqqSlNTUyoqKtLU1JSqqqqdtq1YsSL19fVpbGzM6NGjs2HDhnzr\nW99qdyH9+vVJeXnZ6zyM7lddXfmWmF9aWtKpj/dmm7+D4zff/N4323zzze/d83dnn64x/lOFQqHd\n+9x111059NBDc9NNN+Xpp5/OlClT0tjYuNd9Wlo2dXQpe1TdaY+0Z01NG3vs/I7Yvr2Q0tKSTnu8\nN9v85LW/mI7ffPN712zzzTe/987fW5C3eylFTU1Nmpub226vWbMm1dXVu922evXq1NTU5IknnsiJ\nJ56YJBkyZEjWrFmTbdu2ve4DAACArtZuGI8YMSLz589PkixZsiQ1NTWpqKhIkhx++OFpbW3NypUr\ns3Xr1ixYsCAjRozIO9/5zjz11FNJkueffz4HHnhgysrePJdJAADQ+7R7KcWwYcNSW1uburq6lJSU\nZOrUqWlsbExlZWVGjx6dadOm5fLLL0+SjBs3LoMGDUpNTU2mTJmSSZMmZevWrZk2bVpXHwcAALwh\n+3SN8eTJk3e6PWTIkLaPjzvuuJ1evi1JDjzwwHzta1/rhOUBAED38M53AAAQYQwAAEmEMQAAJBHG\nAACQRBgDAEASYQwAAEmEMQAAJBHGAACQRBgDAEASYQwAAEmEMQAAJBHGAACQRBgDAEASYUwvN3z4\nURk4cGCxlwEA9ADCGAAAIowBACCJMAYAgCTCGAAAkghjAABIIowBACCJMAYAgCTCGAAAkghjAABI\nIoyhV/POfwDw/wljoNfygwEAf0oYAwBAhDEAACQRxgAAkEQYAwBAEmEMAABJhDEAACQRxgAAkEQY\nAwBAEmEMAABJhDEAACQRxgAAkEQYAwBAEmEMAABJkvJiL4DOV11z0D7fd8dPRh3ZJ0ma1mzo0P0B\nAHo6Z4wBACDCGCii4cOPysCBA4u9DABIIowBACCJMAYAgCTCGAAAkghjAABIIowBACCJMAYAgCTC\nGAAAkghjAABI4i2h6QLekhoAeDNyxhgAACKMAQAgiTAGAIAkwhgAAJIIYwAASCKMAQAgiTAGAIAk\nXseYt5iOvh6y11EGAHZwxhgAACKMAQAgiTAGAIAkwhgAAJIIYwAASCKMAQAgiTAGAIAkwhgAAJLs\nYxjPnDkzEydOTF1dXRYvXrzTtkceeSTnnHNOJk6cmG984xttn//Rj36Uj3zkI5kwYUIeeuihTl00\nAAB0tnbf+W7RokVZvnx5GhoasnTp0kyZMiUNDQ1t26dPn56bbropBx98cCZNmpSxY8emf//++cY3\nvpG5c+dm06ZNmTNnTk455ZSuPA4AAHhD2g3jhQsXZtSoUUmSwYMHZ/369WltbU1FRUVWrFiRvn37\n5pBDDkmSjBw5MgsXLkz//v1z/PHHp6KiIhUVFfniF7/YtUcBAABvULth3NzcnNra2rbbVVVVaWpq\nSkVFRZqamlJVVbXTthUrVmTz5s15+eWXc+GFF2bDhg255JJLcvzxx+91Tr9+fVJeXvYGDqV7VVdX\nmm/+G1ZaWtKpj2f+m2v+Dr15fm8+dvPNN7+483en3TD+c4VCYZ/ut27dunz961/PCy+8kPPPPz8L\nFixISUnJHu/f0rKpo0vZo+pOe6Q9a2raaH4PnN8ds/c2v6O2by+ktLSk0x7P/DfX/OS1/zD01vm9\n+djNN9/84n7v2ZN2f/mupqYmzc3NbbfXrFmT6urq3W5bvXp1ampq0r9//xxzzDEpLy/PO97xjhx4\n4IFZu3btGzkGAADoUu2eMR4xYkTmzJmTurq6LFmyJDU1NamoqEiSHH744Wltbc3KlSszYMCALFiw\nIF/+8pfTp0+fXHnllfn0pz+d9evXZ9OmTenXr1+XHwwUW3XNQR26/46fTDu6X9OaDR26PwDQvnbD\neNiwYamtrU1dXV1KSkoyderUNDY2prKyMqNHj860adNy+eWXJ0nGjRuXQYMGJUnGjh2bc889N0ly\n9dVXp7TUSyYDANBz7dM1xpMnT97p9pAhQ9o+Pu6443Z6+bYd6urqUldX9waXBwAA3cNpXAAAiDAG\nAIAkr+Pl2oCeyy//AcDr54wxAABEGAMAQBJhDAAASYQxAAAkEcYAAJBEGAMAQBJhDAAASYQxAAAk\nEcYAAJDEO98Bncg77wHwZiaMe7llxV4AAEAP4VIKAACIMAYAgCTCGAAAkghjAABIIowBACCJMAYA\ngCTCGAAAkghjAABIIowBACCJMAYAgCTCGAAAkghjAABIkpQXewFQTMuKvQAAoMdwxhgAACKMAQAg\niTAGAIAkwhgAAJIIYwAASCKMAQAgiTAGAIAkwhgAAJIIYwAASCKMAQAgiTAGAIAkwhgAAJIIYwAA\nSCKMAQAgiTAGAIAkwhgAAJIIYwAASCKMAQAgiTAGAIAkwhgAAJIIYwAASCKMAQAgiTAGAIAkwhgA\nAJIIYwAASCKMAQAgiTAGAIAkwhgAAJIIYwCKYPjwozJw4MBiL6NoevvxQ09VXuwFAMWzrNgLAIAe\nxBljAACIMAYAgCQupQCKaFmxFwAAf8IZYwAAiDAG6LW8MgLF4muPnkoYAwBAhDEAACQRxgAAkEQY\nAwBAkn0M45kzZ2bixImpq6vL4sWLd9r2yCOP5JxzzsnEiRPzjW98Y6dtL7/8ckaNGpXGxsbOWzEA\nAHSBdsN40aJFWb58eRoaGjJjxozMmDFjp+3Tp0/PnDlz8v3vfz//9V//lWeffbZt2w033JC+fft2\n/qoBAKCTtRvGCxcuzKhRo5IkgwcPzvr169Pa2pokWbFiRfr27ZtDDjkkpaWlGTlyZBYuXJgkWbp0\naZ599tmccsopXbd6AADoJO2+811zc3Nqa2vbbldVVaWpqSkVFRVpampKVVXVTttWrFiRJJk1a1a+\n8IUvZN68efu0kH79+qS8vKyj6y+a6upK8803/00+v7S0pFMf7/Uq1vxiHr/n3vEXc/4O5vfu+bvT\n4beELhQK7d5n3rx5ef/735+3v/3t+/y4LS2bOrqUParutEfas6amjeb3wPndMdv8nju/o7ZvL6S0\ntKTTHu/1qK6uLNr8Yh6/597x9+bjN7+48/cW5O2GcU1NTZqbm9tur1mzJtXV1bvdtnr16tTU1OSh\nhx7KihUr8tBDD2XVqlXZf//9M2DAgJxwwglv5DgA9qq65qAO3X/HtWQd3a9pzYYO3R+AN4d2w3jE\niBGZM2dO6urqsmTJktTU1KSioiJJcvjhh6e1tTUrV67MgAEDsmDBgnz5y1/OpEmT2vafM2dODjvs\nMFEMAECP1m4YDxs2LLW1tamrq0tJSUmmTp2axsbGVFZWZvTo0Zk2bVouv/zyJMm4ceMyaNCgLl80\nAAB0tn26xnjy5Mk73R4yZEjbx8cdd1waGhr2uO8ll1zyOpcGAADdxzvfAQBAhDEAACQRxgAAkEQY\nAwBAEmEMAABJhDEAACQRxgAAkEQYAwBAEmEMUDTDhx+VgQMHFnsZAPwfYQwAABHGAACQRBgDAEAS\nYQwAAEmEMQAAJBHGAACQRBgDAEASYQwAAEmEMQAAJBHGAACQRBgDAECSpLzYCwB4q6iuOahD999x\nZqIj+zWt2dChGQDsO2eMAQDJcjR4AAAgAElEQVQgwhgAAJIIYwAASOIaY4psWbEXAADwf5wxBgCA\nCGMAAEgijAEAIIkwBgCAJH75DuAtwxuMALwxzhgDAECcMQYommXFXgAAO3HGGAAAIowBACCJMAYA\ngCTCGAAAkghjAABIIowBACCJMAYAgCTCGAAAkghjAABIIowBACCJMAYAgCTCGAAAkghjAABIIowB\nACBJUl7sBUBvtqzYCwAA2jhjDAAAEcYAAJBEGAMAQBLXGAPQSaprDtrn++44K9ORfZKkac2GDt0f\n2NXw4UeltLQkjz32m2IvpcdxxhgAACKMAaDXGT78qAwcOLDYy4AeRxgDAECEMQAAJBHGAACQRBgD\n0Au5xhbYHWEMAAARxgAAkEQYAwBAEmEMAABJhDEAACQRxgAAkEQYAwBAkqS82AsAgM5QXXPQPt93\nx1mhjuyTJE1rNnTo/sCbyz6F8cyZM/PUU0+lpKQkU6ZMydFHH9227ZFHHsl1112XsrKynHzyybno\noouSJLNnz86vfvWrbN26NZ/5zGcyZsyYrjkCAADoBO2G8aJFi7J8+fI0NDRk6dKlmTJlShoaGtq2\nT58+PTfddFMOPvjgTJo0KWPHjk1zc3OeeeaZNDQ0pKWlJWeddZYwBgCgR2s3jBcuXJhRo0YlSQYP\nHpz169entbU1FRUVWbFiRfr27ZtDDjkkSTJy5MgsXLgw5513XttZ5YMOOiibN2/Otm3bUlZW1oWH\nAgDF0dFLMlzKUVzDhx+V0tKSPPbYb4q9FHqYdn/5rrm5Of369Wu7XVVVlaampiRJU1NTqqqqdtlW\nVlaWPn36JEnuvPPOnHzyyaIYAIAercO/fFcoFPb5vg888EDuvPPO3Hzzze3et1+/Pikvf/PEc3V1\npfnmm29+r5ptfg+eX1LSocd5vWes04EG2Ov80tfWW6zns9jzBw4cmCRZtmxZUeYX+/h3KPb83Wk3\njGtqatLc3Nx2e82aNamurt7tttWrV6empiZJ8vOf/zzf/OY3c+ONN6aysv0Db2nZ1OHF70l1pz3S\nnjU1bTS/B87vjtnmm1/M+T31715vn98bvvb2Nr+jtm8vpLS0pNMez/w31/zktSgu1vy9BXm7l1KM\nGDEi8+fPT5IsWbIkNTU1qaioSJIcfvjhaW1tzcqVK7N169YsWLAgI0aMyMaNGzN79ux861vfytve\n9rZOOgwAAOg67Z4xHjZsWGpra1NXV5eSkpJMnTo1jY2NqayszOjRozNt2rRcfvnlSZJx48Zl0KBB\nba9Gcdlll7U9zqxZs3LooYd23ZEA0CHLir0AgB5mn64xnjx58k63hwwZ0vbxcccdt9PLtyXJxIkT\nM3HixE5YHgB0vmXFXgDQI3lLaAAAiDAGAIAkwhgAAJK8jtcxBgD4U975j7cKZ4wBACDCGAAAkghj\nAABIIowBACCJMAYAgCTCGAAAkghjAABI4nWMAaDXWVbsBXQyr6P85jJ8+FEpLS3JY4/9pthL2YUz\nxgAAEGEMAABJXEoBAPCGuJTjrcMZYwAAiDAGAIAkwhgAAJK4xhgA4E2tO65x7i3XNwtjAABet7dS\nmLuUAgAAIowBACCJMAYAgCTCGAAAkghjAABIIowBACCJMAYAgCRexxgAgG60rNgL2AtnjAEAIM4Y\nAwC9zLJiL4AeyxljAACIMAYAgCTCGAAAkghjAABI4pfvAAC61bJiL4A9csYYAAAijAEAIIkwBgCA\nJMIYAACSCGMAAEgijAEAIIkwBgCAJMIYAACSCGMAAEgijAEAIIm3hAZ6sWXFXgAAPYowBqDbLSv2\nAgB2w6UUAAAQYQwAAEmEMQAAJBHGAACQRBgDAEASYQwAAEmEMQAAJBHGAACQRBgDAEASYQwAAEmE\nMQAAJBHGAACQRBgDAECSpLzYCwAAoPssK/YCejBnjAEAIMIYAACSCGMAAEgijAEAIIkwBgCAJPv4\nqhQzZ87MU089lZKSkkyZMiVHH31027ZHHnkk1113XcrKynLyySfnoosuancfAADoadoN40WLFmX5\n8uVpaGjI0qVLM2XKlDQ0NLRtnz59em666aYcfPDBmTRpUsaOHZu1a9fudR8AAOhp2g3jhQsXZtSo\nUUmSwYMHZ/369WltbU1FRUVWrFiRvn375pBDDkmSjBw5MgsXLszatWv3uA8AAPRE7V5j3NzcnH79\n+rXdrqqqSlNTU5KkqakpVVVVu2zb2z4AANATdfid7wqFQoeH7Ms+/fr1SXl5WYcfew8DO+dx9qLa\n/J45vxtmm29+Mef32L97vX1+R2cPHPja/y5bVpz5r5Pj773ze+zfvU7WbhjX1NSkubm57faaNWtS\nXV29222rV69OTU1N9ttvvz3usyctLZs6vPjOVF1dmaamjb1yfm8+dvPNN9/3nmLYvr2Q0tISx99L\nj9/84n/v2ZN2L6UYMWJE5s+fnyRZsmRJampq2q4VPvzww9Pa2pqVK1dm69atWbBgQUaMGLHXfQAA\noCdq94zxsGHDUltbm7q6upSUlGTq1KlpbGxMZWVlRo8enWnTpuXyyy9PkowbNy6DBg3KoEGDdtkH\nAAB6sn26xnjy5Mk73R4yZEjbx8cdd9xuX4rtz/cBAICezDvfAQBAhDEAACQRxgAAkEQYAwBAEmEM\nAABJhDEAdLtf/eq3WdbBd30Dup4wBgCACGMAAEgijAEAIIkwBgCAJMIYAACSCGMAAEgijAEAIIkw\nBgCAJMIYAACSCGMAAEgijAEAIIkwBgCAJMIYAACSCGMAAEgijAGAbvarX/02y5YtK/YyYBfCGAAA\nIowBACCJMAYAgCTCGAAAkghjAABIIowBACCJMAYAgCTCGAAAkghjAABIIowBACCJMAYAgCTCGAAA\nkghjAABIIowBACCJMAYAgCTCGAAAkghjAABIIowBACCJMAYAgCTCGAAAkghjAABIIowBACCJMAYA\ngCTCGAAAkghjAABIIowBACCJMAYAgCTCGAAAkghjAABIkpQUCoVCsRcBAADF5owxAABEGAMAQBJh\nDAAASYQxAAAkEcYAAJBEGAMAQBJhDAAASYQxAAAkEcZF9fTTT+fxxx/PY4891vanuyxdunSXzy1Y\nsKBbZre2tua5555LkixatCi33HJL1q5d2y2z6Rm2bNmSlStXFnsZ3WrVqlXFXkKv9pvf/KbYS2iz\natWqPP7440le+7vQHe64445umcPebd26dZfPrVu3rggr6X67+x64uxYptl4bxvPmzcsdd9yRLVu2\n5FOf+lTOPvvsfO973+u2+RdeeGGmTZuWW2+9Nbfddltuu+22fOc73+m2+VdddVV+8YtfJEnWr1+f\nyZMn5/vf/363zL7sssuyZs2aPPPMM5k1a1aqqqpy1VVXdcvsSy+9tFvmtOfFF1/M4sWLkyR33XVX\nZsyYkT/84Q/dMvv3v/99/u7v/i4TJ05Mktxyyy1ZsmRJt8xOknvuuScTJkzIhRdemCSZPn165s2b\n123zk+I8B//yL//SpY+/r4r9vS8pztf/v/3bv3Xp4++rW265JZdddln+9V//Nclr6/r2t7/d5XPv\nvvvuLp+xL4rx9bfjuS6mrVu3ZtOmTfnkJz+Zl19+OZs3b87mzZuzcePG1NfXd9s6/ud//ifXXntt\npkyZkquuuqrtT1dau3ZtnnnmmVxyySVZunRpnn322Tz77LN5+umn80//9E9dOvv16LVh/P3vfz9n\nnXVW7rvvvhx55JGZO3du5s+f323zW1pacvvtt+f6669v+/O1r32t2+bffPPNuf322zNjxoycd955\nGTFiRLd8c05eO0PywQ9+MD/+8Y/zyU9+Mh/5yEfyyiuvdMvsnvKT+ec+97nst99++fWvf525c+fm\ntNNOy4wZM7pl9he/+MV8/vOfz/77758kOfHEEzN9+vRumZ0k3/3ud9PY2Jh+/folee256O4wK/Zz\nUEzF/t6XFPfrv9geeOCB3H777enbt2+SZMqUKfnpT3/a5XM3b968U5T8+Z/uUoyvv+48vj352c9+\nls985jNZvHhxxo0blzPOOCNnnHFGzjrrrLz3ve/ttnVMnjw5gwYNypgxYzJ27Ni2P13pD3/4Q26+\n+eYsW7Ys11xzTdufa6+9NuPHj+/S2a9HebEXUCylpaUpLy/P/Pnzc/HFFydJt8VZ8tp/iJ955pkc\nccQR3TYz2fkbxKWXXpqvf/3rGT58eN773vfm2Wefzbve9a4uX8OWLVvyox/9KPfcc0/mzp2blStX\nZuPGjV0+N0n+93//N7Nnz97j9u46q1dWVpahQ4dm1qxZueCCCzJ8+PBs27atW2aXl5dn8ODBbbff\n9a53pbS0+35GLisry/7775+SkpIkaYvT7lSM5+C3v/1tzjnnnF0+XygUUlJSkjvvvLNL5+9Q7O99\nSXG+/p944okcf/zxu3x+x/O/cOHCLp2/w47j3PH1/8orr+z2n9c7244oKRQKu2wrKSnJrbfe2uVr\nSIrz9bd69ep897vf3eP2T3ziE106P0lOPfXUnHrqqbnrrrvy0Y9+tMvn7cmAAQNSV1fXrTOPPfbY\nHHvssRkzZkw+/OEPd+vs16PXhnFtbW1Gjx6dQYMGZejQobntttty6KGHdvncD33oQykpKUmhUMi/\n//u/p7KyMmVlZd32zfmaa67Z5XMtLS255ppruu2b49SpUzN37txMmzYtFRUVueuuu3LZZZd1+dwk\nOeCAA7r9h5Hd2bZtW2644YY8+OCDueyyy7J48eK89NJL3TK7srIyd955ZzZv3pynnnoq999/f/r3\n798ts5Nk2LBh+dznPpfVq1fn29/+dhYsWLDbYOlKxXgOjjjiiFx33XVdOmNfFOt7358qxtf/Mccc\nk9tuu61LZ+yLM888M+eff36WL1+eqVOn5tFHH80FF1zQ5XOHDBnSbfG7N8X4+nv11VfT0tLSpTP2\nVVVVVS6++OJs3Lhxpx9Suuv/m6OOOiqzZs3Ksccem/Ly/5+AI0eO7PLZ3/ve9zJ8+PAcdNBBXT7r\njSgp7O7Hx15i/fr1bf+c9cILL6SmpmanL5Su9Oqrr2a//fbb6XMtLS1t/7zcXbZu3dptx7zDJZdc\nkvHjx+eUU07p9rOF9fX1PeI/ji+++GLmz5+fESNG5Igjjsi9996bgQMH5j3veU+Xz37ppZfyH//x\nH3nyySez33775X3ve1/q6+vTp0+fLp+9w+OPP77T/GOOOabbZie7fw4mTZqUAw88sMtm9pSvvWTn\n733PP/98ampqdvl+1JV2fP2feOKJede73tUtX/895fnfunVrVq1alcWLF2f//fdPbW1tDjjggLzt\nbW/r0rk95fj/+7//O4cddli3fv31lGNPktNPPz2f//znc/DBB+/0+e46YbOn64mvvfbaLp/98Y9/\nPE8//XTe8Y53ZL/99uv2fy3bV70ujOvr69v+CWt3uvqntq1bt+bVV1/Npz/96dx4441tPzFu27Yt\nH//4x7vtFyQeffTRzJgxI1u2bMl9992Xr3zlKznuuONy4okndvnsJ554Ij/96U+zaNGiHHHEERk/\nfny3nTGcNWtWrrjiim6ZtTcTJkzI+PHjc8YZZ6SmpqZbZ997770ZM2bMTj8Qffe7/4+9d4+LMf//\n/x8znViFkLSOlaWSpCjHpdZpS84rdEIHx6TVUpLKIeV8SMvKqWKzFkklSrY2VLvrGNoIRSWr1Cgx\nUzO/P+Y317vpxPvzfb9e19iZ++32vr23a/bm8dLOXPO8Xq/n8/E4QeU4ERCfGHh4eKB79+7MtYCA\nAGzcuJG49sd6DUm2ErV2hFpWVtbki5IUL1++xP79+1FVVYW9e/ciMTERpqamUv89SPPu3TvcuHGj\nSQvVtGnTiGlev34dI0aMaPa1W7duEX84q6urA5/Ph4eHByv3/oqKCnTq1KnJ9efPnyMpKQmLFi0i\nqi/B2dkZR44coboh4+Pjg+3bt1PTa43FixfjwIEDrK7h+fPnyMvLA5fLhZGREXR0dKjoFhcXN7lW\nXV2N/v37U9H/VOSuMH706BEA4JdffkHXrl1haWkJoVCI7Oxs8Hg8/PDDD0T109LScPToUdy5cwdd\nunRhrnO5XFhYWCAkJISovgQHBweEh4djxYoViI6ORnl5OZYuXYpTp05R0Zdw7949bNiwAWVlZZg9\nezYWLlxIdecSENvFJCQkIDk5GRcvXqSiWVZWhitXruC3336DSCTCpEmTMHHiRKirqxPXtrS0hJ6e\nHsLCwtCrVy8A4i8rWkd548ePh4aGBtzc3GBjYwOA3o5O4+lvyUOyZOeC5lHzmzdvkJycjISEBJSX\nlyM5OZmKrqurK5ydnXHo0CHExMTgxo0biIiIoLqjZm9vjx49ekg9FHI4HKrOHQ8fPkRiYiIuXryI\nXr164ejRo0T1Gt77tbS0mMKY9r0fAF69eoWkpCQkJSWhqqoK06ZNw5IlS6hoL168GPn5+TAwMJDa\nJaY5fA6ICzLJPf/w4cPE9SQ9zvn5+aisrIS5uTmUlJSY12ltTERGRiIpKQlmZmbg8/m4d+8evvvu\nO8ybN4+4No/Hw4ULF5i2FoFAgLi4OKSnpxPX/m+Qux5jyXHF33//DX9/f+a6qakp3NzciOvLSgO+\nsrIyNDU1mcKgc+fOre6k/y+pra1FWloakpKS8Pr1a9jY2MDGxgbXrl3DsmXLiH9BAeIn18TERCQk\nJKCwsBCLFi2icnOUoK2tjXnz5mHevHnMw8HWrVthbW0Nb29vorvI/fr1w9q1a+Ht7Q1HR0dMnz69\n2YEcUujo6ODHH3/Ehg0b8PvvvyMgIIDae69h8VdTU4PCwkJwuVz06dMHbdq0Ia5fXV2NlJQUJCQk\n4O+//0Z9fT327duHIUOGENeWIBQKMWbMGERGRgIAhg8fjv3791PTBwAVFRXs2LGDqiYAPH36FAkJ\nCUhMTISqqioqKyvx888/U9ktb+3ef/36deL6lZWVuHTpEnPPmzBhAng8HnVHkoULF1LVa8j79++R\nlpaGCxcuICsrCzY2NtQeCCTFoJaWFrS0tMDj8ajoNiY1NRWnT59mivK6ujo4OjpSKYy9vLwwePBg\nJCYmwt7eHunp6QgICCCu+98id4WxBD6fj+joaAwePBhcLhf37t2j+kbV1NRktQG/R48e2LNnD968\neYOkpCSkpqZS63GaMmUKxo8fjxUrVkgdocyYMQO3bt0iqh0VFYWkpCSUlZXh22+/xZYtW+Dv70/d\nS1FyfJmSkoJu3brB3d0dVlZW+Ouvv7BixQrExsYS1Tc0NERMTAxCQkLw+++/4927d0T1GiISidCu\nXTuEhYXhwoULcHR0RHV1NTV9AIiPj0d4eDj09fWZsBEfHx+MHz+emOayZctw69YtjBw5Es7Ozhgx\nYgS+++47qkUxIH4ovnHjBoRCIV6/fo2UlBSoqalRXYOVlRXS09Ob7Jq1bduWmOa0adNQXV2NyZMn\nY9++ffjqq68wbdo0qi0kgHj4NCwsjLGOFAgE+OOPP4jvmo0aNQq9evXCmjVrMHr0aHC5XKKtKy1h\nZmaG5ORklJWVwdXVFfn5+dDV1SWqeeXKFSQlJeH69euwsLDAjBkz8OLFC6oWgRIHjpKSkiavKSkp\nQSgUUnMHaqjD5XKpbUwIhUKsWLECf/zxBxYuXAhHR0esXLkS48aNo6L/qchtYbxnzx5ERUUhPDwc\nIpEIenp62L17NzV9icF2t27dqGk2ZOPGjbhw4QLMzc1x69YtWFtb49tvv6WifenSJdTW1qKqqgol\nJSUQCAQIDg7GkSNHiPeZ7tu3D1paWli9ejW++eYbKdswmqxatQpTp05FZGQkM3RTX1+PYcOGYeTI\nkUS1LSwsAIiLkI0bN+LSpUvYtWsXUc2GNJzAt7Ozw+DBg6l5aEs4ceIEzp8/zxRiNTU1cHV1JVoY\nv3//Hmpqamjfvj3U1dWhoqLCyntv8+bNzEOxm5sbTExMqAzeNOTUqVNNLMo4HA5RP18TExNcvXoV\neXl50NfXR/fu3Vn5/fv6+mLGjBk4fvw4li1bhitXrlAJoAgNDUVCQgL8/f1hZWXFtDHRJiAgAJ06\ndUJOTg5cXV2Rk5ODAwcOEHVs8fT0RJ8+fbBz505mniUiIoKYXmt4e3vj/v37zANZSUkJ+vbti8rK\nSnh5eRF/WPn2228xc+ZMDBo0CEKhEHfu3MHs2bOJakoQCATIy8tDmzZtcO3aNfTs2RNFRUVUtP8b\n5K7HuCF5eXnMjq3kBjl06FAq2h4eHtSLgcbcunULJSUlsLW1xatXr6gNge3fvx9nz55FZWUlvvzy\nS5SUlMDe3p5KfyGfz8dvv/2GhIQE/Pnnnxg1ahRu3bqFy5cvU/+SfPTokdSu0ZYtW4gO4BQXF6N7\n9+4tDqCR9rBOTU3FuHHjWvQTpdVjB4inoxsnPTo6OhJPn6yoqEBSUhISEhJQVlYGPp+P48ePU/EP\nl9Bwx0py71NSUoKWlhZVP2tA7I7B5XKhoaFBRa+urg6///47EhIScO3aNQiFQmzbto3ZQaWBi4sL\njh8/LvV+c3d3x6FDh6joV1VVMb3td+7cgYODA2bOnEntPTh//nwcO3ZMaq6A9GevrKwMSUlJuHDh\nAmpqamBjY4NLly4hKSmJmGZL+Pr6YuHChejXrx8A8YxLVFQUfH194eLigl9++YX4Gl68eIGHDx+C\nw+HAyMiIil0jn8/HkydP8ObNG3Tq1AmbN29GZWUlHB0dqRXmn4rc7hh7eHiAx+NBW1ubaWXgcDjU\nCmNdXV14eXmx1oAfFhaG0tJSFBUVwdbWFqdOnUJVVRXWrVtHXDsjIwNXrlxhboz379+nNnikqqqK\nCRMmYMKECaiursbly5fx+vVrjB07Fra2ttSGf9avX48nT57gyZMnMDExQW5uLvEe96ioKPj5+Ul5\nWZeWlqJz585QU1Mj3sYjcSCQBT/RwYMHY9GiRRg6dChEIhFycnKotDR06tQJjo6OcHR0xIsXL3Dh\nwgV4e3tDTU2NmmUR2ztWgLinNjg4GGpqahAIBOByudiwYQPMzc2J6iorK8PKygpWVlaora1Famoq\nTp48iaCgIFy9epWotgTJ+61jx444deoUevXqhRcvXlDRBoAOHTrA3t4e9vb2KCsrQ0JCAlavXo2z\nZ89S0RcIBODxeMxGREFBAfh8PlFNbW1tLFiwAAsWLGD6zOvr6zFz5kzMmDGD6kP548ePmaIYAPT1\n9fHw4UO0bduWSshTZmYmqqqqYGNjA39/f0RGRsLNzY1oO0NqaipCQkKgpaWFyspKbN26VSY8tVtE\nJKfY29uzqr9v375m/0cLR0dHqf8XiUSiuXPnUtG2t7cXCYVC0dy5c0W1tbVUtVvin3/+ER0/fpya\nnuTvK/n9l5SUiNatW0dU8/r164xeXV2dyMnJSTRhwgTRuHHjRL/99htR7YbweDxRQUGBSCQSibKy\nskRHjx4VlZeXU9MXiUQioVAo+uOPP0RHjx4VHT16VPTnn39S0a2vr29y7cOHD6KHDx9S0ReJRKI1\na9aI/v77b+bnx48fi9avXy969+6d6LvvvqOyBnt7e1FZWRnzc0lJCZV7AI/Hk/pZIBCIysvLRW/e\nvCGuLeHly5eiu3fvikpLS0W+vr6ixYsXi65evUpF+/r161I/V1RUNLlGmj/++EM0bdo0kampqWji\nxImiSZMmUfv8Nebu3buikJAQqpobN24UTZ8+XRQcHCzauHGjyN7eXrRmzRrRuXPnRBs3biSuP3v2\nbNHbt29Fly9fFq1bt04kEAhETk5ORDXt7e1FlZWVIpFIJHr+/LnI1dWVqN7/K3K7Y2xmZsZKJLOE\n5cuXIzs7Gw8fPgSXy4WxsTHMzMyo6Uv8lCVP7RUVFdRiYSdOnIjjx4/Dzs4OU6dORefOnYkO3TTk\n7du32LBhAzZs2MBo3r9/H1FRUVR8dCXU19czA2cVFRXQ0dFBXl4eUc1du3YxXp6XL1/Gu3fvkJyc\njKqqKixfvpxK8hEg3rF0d3dHfX09tm7dChcXF/j5+eHgwYNU9AGxbVtMTAzVwbf8/Hx4enrizJkz\njC3fgwcP4OfnR9XXlO0dK0DsStGwdUtHR4e4r21OTg7WrFmDCxcuML//goICeHt7Y/fu3cQDNhq2\nMLVt2xbV1dVwdXUlqtmQkydP4vz58xg4cCDz96+trUV4eDjevn2LCRMmUFnHkCFDcO7cOZSXl0NV\nVZVKGw2fz0dERASWLVvGWMQ9evQIV69ebTHwghTr1q1Dfn4+CgoKIBKJMG3aNBgbG4PP51M5rVFV\nVYW6ujpSU1Nhb28PZWVl4p97FRUVJtClR48e1CPo/1vktjBOTU3F0aNHoa6uzrQy0IhklhASEoLn\nz5/DwsIC79+/R0REBAYMGABvb28q+gsXLoS9vT1KSkrg5uaGJ0+eYO3atVS0FyxYwPzzmDFj8ObN\nGyqJbwCwYcMGGBgYSFlzDRgwAIaGhggLC6NmHePo6IikpCQ4OjrCzs4OysrKLYYP/K9QU1NjfIsz\nMjIwZcoUcDgcdOzYUaqdhzR8Ph+WlpbYu3cv5s+fDzs7O2rHuBK6d++OVatWYeDAgVJeqiSPVENC\nQhAaGirlVW1kZIT169dj06ZN1CzTTE1NMWPGDJiamoLL5SI3Nxd6enqIi4ujlkDYo0cPBAcHw8LC\nAiKRCFlZWcx7kxS7d+9m7vkS+vfvj/DwcGzevJm4XWPDFiYJHA6Hmof2mTNnEB0dLeUT/+WXX+Lg\nwYPw8PAgXhhbW1u3OMfB4XCQmppKTHvr1q0AIOUA1bt3b1RXVyM8PJxxjCBJbGws5syZg7CwMKnf\nQ25uLpKSkqi18XXp0gULFixATU0NzMzMEB8fT3xjqvF/dzaGXv8b5LYwvnz5Mqv69+/flxpC8vDw\ngKOjIzV9TU1NREVF4cmTJ1BRUYGuri5xH1e2UwcBoLCwENu2bWtyff78+VT7zOzs7Jh/tra2Rk1N\nDfEdKz6fD6FQiA8fPkD3SKoAACAASURBVCA9PR3u7u7MazTt2vh8PuLj45GYmIgzZ87gxYsXTRLQ\nSNOzZ08AoGoTJxAImi08zc3NqTriNLdjJbkH0LLv2rhxIxISEvDXX38xsx2kXRI4HA769OnT5Lqe\nnh7xHldA2kO7srISRUVFjIc2jWAfVVXVZsOT1NXVIRQKiesnJCRAJBLh4MGDMDAwYMK1srKyUFhY\nSFT71q1bOHPmjNQ1VVVV+Pr6wsHBgUphLOnpb3hawwY//PADKisroaenB0A8dE3SEQQQF/+zZs0C\nIH44efr0KWbNmiWzkdByWxg/fPgQISEhKCoqQn19Pfr16wd/f3/o6+tT0a+rq8P79++ZYvTdu3fU\njjEBsY9rWFgYNDQ0MHToUNTW1sLExIToceb69esBtJw6SIPWvgBoFIds7ppMmTIFM2bMAJ/Px+jR\no5mCICAggGpLQWBgIM6cOYOgoCCoq6vj/PnzWLlyJTV9AK1+ES5btozI7m1L76/6+nrGnYQGPB4P\nf/zxBzME+ffff1NLn7pz5w4GDRqEa9euQVNTE6NHj2Zey8zMJNrOU1tbi7q6uib3OIl1JC0OHjyI\nX375BV999RVEIhEKCgowd+5c4m0VIpGoWfehwsJCKoWxpCi/efMmvv/+e+a6nZ2d1CkiCVo6EeNy\nuRAIBES1JUje69OnT2fNEQoAVq9eLeUAQuO0lnTc+f8auS2MN23aBD8/PxgbGwMAbt++jeDgYGqT\nki4uLpgyZQr69OkDoVCIoqIi4nHUDZH4ZvJ4POTk5GD//v24desWbt68SUyT7dRBQLxTmJSU1GR3\n6uTJk1Se5FvbNXn27BlRbQcHB4wdOxZv376FgYEBAPGuyZAhQzBz5kyi2g0xNDSUcj9puFNPqij9\nbyD1kDZq1CgEBQXBx8eH2SGsqKhASEgIbG1tiWg2B5vpU9nZ2Rg0aFCLLjQkC2NbW1usWLECPj4+\nzG7ZgwcPEBYWBmdnZ2K6jbl06RIuXrwIVVVVAMCHDx+oFMZLly7F/Pnz4eTkBENDQ9TX1+Pu3bs4\nefIkM3tAA1VVVYSGhkqFa5HeFNLU1MSff/7ZZAPgt99+Q5cuXYhqN4ZNRyhAnLw3Z86cJm1kJFs5\naIfo/L8it4WxkpISUxQD4uKMZt+LjY0Nxo4di2fPnoHL5aJ3797UBtAAcSvJrVu3UFRUBGVlZQwZ\nMoRaccpm6uC6deuwevVqHDt2DAYGBhAKhbh79y50dHSIHycB7O6aAM3foL777jviup8KWzGpDSF1\nH1i5ciUOHToEOzs7qKmpob6+HvX19XBwcKA6hMVm+pSHhwcA8fBz4/cd6Sh4V1dXdO3aFX5+figu\nLgYg7nV2cXGhFm4EiAcNG+/Qkk5+A4Cvv/4a+vr6iI2NRWZmJjgcDvT09BAVFQUdHR3i+hL27t2L\n+Ph45OTkABD/3Uk/DK9duxaenp7Q19dnHgru3LmD0tJS4r3ljcnNzUV0dDScnJwAiMNHaMQxS/j6\n66+paX2uyG1h3L59e0RGRjIpYFlZWczUJA2uXLmCc+fOsRYJvWvXLnTt2hWTJ0+GmZkZtRYSgN3U\nwc6dO+Pw4cOMhzCHw4Grq6vUF5MkCIMkbOyafA7I+lDG/wtKSkpYvHgxFi9ezPQ2N+4tlYSgkITN\n9Klr164hMzMTycnJePr0KXO9vr4eSUlJxB8O7ezspPr7GyMZkCIJn8+HtbU1TExMIBQK8eDBA+jr\n68PLywuA+P5ICsnQaUsEBgY2OyT4v4TL5UJbW1uq3/nKlStE+9t79+6NuLg4XLt2jbnvOzo6YuTI\nkcw9h8/nM7v4JGHTEQpgv5Xjc0Buk++qq6tx/Phx5ObmgsPhwMTEBE5OTmjXrh0V/YkTJyIoKKjJ\nMQ5N+7iKigrcvHkTt27dQn5+PjgcDutpfLJwlO7s7Ez8AaW6uhrx8fHMAJRk8ElDQ4PaDVoWofG7\nl+U1kNZmO32quroa9+/fx8aNG6V2yTkcDgYMGMCafaYEGv/tJTulLSHZrGEDGn9/e3t79OjRQ6oY\n43A41FwZWoLW5z4lJQU//vgjSkpKYGxsjCdPnsDPz49oHH1DGrZynD17Fvv27aPayvE5ILc7xkpK\nSjAwMMCyZcsAAHFxcVR3qwwNDWFmZgY1NTVqmg2pqKjA7du3cffuXcY/l5ZlWmvIwlE6jWdFdXX1\nFo/P3NzcWC8O5RmaJ0eNIfnek4X0qaqqKlhaWmLXrl0yeTpA47P/5ZdfIi0trclpIQ1nBFlARUUF\nO3bsYHsZTaC1Rzh+/HiMHDkSjx8/hqqqKvr06UPcEaohbLdyfA7IbWHs7e2N4cOHMz9/+PABq1at\nwo8//khFf/To0bC2tkafPn2kJmZpfVEtX74cFhYWsLS0xJIlS6T6m2m0ErSELHxZsr0GOT3EAUCv\nKH348CHi4uKaFCdbtmzBvn37qKyhOUi+9yIjI3Hu3Dl06NABL168QFBQECIjI4npNcfx48exdu1a\nZvi3ITS8fD8Gjc++h4cHJk6cSH3oS1awsrJCeno6zM3Npb77aM7YNAet+35mZiZiY2NZa6Nku5Xj\nc0BuC+O3b9/CxcWF+dne3h4JCQnU9A8ePIht27ZBS0uLmmZDTp482eJrfn5+rH9ByTNsF+akaGxs\n35jVq1dTK0p9fHzg5OSEbt26UdGTBWQhfUoSItTQ01fe+PLLL5l+Ynnk1KlTqKurk7rG4XBw5coV\nllZEl82bN8Pf3x/a2tqs6DcM93J1dcXTp0+phXt9LshtYayuro6YmBiYmZkxdlk0oiklGBoawsLC\ngngM6v8Fed6xBBR/f1K0ZofX+IuSNN26dSM+ZPV/geR7TxbSp4YNG8boVlZWok2bNhAKheDz+ejW\nrRuuXr1KfU0NofHZnzlzJhYvXgxDQ0OpHVNZaKWg8fdnO1yrJWjd93v37o1Ro0ZR0WoOtls5Pgdk\nryqjxPbt23H48GHs3r0bXC4XAwcOZGIjaVBfX49JkybBwMBA6uZIciL5U2Fzx5LN/k4Jw4YNY1X/\n31qYT58+nfnnR48eMaEWfD4foaGhVG3jjI2NERYWhiFDhkg9nJL00W1IdXV1k6PUL7/8kqgrgyyk\nT2VlZQEQ+8hPmTIFJiYmAMT2hUlJScT1JeTl5aG6ulrq9z906FAqXvJ79uxhtZWirq4OycnJKCsr\ng6urK/Lz86GrqwsVFRUcOXKEmK7E8WLmzJnNfsewnX7Wt29fon++JOlWW1sbXl5eTVpJaCWvZmZm\nYseOHXj16hUA8X3Hx8cHlpaWVPQ/B+S2MNbQ0MCcOXPw4sULDBkyhLoTQGuG8mz2+NKguroaMTEx\nKC8vh7+/P7KysmBkZIT27dtTO0rPz89HaGgoampqcOrUKRw7dgxDhw7FgAEDmIFMtiB9g2ab9evX\nM3Z5JiYmyM3NpeahLUHypdA4aZBGYbxu3Tqkp6dDW1ubKcwkham1tTUxXVlKn8rNzZWagjczM8Ou\nXbuoaHt4eKCqqkrqKFsSSy0p1EnSo0cPeHt7E9dpiYCAAHTq1Ak5OTlwdXVFTk4ODhw4gJ07d0oF\nPvyv8fT0BCD2MWaL33//HbGxsU0eiqKiohAYGEhUW5I0qaWlBS0tLdYGzbdu3Yrt27czJ3h5eXlY\nvXo14uPjWVmPLCK3hfGxY8eQnJyM2tpanD9/nun3lRjQk6Y1Sx62e3xJ71j6+vpixIgR+O233wCI\nm/9XrVqFQ4cOEdVtyMaNGxEUFISgoCAA4lSygIAA/Pzzz1T0X758if3796Oqqgp79+5FYmIiTE1N\n0b17d+I3aLZ5/PgxTp48CScnJxw4cAClpaWIiIigoi15AJbEk7PBgwcPkJGRQf1kRpYetrt16wZP\nT08pH+/27dtT0ebxeDh16hQVrebo3bs3fHx8YGJiwsqOYWlpKbZs2cK4Ejg6OraYRPi/RLJDXl5e\njsTExGYHX0kTEhKCtWvXsjJb8CmtMjR8pLW0tKTa2gwMDGTq3iALyG1hnJqaitjYWObmsHbtWsyZ\nM4daYdwabB+lk24lqKmpwbx583Dx4kUA4hRAWgWpBGVlZalQk759+4LL5VLT9/f3h7OzM/Mw0KlT\nJ/j6+srFUFJ9fT0TcFFRUQEdHR3GMpA0fn5+2LFjB2xtbcHhcJg2Asn/0xgAMjAwYHyE5ZUdO3Yg\nMzMTBQUFEAqFmDx5MrVELjMzMzx69Ig1z2RNTU1oamqytmMoEAjA4/GYB7OCggLw+Xxq+j/88APc\n3d1ZaSXp2bMnRo8eTV33U2kYekMKHR0deHh4YPjw4RAKhfjrr7+goaHBtHrQekCTZeS2MJakjElu\nDh8+fKA+ANQSNHaS2GwlEAqFKCoqYv6eGRkZTSJSSaOhoYFff/0VtbW1uHPnDlJSUtC5c2dq+kKh\nEGPGjGHssoYPH856sAktHB0dcfHiRTg6OsLOzg7KysoYMWIEFW2Jf2paWhoVveZ4/vw5xo0bh969\ne0NJSYlqj6+s8M8//6CwsBC1tbUQiUS4f/8+7t+/T2UALTU1FUePHoW6ujqzY8vhcHDjxg3i2oB4\n5/Dly5estfF5e3vDxcUFz549w6RJk8DhcLBp0yZq+np6ei32GZNGV1eX1f5eWaBbt27o1q0bampq\nAPwnv0DS6qFAjgvjyZMnw9nZGYWFhQgMDERWVhbmz5/P9rKowWYrQUBAANavX4/c3FyMHDkSBgYG\nzfqakmTLli04fvw4NDU18dNPP2HQoEFUjvIkKCsr48aNGxAKhXj9+jVSUlJYC3uhTcNIXmtra9TU\n1KBjx45U13Dy5EmcPn26yXEujR3j0NBQ4hqyzuLFizF69GhWjrTZdkVoro2va9eucHd3p6I/ZMgQ\nnDt3DuXl5VBVVaXqxgSIv3unTZuG/v37SxWnNO6/Ghoa0NDQkIkgKbZo7eGT7fkaWUFuC2MHBweM\nGTMGd+/ehaqqKpYsWSIznqY0WinYbCXo27cvjh07RkWrJb744gtYW1vDwsICQqEQHA4HDx48wNCh\nQ6nob968GXv27MGbN2/g5uYGExMTqoU5m5w5cwbR0dFNBmBo+pieOHECBw4coHpKEBsbizlz5iAm\nJqbZ3TK2I3Fp0rFjR6xatYoV7YcPHyIkJARFRUWor69Hv3794O/vL3U/JElLbXy0CmM2HwoBYPfu\n3fDw8KDq4S8ZaJ80aRI1zc+Rt2/fsr0EmUBuC+PMzExUVVXBxsYG/v7+iIyMhJubG8aNG8f20qjY\nhbHZSjBmzBj8888/UFJSAofDQX19PTp27IgOHTpg7dq1VDweXVxcIBQKpfo8JZPpNKirq2Oezhv2\nuQqFQqq9zmxw+PBhhIeHs/ogamJigjZt2uCLL76gpikZcGnOz1lW2rhoMWzYMJw4cQLm5uZSdnk0\nHFk2bdoEPz8/GBsbAwBu376N4OBgagPPbLfxnTx5Ej/++CPVh8KG6OvrU7VmBMSuE35+fs0OtslC\n4qIEtueLFIiR28J43759OHz4MFJTU6GkpISYmBgsXLiQWmHMtl0Ym60E3377LYYNG8ZYY2VmZuLm\nzZuYM2cOPD09qRTG9fX1zLABG3h7e+P+/ftMsVRSUoK+ffuisrISXl5emDZtGmtrI02fPn2gp6fH\n6hr69+8PKysrdOnSRarPl+SumWToZ/r06az7OLPNtWvXAEDKDYFWgaKkpMQUxQBgampKtd+1cRtf\ndna2VAoraQYOHEj9obAhmpqacHBwgLGxsVQrBckTEz8/PwDNJy7Snu14+PAhysvLMWrUKOzfvx/3\n79+Hq6srzM3NifpIK/h05LYwVlVVhbq6OlJTU2Fvbw9lZWXmSZ4GbNuFtWvXjrVWgtu3b8PX15f5\nefTo0Thw4AC8vLyofUFNnz4dR44cgaGhodSOFa0dY11dXWzcuJHZPSwoKEBUVBR8fX3h4uLyry6M\nO3XqBHt7e5iamlL7YmxMbGwsEhMTWYlklwUfZ7aJjo5GTU0NCgsLweVyqaZvtW/fHpGRkYxlZlZW\nFtVgIXt7e6k2vsWLF6Nt27bU9Nl4KGyIhYVFq3alJElPT8eePXtQVVUFQOzQ0a1bN6q9tcHBwdi+\nfTuuXbuGvLw8BAYGYs2aNTh27BhRH2kFn47cFsZdunTBggULUFNTAzMzM8THx1O9ObFtF7Z48WJU\nVlY2CRmgURjq6Ohg2bJlMDMzA5fLRW5uLtq1a4fLly/jyy+/JK4PAHFxcaivr8ft27eZazRbKR4/\nfix1pK6vr4+HDx+ibdu2VB/Q2MDc3Bzm5uasrmHw4MHQ1NRkZdeMTR9nWSE+Ph7h4eHQ19cHn8/H\nixcv4OPjg/HjxxPXDg0NxfHjx/Hjjz+Cw+FQ6++vq6sDn8+Hh4cHIiMjYWVlBUB8ejV37lxqASxs\nPhQC4k2JW7duoaSkBLa2tnj16hW6du1KRXvfvn3Ys2cPfH19ER4ejsuXL6Ndu3ZUtCWoqqqiR48e\niIyMxNy5c6GtrU3dlaklZCF5VhaQ28J427ZtyM/PZ450+/bti507dwIA7ty5g0GDBhHVZ9su7M2b\nN6yZ3G/btg2///47CgoKUFdXhwkTJsDc3BwqKipEk78aIhQKqXsnN8TU1BQzZsyAqakp83Cgp6eH\nuLg4DB48mLV1kUTyuZIF/96ioiJYWVmhV69e1C3T2PRxlhVOnDiB8+fPM5sRNTU1cHV1JVoYSwaw\nXr58iYkTJ2LixInMa6WlpcT7mzMyMnD06FHcvXsXtra2zHUul0vtgRxg96EQAMLCwlBaWoqioiLY\n2tri1KlTqKqqkkpCJEXbtm3Rs2dPCIVCaGpqwt7eHgsWLMDkyZOJa0tQUVHBunXrcPv2bQQEBCAj\nI4Nqj/nDhw8RFxfXbMAKreRZWUduC2NlZWXGvw+A1D/v2LGDeK8b23Zho0aNYtXknsPhMAbvPB4P\nc+bMaRLPS5IRI0bg9OnTGDhwIPXhH0AcC5yfn4+CggKIRCJMmzYNKioq0NXV/de2UWRnZ2PQoEEt\npmzRiGOWsHXrVmpajWHTx1lW4HK5Uid07dq1k/ockoDtASxra2tYW1vj/PnzmDp1arP/TmpqKvE5\nFzYfCgFxHHh0dDTjyuHp6Yl58+ZR0dbW1kZcXByMjIzg4+ODHj16oLy8nIq2hD179uDGjRtYuXIl\nlJSUoKKigu3bt1PT9/HxgZOTk8y4cMkiHJFiDLIJTk5OVBLI8vLyUF1dzfT4AuR7XIcNG8Y4IFRV\nVUFDQ0Pq5kjD5H758uVo164dcnJyYG1tjezsbLi5uVEtCCU35YbQnE7m8Xi4cOECY6ouEAgQFxeH\n9PR0KvpsUlxc3KSXXElJCVpaWsTbiSRDOC1B2zJPIBCw4uPMNtu2bcPjx48xdOhQiEQiZGdnw9jY\nGCtXrmR7aazi7OxM/B5UXFzc5Fp1dTX69+9PVFfC3LlzERUVBVdXV0RFRaGiogLu7u44c+YMce36\n+npUVVWhffv2SEhIQGVlJSZMmECthQ8AXr58icuXLzfZsaURbgMArq6uOHz4MBWtzxW53TFuDRoD\nYGz1+GZlZQEQfyE3bvSnlXxTVVWF8PBwODk5ISAgADweD4GBgVQLY7ank728vDB48GAkJibC3t4e\n6enpCAgIoKbPJt9//z1rjhyS4/O0tDRwuVxYWFgwhRmt9LGTJ0/il19+YdXHmW1++OEH/Pnnn8jN\nzQUALF26FGZmZlS0ZcEusiVo7FNpaGiw+lC+cOFC2Nvbo6SkBG5ubnjy5AnWrl1LRbu0tBRpaWlS\nRenZs2epFaUAu+E2AGBsbIywsDAMGTJE6pSG5omdrKMojFmCrR7furo6CAQCuLu7IzIykrk51NfX\nw9nZmcoAiEAgQHFxMZSUlPD06VPo6OhQyYhvCNvTyUKhECtWrMAff/yBhQsXwtHREStXrpQJH23S\nsOnIMXbsWADA8ePHcfToUea6ra0tFi1aREy3IWyEi8gajx8/xvXr17FixQoAwIYNG6ChoUGltUsW\n7CJbgsamDNsP5d27d0dMTAweP37MtI/RciTx8PDAxIkTmTY+NmAz3AYAXr16BQBNWhcVhfF/UBTG\nzUDjqZ2tHt/GAyCSv6uSkhK1ARAvLy/k5uZi6dKlcHd3R3V1NfWserankwUCAfLy8tCmTRtcu3YN\nPXv2RFFRETV9NpEFR47KykpcvXqVGX68d+8eXr58SUWbjXARWSMwMBDe3t7Mz7NmzUJwcDBiYmKI\na8uCXSSbsP1QHhoaiiNHjsDExISKXkO+/PJLeHl5UddtCFvhNjNnzmTaKBsjD+/7/wa5LYxjYmJg\nY2PT7IS8nZ0dMd2GPb4RERHUe3xlYQBk+PDhUloNCQ8Pp3KsxfZ08vr161FRUQEfHx9s3rwZlZWV\ncHZ2pqLNNrLgyBEWFoaIiAjs3LkTIpEIenp6CA0NpaLNto+sLFBXV4chQ4YwPxsZGVFL/ZIFu8iW\noPE7YPuh/IsvvsCECRNgYGAg1c63Z88e4tozZ87E4sWLYWhoKOWhTrOVgq1wm7179xL98/9NyO3w\n3YEDB/Dbb79BQ0MDtra2mDBhAtUdnJZ6fDU1NamtoSVoDICwre3j44NRo0bh3r17qKqqQo8ePZCW\nlob4+Hji2h8jMDCw2cn5fxMNHTl69eoFY2Nj8Pl8an2+jREIBAgODsamTZuIa02aNAkHDx5s4iMr\nTzvImzdvRllZGczMzCAUCpGdnQ09PT2sWbOGuHZdXR1jFyl5/1lZWaG2tpaKO0Z9fT0qKyvRuXNn\nPH36FAUFBRg9ejTU1NSQlpZG3LIyLy8PFRUV6Ny5M/NQ7ujoiNmzZxPVlZCTk9PsdQsLC8ZSjxST\nJk3CxIkTm/gm0z6xZCvcRsGnIbeFsYRXr17h6tWriI+Ph7a2NubMmUM0lae1Hl+aJu+tQcuVg01t\nWZhObgk2H0xIEhsbizlz5iAsLKzZozuayXenT5/G3r178ebNG6iqqkIoFGLs2LHYvXs3cW0/Pz/4\n+fmhffv2xLVkmRs3buD+/ftQUlLCwIEDmR1k0sVRXV0dLl68iFevXsHV1RX5+fnQ1dWlljrm7e0N\nW1tbGBgYYMmSJbCxscHff/9N5b0nobq6mhlAk5xYyMO9z83NDZGRkcT+/E+BzXAbBZ+G3LZSAEBZ\nWRkuXryI1NRUdOzYEWPHjsXZs2eRkpICf39/Ipqy0OP7MdjsN6Kl/c8//zSxzKE9nSxvSIqdhv3F\nbHHq1CmkpqbCzc0N0dHRuHLlCl68eEFFm20fWVlh+PDhUm1VEvz8/IgWRwEBAejUqRNycnLg6uqK\nnJwcHDhwgAl4Is3r168xbtw4/PTTT3BycsLs2bOxcOFCKtqA2EM9PT0d2traACBT7z/S+3S9e/eG\nj48PTExMpFopaO4YsxFuo+C/Q24LYwcHBwgEAtjZ2WHv3r1Mr/GUKVNgb29PTFcWenwVsG+ZI4+M\nHj0aAHD16lXW+93U1NSgpqYGgUAAoVCIb775Bk5OTnBxcSGuzWa4yOcA6eKotLQUW7ZsYbzMHR0d\nWwydIcH79+/x119/IT4+HlFRUeDxeKisrKSm/+DBA2RkZMjkwBXpNWlqakJTUxM8Ho+oTmuwEW6j\n4L9Dbv9rbNiwAfr6+lLXIiIisHTpUipH+S0VxYA4oYnNwpjN7hpa2mxb5sgzHTt2xM6dO2FiYiJ1\nfE3TLmjgwIGIiYnBqFGj4OLigm7duuH9+/fU9Pft24eHDx+Cy+XC2NgYnp6e1LRlHdLFkUAgAI/H\nY3QKCgrA5/OJajbEy8sLkZGRcHd3R6dOnRAREUF18NbAwABv3ryRiWh22ixfvhzZ2dlSnz1a/tkS\nzMzMsGjRIibcJicnB+bm5lTXoKB15LYwfvHiBX744YcmPrZLly5lbQBIAsnicNeuXfD29mb+vzkW\nLFhATB8QP5SsX79e6trKlSuxe/duartpbFnmSJAcX7b02r+Zly9fori4GA8fPoSKigo6dOgAgG5h\n7Ovrywz7WVpa4s2bN0ws8507dzBo0CBi2v7+/pg7dy58fX0hEAiQk5MDf39/HDp0iJimgv/g7e0N\nFxcXPHv2DN9++y0A8TAgLUaNGiXllezu7o7g4GBqAUfPnz/HuHHj0Lt3b5lr5SF97wsJCcHz589h\nYWGB9+/fIyIiAgMGDGjxu5AEDcNtOBwOFi9erCiMZQy5LYzZ9rFtDZI7JleuXEFBQQFu3ryJZ8+e\nNXl9z549xKaiL126hKNHj+LRo0e4e/cuc10ykAiIrZRowJZljgQnJ6cWPVuPHDlCZQ20+fDhAwIC\nAlBYWIh+/fpBJBLh0aNHGDp0KNatW0d9PZIH4Ma9/Tt27CD6Pqivr2cS+ABxuMgvv/xCTO9zg3Rx\nVFNTg3PnzqG8vBwqKirUhyBbGvykhbe3N9NfLGsMGzaM6J9///59nDhxgvnZw8MDjo6ORDUlSFok\nJfqS07K8vDzk5eVRd8ZQ0DJyWxiz7WPLFtHR0Xj8+DFKSkqofxAnTpwIKysrhIaGwtXVlbnO5XKb\nWFeRZurUqZg1axZVzYZ0794dq1atwsCBA6XaCRwcHKhNx9Nm27ZtMDQ0bHIqcOzYMYSEhGDDhg0s\nrUwa0oWZqqoqLl68CEtLS4hEImRlZbF+SiVLkC6OYmJiMHjwYNaSB9kc/ASAnTt3UglSaYlvvvmm\nyTUlJSX07NkT33//PVHturo6vH//nrFHe/fuHbVQobdv3wIAE8WtQHaR28JYW1sbcXFxMDIygo+P\nD3r06IHy8nK2lwWA7BezpqYmhg4dirNnzyInJwcPHjyg2mulqqoKd3d3pKSkSDlCAHRN1q9fv47B\ngwc36TOnRc+ePQGIbZPkhZs3bza7Mzx//nzMmDGDhRU1D+ke15CQEOzZswc//vgjuFwuBg4cSPUo\nn02sra1b/P1yOBykpqYSj2Wvrq7GmDFj0KtXL6ioqFBvJVBVVWVt8BMAtLS0MGfOnCYP5bTsEmfP\nng0NDQ2mQM7INzOjnwAAIABJREFUyEBFRQUsLS2xadMm/Pzzz8S0XVxcMGXKFPTp0wdCoRBFRUX4\n4YcfiOk1ZPr06cwa/vnnH+jp6TH9zlOmTKGyBgWfhtwWxmFhYaiqqsLkyZMZH9sDBw4Q15WFHl9A\n/OVcVFQES0tL6r1WS5YsYd0RIjc3F3Z2dmjbti1UVVWpJQ9KWL58OV6+fIkXL15gyJAhrIZb0KKh\nPVJjuFwuxZWwQ0lJCfPPy5cvZx4KORwO6urq2FoWVRISEiASiXDw4EEYGBjA0tISQqEQWVlZKCws\npLKG7du3t/ga6f5yQBwJ3njw88OHD0Q1G/L1119T02qOjIwMqXaG7777Ds7Ozli0aBFxbRsbG4wd\nOxbPnj0Dl8tF7969pRwiaODt7Q13d3fU19dj69atcHFxgZ+fHw4ePEh1HQpaRu4K4/Dw8BZfo+Fj\ny2aPb0PY7LWSBUeIy5cvs6p/7NgxJCcno7a2FufPn8e2bdvQtWtXuLu7s7ouknTu3BnZ2dmwtLSU\nup6RkSFTPY+kTmw8PT3B4XAgEAjw9OlT9OzZE/X19SguLoahoaFc9BlL0v1u3rwpdWxuZ2dHZUMA\nQKvhIST7yyX3W8lpkaamJvNZMDExIaLZHNOnT8etW7dQUlICW1tbvHr1qkkSHEnU1NQQEhLCRHLf\nu3cPAoEA165dI5b+6OTk1OpJEM1AJT6fD0tLS+zduxfz58+HnZ0dzp49S01fwceRu8JYErl89+5d\nvHnzhrFMyc7OppL8w2aPb0PY7LVi2xECEDsj7N+/H1VVVdi7dy8SExNhampKNHGrIampqYiNjWW8\nVNeuXYs5c+b8qwtjf39/eHp6QldXF4aGhhAKhbh37x6Ki4tx+PBhqmu5evUqrKyspK4lJCRg8uTJ\nsLOzI6J55swZAOKp9IMHDzInJsXFxdi3bx8RTVlFVVUVoaGhGDx4MFMc0br/tAbJNraGvaXnzp1j\njtYBMO5INAgLC0NpaSmKiopga2uLU6dOoaqqitoA7N69exEXF4fs7GyIRCL07t0bERERqK2tJZb+\nJ3FB+uWXX9C1a1fmpCI7O5u6pzGfz0d8fDwSExNx5swZvHjxguk/ViAbyF1hLClG09LSpL6M3d3d\nsWTJEuL6bPf4Smiu14pWjxnbjhCAuEhzdnZmLLI6deoEX19falHYkiJAsovx4cOHf/1xes+ePXHu\n3DlkZmbiyZMn4HA4mDt3LkaOHEktbODu3bu4d+8eoqKipFob6urqcPjwYUyePBmzZ88muoZnz55J\ntRF179692dOjfzN79+5FfHw8cnJyAAC6urrYv38/y6si21/e8DQyOzubtZTN3NxcREdHMw/lnp6e\nmDdvHjV9LpcLHR0dqKurM9fS09OJ2tV99dVXAIC///5bKtXW1NQUbm5uxHSbIzAwEGfOnEFQUBDU\n1dVx/vx5rFy5kuoaFLSO3BXGEl69eoX8/HwmnrawsBDFxcXU9Nns8QWke604HA769OnD9FqRTt6j\nVXy2hlAoxJgxYxAZGQlAHE9L84t58uTJcHZ2RmFhIQIDA5GVlYX58+dT02cLDoeD0aNHMyl4tNHS\n0sIXX3wBgUAgtYPH4XAQFhZGZQ2DBg3CrFmzMGjQIHA4HNy/fx/9+/enoi0rcLlcaGtrSx2dX7ly\nhZqXL9uwmTonsceUrKGiooJqj/OCBQvQo0cPqfYNWr8PPp+P6OhoqZMK2jvGhoaGmDVrFqqrq/HH\nH38wNYgC2UFuC+O1a9fC398fJSUl4HA40NbWprZjCrDb4yvhiy++gJGRUZPrpJP3hg0bxtwI6+rq\nUFNTgx49elDt+1VWVsaNGzcgFArx+vVrpKSkQE1NjZq+g4MDxowZg7t370JVVRVLlixRxFNTQEdH\nB9OnT8eYMWOkkr8EAgGCg4MxfPhw4mtYt24dCgoK8PjxY4hEInz33XdMYUxj+EsWYLM4ao1/e7gO\nIP7d29vbo6SkBG5ubnjy5AnWrl1LTV9FRQU7duygpteQPXv2ICoqCuHh4RCJRNDT0yPWvtESHh4e\n4PF4Td77jf3UFbCH3BbGw4cPx+nTp5t9LTw8nPgxF5s9vh+D9JdDVlaW1M95eXmIj48nqtmYzZs3\nY8+ePXjz5g3c3NxgYmKCLVu2UNPPzMxEVVUVbGxs4O/vj8jISLi5ubEaBS5PpKWlMf/92QhZ0NfX\nb9YqkHS4iKzAZnHUGqT6ywFg5syZ4HA4EIlEePr0KeOjTtsubsKECRg1ahQeP34MVVVV6OrqUt0U\nsLKyQnp6OszNzaWcami4Q2hra7doz7Zs2TIqp4Y8Hg+xsbHEdRT835Hbwrg1JH1vJGGzx/dj0N65\nMTAwQHBwMFXNzp07w9vbG126dMGTJ0/w5MkTJpqYBvv27cPhw4eRmpoKJSUlxMTEYOHChYrCmBKx\nsbGshiy0hDzsWALsFEeSk6rmfscSq0aS/eV79+4l9mf/N8TFxUEgEGDq1KlYsmQJKisrMWvWLMyd\nO5eK/qlTp5rMU3A4HFy5coWKfkvQaqkwMzPDo0ePmL5nBbKHojBuBhpfTmz2+LLNihUrpIrvV69e\nEbPpaQkfHx/Y2trCwMAAXl5esLGxQUJCArVjNVVVVairqyMlJQVz5syBsrKyzJwYyANqamqshiy0\nhCy0E9CAjeKo8UkVbWg53nyMn3/+GSdOnEBSUhL69++P1atXw8XFhVphzLZVZkvQ+uylpqbi6NGj\nUFdXh7KyMnUPfQUfR1EYNwOtDwhbPb4fg/SDQcNeag6HA3V1dRgYGBDVbMzr168xbtw4/PTTT3By\ncsLs2bOp+agCQJcuXbBgwQLU1NTAzMwM8fHx1I3m5ZmBAwc2CVl4//4928uSG5orjkh7uUpa5Bo/\nmEvYs2cPUX1ZgcvlQllZGcnJyfD09AQAKsN3gYGBCA4OZlpKGkOrlYRtZPXBQMF/UBTGMgiNHeuw\nsDBMnjwZAwYMaPIa6QLRwMAAx48fx8OHDxmrul69eqFdu3ZEdRvy/v17/PXXX4iPj0dUVBR4PB4V\nL1HJwKW5uTmqqqrQsWNHpKamok+fPti5cydxfQVifH19mbRBS0tLvHnzBiNGjGB7WXLTSnHv3j0c\nOnQIlZWVAMTDj69fvyYaDS7ZbGhuyPn169fEdGWNAQMGYPz48YyfeHR0NBUPf0kRListJWzBtoe+\ngo+jKIybge0vJxo71kZGRjh06BCKi4sxduxYTJkyhUlkIp28t2bNGlhYWGDZsmUQCATIycmBn58f\n1RvmypUrERkZCXd3d3Tq1AkRERFwdnYmrtvQIkzyc0FBAXJycrBu3ToMGzaM+BoUANXV1YiJiUF5\neTn8/f2RlZUFoVDI9rKIDn/JEps2bYK3tze2b9+OoKAgpKSkwNTUlKim5FTKzMwMmZmZUkX5wYMH\nYWNjQ1RfVli3bh08PT2ZmYpvvvmGShvFx4YtaQ4/NwetGRO2PfQVfBy5LoxbisXcunUryysjj52d\nHezs7CAQCJCVlYXvv/8eXC4Xc+bMwbRp04gW5zU1NVK70qamptQ9fHV1dREQEAAAKCkpoeaf2pLb\nSUVFBby8vBSFMSV8fX0xYsQI/PbbbwDEv/9Vq1YxX1YkYHv4S5Zo06YNhg0bBlVVVRgbG8PY2Biu\nrq5N0ghJsHLlSrRr1w45OTmwtrZmNWyDDVqKRybthjJx4kQAYkcYLpcLCwsLJnVWVVWVqHZYWFir\n32mrV6+mlj7Jtoe+go8jt4Vxa7GYOjo6rK6N1o717du3kZiYiJycHAwdOhTffvstrl+/jpUrVxLt\nt5NEAQ8cOBCA2LuV9m6dp6cnU6TU1dXh+fPnMDIyQkxMDNV1SOjUqZPcDF7JAjU1NZg3bx4uXrwI\nQDwM+/PPPxPVZHv4S5Zo27Ytrly5gh49emDnzp3o2bMnSktLqWhXVVUhPDwcTk5OCAgIAI/HQ2Bg\noNyEi0jikQGxbehff/1FJZJYYod4/PhxHD16lLlua2uLRYsWEdWWpRANtj30FXwcuS2M2Y7FZLPH\nFxA/vRsYGGDq1KlYs2YNlJXFbwVzc3PiN6n169dj8+bNKCgoACC+aQUGBhLVbMyZM2ekfv7nn39Y\nHb55/vy5ojCmiMQiUfI7z8jIIP5wphj++g/bt2/H69evsX79ehw7dgx///03kzwo6f0mhUAgQHFx\nMZSUlPD06VPo6Ojg6dOnxPRkjcY2YYaGhnB1daWmX1lZiatXr8LU1JRJn3v58iVRTT09PQwaNAjp\n6elEdT4Ftj30FXwcuS2M2Y7FZLPHFxBPAGtoaDA/19XVISgoCJs2bcLBgweJavfr1w9btmxhBj4K\nCgqaDTugiZaWFvLy8ojrNFcU8Xg8lJWVYfv27cT1FYhZv3491q9fj9zcXBgaGmLEiBHYuHEjUU3F\n8Nd/UFdXh7q6OoCm7UVubm5Ej/W9vLxw7949LF26FO7u7qiurqa6KcI2DRNXAfGmwKtXr6jph4WF\nISIiAjt37mTS50gXhjk5ORg0aBCSk5ObfX3MmDFE9RvStWtX+Pn54e3btxAKheBwOE2sCxWwC0fE\n9qQZS6SkpODHH39ESUkJjI2N8eTJE/j5+WH8+PFU1yHp8d27dy+1Hl9AXBhLnlpVVFQgEokwduxY\nKj6+W7duRUVFBUJDQwGIhxE6dOhANeCksWVQeXk5hg8fjpCQEKK6zYXHdOrUCX369GF27RWQ48aN\nG4iIiEB0dDTq6+uxYMEClJWVQSgUIiAgAF9//TXxNdTV1TU7/JWamkpc+3PAyclJMYhEkPDwcKmf\n1dXVMWHCBCrOFBKqq6vB4/EYD18AVPUlSKLgN23aRE3Tx8cHN2/eZCLpaScfKvg4cvtNPH78eIwc\nOZKJxezTpw8Tz0wLtnp8AXaTv27fvo2TJ08yP2/evBkODg5UtCU0dMCQeCm3b98egLjnedCgQUR0\nLSwsiPy5Cj6NXbt2MTvzly9fxrt375CcnIyqqiosX76cSmEs78NfH4P0psDOnTvx66+/NpnlkJeA\nhdbeazRikdetW4eMjAxm2J1mYdhwQ4iNKHgAKCwsRFpaGlVNBf8dclsYZ2ZmIjY2Fm/fvpW6QZKe\nzJXAZo8vIE5eYyv5SygUSkVi3r17l7pFXmuekTt27KD2PlBAFzU1NfTq1QuAuK94ypQp4HA46Nix\no1Q0MUnkffiLbdLT03H16lXFwFMz0IhFfvDgAdLT01mZqZCFKPhJkybh8uXLMDQ0lLrnsLFjrqB5\n5LYw3rx5M/z9/aGtrc2KPps9vgBgYmLCWvLX+vXrERQUxMRh9+3bF0FBQVS0PwU57S6SC/h8PoRC\nIT58+ID09HS4u7szr717947KGuR9+OtjkP78jRgxAvn5+RgwYAC4XC5Rrc8NGsWqgYEB3rx5w7QS\n0EQWouDv37+P6OhodO7cmbmmaKWQLeS2MO7duzdGjRrFmv6lS5ea7fGlQX5+Pt69e4e4uDi0bdsW\nHA4HI0eOpOYMYWRkhG3btsnU8F1DFO4Q/16mTJmCGTNmgM/nY/To0dDT0wOfz0dAQACGDBlCZQ3y\nPvz1Mfr27Uv0z+dyuXBwcGCSNiVH+fLSSsE2z58/x7hx49C7d28oKSlRbaWQhSj4wsJCxj9dgWwi\nd4WxZCJXW1sbXl5eMDc3lzrOoNXrytaRzo0bN7Bp0yYsWbIECxYsQE1NDXJzc3HgwAFoa2tj+PDh\nxNewbds2lJeXM8N3R44coT58p0A+cXBwwNixY/H27VsmCU1VVRVDhgzBzJkzqayh4WdMXgfu4uLi\nIBAIMHXqVCxZsgSVlZWYOXMm5s2bR/wBPSMjAzk5OdRnShSIkdz32aBxFHxlZSWV77yGTJw4ETdu\n3MDAgQOlao+2bdtSXYeClpG7wlgSyaulpQUtLS0qPVXNwVaP708//YQDBw4w1nAAYGxsjBEjRsDH\nx4fKTeLWrVusD9+1hqKV4t9Nc/3l3333HTV9eR/+AoCff/4ZJ06cQFJSEvr374/Vq1fDxcWFys75\niBEj8PLlS/Tp04e41ucGjVjkDh06NIljNzIyIq4LiOdZEhMTpWaL0tLSqPoInz59GrGxsVLXOBwO\nrly5Qm0NClpH7gpjyUTu6dOnm3wZNkzjIQ1bPb51dXVSRbGEXr16Ueu3k4Xhu9aws7NjewkK/sUo\nhr/E7QzKysq4dOkSc0+m5SOflpaGqKgoaGhoSB3l/9sfTGQlFpmNOHYJP/zwA9zd3dGlSxfiWi2R\nkpLCmraCT0PuCuNr164hMzMTycnJUgMvdXV1uHjxIpXUOTZ7fFu7MZLOq5fQePhOX18fI0eOpKI9\nbNgwJgq6MZIvx9mzZ1NZiwL5RDH8BQwYMADjx4+Hrq4uDA0NER0dTW0qv7XCJDU1lQli+bchK7HI\nbMSxS9DT02viYU+LwMBABAcHY9asWc2+rhi+kx3krjAeNGgQlJWV8fvvv0tFY3I4HCrHqWz3+Obm\n5jb7wRSJRHj27BlRbQlGRkbYuXMnLl68iMTERJSWllIrELKysqjoKFDQEorhL7GXraenJ3N0/803\n32Du3Lksr0ps1/lvLYxlJRaZjTh2CZMnT8a0adPQv39/qf5eGq0Unp6eyM/PR4cOHVBQUAAul4sB\nAwbAxcUFOjo6xPUVfDpyVxirq6vD0tISCQkJLf47JE3O2e7xvXDhAtE/vzUqKytx6dIlJCQkoLCw\nEBMmTMDbt29x+fJlamsIDw/H8uXLm41mBkA8WEWBAsXwF3DlyhWcO3eONR/5lpCllq7/NbISi9ww\njn3UqFHo378/NmzYQEV79+7d8PDwgJaWFhW9hjx69IjZFBswYACzKbZu3ToEBga26q2vgC5yVxh/\nCiQH8tju8WXzwzdq1Cj06tULa9aswejRo8HlcqmHGkh2gxwdHZu89vr1a6prUSCfKIa/xLHwQUFB\nrPZ6Nse/2apR4tndeHdUEotMC319fezevZvZNdbV1YW6ujo1bZqDtg1he1NMwaejKIybgeTNURZ6\nfNkiNDQUCQkJ8Pf3h5WVFWxsbKivQWLRZWZmhszMTFRWVgIQfzkcPHiQlTUpkC/kdfirIYaGhjAz\nM5PrAUS2YDsW+cCBAzh9+jS++uoriEQiFBQUYO7cuXB1dSWurampCQcHBxgbG0u1UtCwCmV7U0zB\np6MojCkjCz2+bDF58mRMnjwZVVVVSE5ORkREBJ48eYKwsDDMnDmTuLF/Q1auXIl27dohJycH1tbW\nyM7OZqbjFSggibwOfzVk9OjRsLa2Rp8+faQKFEUrBXnYjkW+fPkyLl68yGwEffjwgVphbGFhAQsL\ni2Zfk/gbk0KeN8U+NxSFMWXY7PGVFTp06AB7e3vY29ujrKwMCQkJWL16Nc6ePUttDVVVVQgPD4eT\nkxMCAgLA4/EQGBhIvbVDgYKG/JuHvxpy8OBBbNu2jZVez4MHD2LRokXNvkbDlYht2I5F1tHRaTJs\np6urS0V7+vTpLb7m5uZG9MFMnjfFPjfkrjAuKipCr1698OzZsxZ7/EianCsa7KXR1taGq6srld2C\nhggEAhQXF0NJSQlPnz6Fjo6OlH2fAgVsIA87loC4lcLCwgLKyvS/gsrLy3Ht2jUMHDgQKioqzPW2\nbdvC2tqa+npow3YsMp/Ph7W1NUxMTCASiXD//n307dsXXl5eANgbgCb92VNsin0+yF1hvGzZMmzb\ntg0BAQEIDQ1t8mHo27cvFZNzBezi5eWFe/fuYenSpXB3d0d1dTWV1C0FClrj3zz81ZD6+npMmjQJ\nBgYGUq0UNIqi9PR0Jopb4mkuT8ljbMciS4YAm6O4uJjaOhpD+rOn2BT7fOCI5GWL4v/n0KFDyMzM\nxIMHD5hBLAkcDof1HjcFChTIL87OznJxD7px44ZUQSyhpf5PBf87motFBuh4+X4MNt//8vLZU/Bx\n5G7H2N3dHe7u7jh//jymTp3K9nIUsMTOnTvx66+/NjkxkCdnAAWyh7zsU+zfvx8xMTGsaOfn5yM0\nNBQ1NTU4deoUjh07hqFDh2LAgAGsrIc2shCL3BJsvv/l5bOn4OPIXWEs4auvvoKTkxOKiopQX1+P\nfv36wd/fH/r6+mwvTQEF0tPTcfXqVYVdlALqyPvwFyA+Vl61alWTPl8HBwfi2hs3bkRQUBCCgoIA\niP3VAwICqMUSsw2bscgfg/SaHjx4ACMjo2Zfo+mKpEC2kdvCePPmzfDz84OxsTEA4Pbt2wgODlYc\npcgJI0aMQH5+PgYMGKDwkFRAFXkf/gLA+LlWV1dT11ZWVpbaAOnbt69c3QPYjEVmm9DQUBw5cqTZ\noc/AwEAWVqRAFpHbwlhJSYkpigHA1NRUJp+gFZCBy+XCwcEB7dq1AwC5DFlQwA7yPvwFAMuXL8fL\nly/x4sULDBkyhLiHbEM0NDTw66+/ora2Fnfu3EFKSgo6d+5MRVsWYDMW+WOQbmf44osvMGHCBBgY\nGEg9lLLlhKFANpHbwrh9+/aIjIxkhj2ysrKI2rQpkC0yMjKQk5ODNm3asL0UBXLGpUuX2F4C6xw7\ndgzJycmora3F+fPnsW3bNnTt2rVVx4L/FVu2bMHx48ehqamJn376CYMGDZKL3VIJbMYiA8C9e/cw\ncOBAqWtZWVkYNmwYhg0bRlR74cKFRP98Bf8O5M6VQkJ1dTWOHz+O3NxccDgcmJiYwMnJidlBVPDv\nZsuWLZg7d26LXtYKFJBC3oe/AMDR0RExMTFwcnJCdHQ0RCIR5syZg1OnTlHRz8vLQ3V1NYRCIXNS\nOHToUCrabOPn54eioiLqsciFhYV4+vQpdu7ciVWrVjHXBQIBQkJCkJaWRlQfEMcyJycno6ysDK6u\nrsjPz4eurq7U7rECBXK7Y6yuro5ly5Y1+9qyZcuwf/9+yitSQJO0tDRERUVBQ0MDSkpKilYKBdSQ\n9+EvQOxjDPxn2OrDhw+oq6ujou3h4QEejwdtbW3m6J7D4chNYcxWLPL79++Rm5uLiooKJCcnM9c5\nHA6WL19ORLMxAQEB6NSpE3JycuDq6oqcnBwcOHAAO3fupKKv4PNAbgvj1uDxeGwvQQFhUlJSWnwt\nNTVVLmJ5FbCDvA9/AeIBMGdnZxQWFiIwMBDZ2dnUIol5PB5iY2OpaMkibMUi9+/fH/3798eECRPQ\nu3dvqKmpobKyEqWlpTA0NCSi2ZjS0lJs2bIFTk5OAMQnFw2LdAUKAEVh3CyKITz5JioqSlEYKyCG\nvA9/AYC9vT3GjBmDu3fvQlVVFYsXL0bbtm2paJuZmeHRo0f46quvqOh9TtDorDx16hSMjY3x9ddf\nY/78+czg+4YNG4hrCwQC8Hg85ju+oKAAfD6fuK6Czwv52qZQoOATkNO2ewWU2LJlC169esUMf7Vv\n315uhr/q6urw7t07zJ8/H126dIGVlRVGjhwJDQ0NZhePNKmpqZgyZQqGDh3KDHzRjESWZWhsCuXl\n5WH69OlISEjAzJkzsXHjRjx//py4LgB4e3vDxcUF9+7dw6RJk7B8+XKsWbOGiraCzwfFjrECBY1Q\nnBgoIEm7du1gbW0NCwsLZvjrwYMHctHjmpGRgaNHj+Lu3buwtbVlrnO5XGp//8uXL1PRUdA8fD4f\nZWVliI+Px/79+1FXV0etfXHIkCE4d+4cysvLoaqqCg0NDSq6Cj4vFIVxMyhs2xQoUEAKeR7+sra2\nhrW1Nc6fP4+pU6c2+++Q7vF3dnZuck1JSQk9e/aEh4cHevToQUxb1qFxWubg4AB3d3dMnjwZ3bp1\nw65duzBx4kSimtbW1i1ueHA4HMZXXIECQI4L4+rqasTExKC8vBz+/v7IysqCkZER2rf//9q7+5gq\ny/8P4O8DyhE0RU+mhk8c0eFM0IPGZjJI+8PRDvkwoqWomLJWZMwVRcPUdANN09A5dXPyoKRDych8\nSLHBdEVDGEKDCQfTPGmteBTqHMY53z8Yt6FI7PfrPtftfb1ff51z339cH/Con3Pd13W9h2Pv3r2i\nyyOBuJSC1CT75i8Aj22KAfXX+IeFhcHpdCrNUklJCQBg6tSpSE1NRW5urmpja4HoWOSuri4UFhYq\n75OTk1V/SnfmzBm43W4cPHgQwcHBCA8Ph8vlwg8//IBbt26pOjY9eaRdY/zhhx9i+PDhqKqqAgA0\nNjb2OluR9O3gwYOPvZeQkODBSkg2PZu/qG9qfzEtKyvD+++/j7CwMFgsFiQnJytLO1wul6pja0FG\nRsZjj8bzRCzy1atXYbPZlPeeWLrm5+eHoUOHory8HNHR0TCZTBg9ejSsViuuXbum+vj0ZJF2xri9\nvR2vv/46zp07BwCIjo6W6hxR2f3555+4evUqZs6c2etwd19fXyxYsEBgZaR3ly5dwpEjRzBs2DAl\nYIFnaD+gdqPU2dmJ7OxsWCwWeHl5oaqqCk1NTaioqJDiaZHoWOTq6mpYrVb4+voq43vq8+/j44OM\njAzMnj1b+bPvOVObqIe0jbHL5cLt27eVf4RLSkqkmC2gbsXFxcq6MoPBoAR8FBUVCa6M9I6bv8T6\n/PPPkZWVhb1798LtdmPSpEnYs2cPOjs7sWvXLtHlqU50LLLIz39mZiYKCwvx448/AgACAwMZ5kWP\nkDYS2mazYevWrbh+/Tp8fX0RHByMjz76qNfB+0RE/zVu/upfT0z0f81utyMgIAD19fUAoHwZ7uGJ\n9bVaICoWed++fUhKSsL69ev7fCrgiRnrjo4OfP/992hra+t1ffHixaqPTU8OaWeMp0yZgqysLNFl\nkCA3btxARkYG2tvbceLECWRlZWHu3LmYMWOG6NJI52Tf/AV0b8Bqbm6GyWTCzZs3YbPZEBERAaPR\nqNoa/5ycHKSmpmLLli3KU6K7d+/CZDLBaDSqlvimNaJikWtrawF0p80BQHl5OSwWi6pjPiwhIQHj\nx4/HM888o1zj8Zz0MGkb4127dqGgoOCR5RNc5yeHrVu3YvPmzdi8eTMAYP78+di4cSPXmZPqysrK\nejW/FotHlQbwAAAMZElEQVQFa9asQXJyMvLy8gRW5jnvvfceXn75ZQQHB2P9+vWIjo7GmTNnsGfP\nHtXW+EdFRSmz0V1dXUhISIC3tzcaGxuRlpamyphaJCoWuees4ueffx5A9wzym2++qfq4/zR48GAp\nlsvQ/4+0jXFJSQkuX74Mo9EouhQSYNCgQb2WzQQFBcHLS9pDWsiDZN/8BQB//PEHXnrpJRw6dAjx\n8fF49dVXVV/7unv3buzcuRNA9zrXjo4OnD9/Hi0tLUhKSkJkZKSq42uFqFjkhz/bIj7rL774IoqL\nixEWFqZsfAXgsThyejJI2xjPmzcPN27cwIwZM9gQSeipp57CyZMn8ddff6GyshIXL16EyWQSXRZJ\nQPbNXwDw999/49q1aygsLEROTg5aW1vR3Nys6phGoxETJ04E0D0xEhMTA4PBAH9//15Nkt71xCL/\n/PPPWLRoEQwGA7Zt26b6uA8vWRCxhOHEiROPHFXHTdf0MGk333366afIzc3F0KFDATzYiMGlFHJo\nb29HdnY2Kioq4OPjg9DQUCxfvlz5PBD917j564ErV67g2LFjWLRoEV555RXs378fzz77rKqboOLi\n4vDFF1/A4XBg4cKFOHr0KMxmMwAgNjYW+fn5qo2tRZ6ORbZYLMrv2+124+bNmzCbzcrfg5MnT3qk\nDqJ/I21jbLVakZ+fjyFDhoguhQSpra3F/fv34XK5lAZFhlheEiM9PR2pqamIj4+XevNXXzo7O7Fl\nyxZVZy6PHTuG/Px8OJ1OzJw5E9u3b4fT6VQ2o33wwQeqja0FomOR7XZ7v/cDAgJUHR/gpmsaGKmX\nUty7dw+TJ08WXQoJkJiYiNbWVowZM0ZZ62YwGNgYk2q4+euB/Px8ZGZmoqmpCT4+PnC5XIiKilJ1\nzOXLlyMqKgptbW0IDg4G0B34MGfOHCxbtkzVsbVAdCyyJxrff8NN1zQQ0jbGly9fRk5ODtOnJNXa\n2orjx4+LLoMkws1fD5w4cQKXLl3C2rVrkZubi6KiIty5c0f1cftqzmJjY1UfVwv8/PwAdB+TtmHD\nBuW61WpV7Yg8reGmaxoIaRvjixcvii6BBLJYLKirq8PUqVNFl0KS4OavB3x8fGA0GtHZ2QmXy4WF\nCxciPj4eq1atEl2a7skci8xN1zQQ0jXGWkjfIfEuXbqEI0eO8IkBeYzT6YTL5YLD4UBxcTHWrVun\n3Ovo6BBYmeeFhITg6NGjmD9/PlatWoWxY8fC4XCILksKMscip6enIzs7GyNHjsShQ4cQGhqK9PR0\n0WWRxkjXGJeVlQF4kL5Dcvr2229Fl0CSiYmJwdKlS+F0OhEREQGz2axs/pozZ47o8jzi2LFjAIAJ\nEyYAAEaOHInw8HAA3c0yqc/LywtjxoxRllYAQFFRkRSxyG63G4sWLcJbb72F0tJS1NTUwOFw8DQi\n6kW6xrgn6a4nfYfktHLlykeueXt7Y8KECUhMTMT48eMFVEV6JvvmLwBoampSXn/55ZdYsmSJ8r6l\npUVESdKRORY5OTkZ69atQ1dXF3bs2IFVq1YhNTUVBw8eFF0aaYh0jfHt27exY8eOx95PSUnxYDUk\nSlhYGJxOp3KEUUlJCQBg6tSpSE1N7RXZS/RfkXnzFwAkJSUpr0tLS3u9J8+QORbZ6XQiPDwcmZmZ\nWL16NaxWKwoKCkSXRRojXWPs6+vLDVeEsrKyXs2vxWLBmjVrkJycjLy8PIGVEclBlllKrZE5Ftnp\ndKKwsBDffPMNTp06hTt37qCtrU10WaQx0jXGTz/9dK/HdySnzs5OZGdnw2KxKDuzm5qaUFFRAUkz\nb4hIAjLHIm/atAmnTp3C5s2bMWzYMHz11VdITk4WXRZpjHTJd9u3b9d9whH9u99++w1ZWVmw2Wxw\nu92YNGkS4uPj0dnZiaFDh2LcuHGiSyTSnWXLlimpfz2RwAAYC0zCvf3229KczkH9k64xJrnZ7XYE\nBASgvr4ewIP/kHsEBQWJKo1I97QQCyw7xiL3rSeVkki6pRQkt5ycHKSmpmLLli3KzNXdu3dhMplg\nNBqRk5MjukQi3WLjKx5jkfvGNe/Ug1mIJJWoqChlZuDIkSMwGAzw9vZGY2Mj3njjDdHlERGpirHI\nRP3jjDFJZffu3di5cyeA7pCPjo4OnD9/Hi0tLUhKSkJkZKTgComI1MNYZKL+8WsiScVoNGLixIkA\ngJKSEsTExMBgMMDf37/X0UVERHqUnp6O33//XYlFHj58OGORAYwYMUJ0CaQRnDEmqTidTrhcLjgc\nDhQXF2PdunXKvY6ODoGVERGpT8ZY5O3bt/e7hjglJQV79+71YEWkZWyMSSoxMTFYunQpnE4nIiIi\nYDab4XQ6sXHjRsyZM0d0eUREqpIxFnnatGmPvffwmc5EPK6NpGO329HW1obg4GDlWn5+PpYtW8ZN\nKESkaytXrkROTg4yMzMRGBgIq9WK1atXIysrS3RpHlFXV4fm5mYA3U8QMzIy8PXXXwuuirSEM8Yk\nnb6OjIqNjRVQCRGRZ8kci/zxxx+joaEBDQ0NCAkJQXV1NdauXSu6LNIYTo8RERFJYtOmTbh+/boS\ni1xcXCxNLHJ9fT2OHj2KKVOm4MCBA8jPz4fNZhNdFmkMZ4yJiIgkMX36dKSlpSnvly9frrzWeyxy\nV1cX7t+/DwBobGzEuHHjUFtbK7gq0ho2xkRERITW1lbRJahqxYoVOHfuHFasWAGr1YpBgwZh3rx5\nossijWFjTERERLqPRbZarcrrBQsWoL29Hf7+/gIrIi1iY0xERES6d+rUKeTm5uL+/fv454FcRUVF\nAqsirWFjTERERLp3+PBh7Nu3D2PHjhVdCmkYG2MiIiLSfSzy5MmTYTabRZdBGseADyIiIp0bSCyy\n3qWlpaGurg6zZs2Ct7e3cl2Gn50GjjPGREREOsdYZCAsLAxhYWGiyyCNY2NMRESkc0uWLFFe9xWL\nrOf0z8rKSoSGhmLUqFGiS6EnABtjIiIiScgYi1xaWorQ0FCcP3++z/uRkZEeroi0jI0xERGRJOrr\n65GXl4f4+HgcOHAAd+/exf79+0WXparExEQAQFJS0iPrrL29veFyueDl5SWiNNIgNsZERESSkDkW\necOGDfjpp58QEBAAAPj1118RFBSE5uZmvPvuu1i8eLHgCkkL+BWJiIhIEg/HIkdGRiIoKEh0WR4R\nGBiIgoICXLhwARcuXMDp06cREhKCs2fPIi8vT3R5pBGcMSYiIpKEzLHI9fX1vU7nmDJlCmpqauDr\n64uuri6BlZGWsDEmIiKShMyxyLNmzcLSpUsxa9YseHl5obq6GmazGadPn8bs2bNFl0cawYAPIiIi\nSURHR/cZi+zn5yeoIs+6ceMGbDYb3G43Jk6ciOeeew5OpxM+Pj6iSyON4IwxERGRJGSMRT5+/Dhe\ne+21R9L/qqurcfbsWSbfUS9sjImIiCQxatQoxMXFSRWL3HMKRX/pf0Q92BgTERFJQsZY5IiICADA\nd999h8zMTMHVkNaxMSYiItI5xiID/v7++OyzzxASEoLBgwcr15l8R//ExpiIiEjnGIsM3Lt3D3a7\nHTU1NRg8eDBGjBgBQI6fnQaOp1IQERFJwm639xmLPHr0aN3GIjscDmzcuBGVlZWYNm0a3G436urq\nMHfuXKSlpWHIkCGiSyQNYWNMREQkibi4OOlikbdt24aAgAAkJCT0up6VlYWGhgZ88skngiojLdLn\n10MiIiJ6hIyxyOXl5Y80xQCwevVqVFdXC6iItIyNMRERkSRkjEX+57F0D9Pr8hH6v+PmOyIiIknI\nGItsMplQWlqK8PDwXtdLSkowZswYQVWRVnGNMRERkURki0X+5Zdf8M477yAwMBDTp0+Hy+VCVVUV\n7HY7Dh8+DJPJJLpE0hA2xkRERDr3uFjkHnpOvgMAt9uNK1euoKGhAQaDAWazGS+88EKfvwuSG5dS\nEBER6ZzsscgGgwERERFKCh7R43DGmIiISBLr169nLDJRPzhjTEREJAnGIhP1j40xERGRJBiLTNQ/\nHuBHRESkcw6HAykpKbh16xb8/PxgNBphs9ng7e2NTZs2iS6PSDO4xpiIiEjnGItMNDCcMSYiItI5\nxiITDQwbYyIiIp1jLDLRwPBvAxERkc71xCI/jLHIRL1xjTEREZHOMRaZaGDYGBMREUmAschE/46N\nMRERERERuMaYiIiIiAgAG2MiIiIiIgBsjImIiIiIALAxJiIiIiICwMaYiIiIiAgA8D+3aZC0b1un\nIwAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f84739fef28>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "kpc1RodOQjoz",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "We clearly see three fields that has to have some relationship with the target.: Day_of_the_week, Time_of_the_day_seconds and Day_of_the_month let's explore those relations. First we have a look if there are no outliers\n"
      ]
    },
    {
      "metadata": {
        "id": "DksY-MsWQ4dt",
        "colab_type": "code",
        "outputId": "5ba4770b-0eca-45f3-971c-211731d6e0ab",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 361
        }
      },
      "cell_type": "code",
      "source": [
        "plt.subplot(1,2,1)\n",
        "plt.scatter(train_df['Day_of_the_week'], train_df['Target_sign'])\n",
        "plt.ylabel('Feature Y')\n",
        "plt.xlabel('Feature X1')\n",
        "\n",
        "plt.subplot(1,2,2)\n",
        "plt.scatter(train_df['Time_of_the_day_seconds'], train_df['Target_sign'])\n",
        "plt.ylabel('Feature Y')\n",
        "plt.xlabel('Feature X2')\n",
        "\n",
        "plt.show()\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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vfxuJ2gCEGf0MxIdm97y7dOmihIQElZSU6LHHHlNDQ4MSExMjURuAMKOfgfjQ7J7322+/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\nMoQ3AACWIbwBALDM/wOXuIrkxdZzjgAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f68c3f64be0>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "krDY1KRfRUEQ",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "Let's see relationships between sign a document and time, day etc... For better human understanding we transform the time in seconds to hours. As those probably are laboral documents there exist relationships between working hours (Wednesdays at 11h) and documents signed. But this is not clearly having relation with the signed docs it's produced but the volume of docs that are happening at that times."
      ]
    },
    {
      "metadata": {
        "id": "oqJAj0rfRaEy",
        "colab_type": "code",
        "outputId": "9e0282c3-037a-4d37-be96-d9a6cfd02b8c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 356
        }
      },
      "cell_type": "code",
      "source": [
        "train_df[\"TimeReviewed\"] = pd.to_datetime(train_df[\"Time_of_the_day_seconds\"], unit='s').apply(lambda x: x.hour)\n",
        "\n",
        "plt.figure(figsize=(14,5))\n",
        "\n",
        "plt.subplot(1,2,1)\n",
        "plt.hist(train_df['Day_of_the_week'][train_df['Target_sign'] == 0], bins=7, alpha = 0.7, label = 'Not signed')\n",
        "plt.hist(train_df['Day_of_the_week'][train_df['Target_sign'] == 1], bins=7, alpha = 0.7, label = 'Signed')\n",
        "plt.ylabel('Distribution')\n",
        "plt.xlabel('Day of the week')\n",
        "plt.legend()\n",
        "\n",
        "plt.subplot(1,2,2)\n",
        "\n",
        "plt.hist(train_df['TimeReviewed'][train_df['Target_sign'] == 0], bins=12, alpha = 0.7, label = 'Not signed')\n",
        "plt.hist(train_df['TimeReviewed'][train_df['Target_sign'] == 1], bins=12, alpha = 0.7, label = 'Signed')\n",
        "plt.ylabel('Distribution')\n",
        "plt.xlabel('Hour of the day')\n",
        "plt.legend()\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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6+eWXVVZWVqfNYDDoiy++8EhQAABcCfIUAMBTmlQw7d2719NxAABw1chTAABP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\nQmCC\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f43ba948710>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "mfplJ5MW9fsx",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "Let's explore the day of the moth (28 days and not 30). We se that it have some direct relationship with the signed documents. Specially at the end of the month. So, from the data we can have some chance of obtaining a prediction (or classification of documents)"
      ]
    },
    {
      "metadata": {
        "id": "OwsVbkR-SCEp",
        "colab_type": "code",
        "outputId": "df35c924-6b9a-47aa-f06f-0c027677b0a5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 352
        }
      },
      "cell_type": "code",
      "source": [
        "print('Number of days of the month ',train_df['Day_of_the_month'].nunique())\n",
        "train_df[\"TimeReviewed\"] = pd.to_datetime(train_df[\"Time_of_the_day_seconds\"], unit='s').apply(lambda x: x.hour)\n",
        "test_df[\"TimeReviewed\"] = pd.to_datetime(test_df[\"Time_of_the_day_seconds\"], unit='s').apply(lambda x: x.hour)\n",
        "\n",
        "plt.figure(figsize=(14,5))\n",
        "\n",
        "plt.subplot(1,2,1)\n",
        "plt.hist(train_df['Day_of_the_month'][train_df['Target_sign'] == 0], bins=30, alpha = 0.7, label = 'Not signed')\n",
        "plt.hist(train_df['Day_of_the_month'][train_df['Target_sign'] == 1], bins=30, alpha = 0.7, label = 'Signed')\n",
        "plt.ylabel('Distribution')\n",
        "plt.xlabel('Day of the month')\n",
        "plt.legend()\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Number of days of the month  28\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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MYMeOHQQFBbF48WJ27tzJ4sWLWbp0qZnhei13x5mb2t4aZvDmvUhEPM3SIbWMjAz69+8P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\ngg==\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f43b9f6d9e8>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "_Qqw1N-OSzly",
        "colab_type": "code",
        "outputId": "58395c09-38f5-4796-c5cc-a12d5ad42b35",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 365
        }
      },
      "cell_type": "code",
      "source": [
        "plt.scatter(train_df['Day_of_the_month'], train_df['TimeReviewed'], c = train_df['Target_sign'], cmap = 'autumn')\n",
        "plt.ylabel('Hour of the day')\n",
        "plt.xlabel('Day of the month')\n",
        "plt.colorbar()\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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5v/uTN87GuFOHCxWMf+iw2XtjNgC9z5CK6xiGjpHUofO4unYkyzbPSOjEX69v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/xZELoZjPf66mbMhdAtUHO3UuYfCcre72sLcu1hNkFsz8Ghg+r/nMZc5v5+b75roF4T8Gk15xB2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\nSCT1QBZOiUQikUjqgSycEolEIpHUg/8FNulUqM4DQCkAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f43bd6c9a20>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "7ealAr1D-dTc",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "There are some more correlations like Account_age_days so we can try to build a model."
      ]
    },
    {
      "metadata": {
        "id": "DUO9U6baSy4o",
        "colab_type": "code",
        "outputId": "77db9673-6c15-4cf0-ae16-b130bfef5a43",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 334
        }
      },
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(14,5))\n",
        "plt.subplot(1,2,1)\n",
        "plt.hist(train_df['Account_age_days'][train_df['Target_sign'] == 0], bins=30, alpha = 0.7, label = 'Not signed')\n",
        "plt.hist(train_df['Account_age_days'][train_df['Target_sign'] == 1], bins=30, alpha = 0.7, label = 'Signed')\n",
        "plt.ylabel('Distribution')\n",
        "plt.xlabel('Account_age_days')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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wpk+fTu3atfHy8iImJgZPT0/Cw8MZP348FouFyZMn4+3tTXBwMHv27CE0NBSbzcbcuXMB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\nIqamoBIREVNTUImIiKkpqERExNT+H1vHdT+09J00AAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f43ba0c71d0>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "ExXKTaKiT4Hl",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Data modelling\n",
        "Lets move to build the regression model studying different regression/classification models. We will explore the possibilities of different models using K-Fold Cross Validation and modify the hyperparameters of the model as sometimes the sklearn default parameters (specially in random forest) do not prevent overfitting and the model do not learn from the data.\n",
        "\n"
      ]
    },
    {
      "metadata": {
        "id": "TaJ0rsKjUc6D",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "#x_train, x_test, y_train, y_test = train_test_split(X_train, Y_train, test_size=0.20)\n",
        "\n",
        "# Algorithms\n",
        "from sklearn import linear_model\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.linear_model import Perceptron\n",
        "from sklearn.linear_model import SGDClassifier\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn.svm import SVC, LinearSVC\n",
        "from sklearn.naive_bayes import GaussianNB"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "jD7piUqakmFC",
        "colab_type": "code",
        "outputId": "87f40ef3-b401-450c-81fc-3b048d6998bb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        }
      },
      "cell_type": "code",
      "source": [
        "X_train = train_df\n",
        "X_train = X_train.drop(\"Target_sign\", axis=1) \n",
        "Y_train = train_df['Target_sign']\n",
        "X_test = test_df\n",
        "\n",
        "print(X_train.shape)\n",
        "print(Y_train.shape)\n",
        "print(X_test.shape)\n",
        "\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(132637, 34)\n",
            "(132637,)\n",
            "(10000, 34)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "iwAfVq6c_sM3",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Estimation of performance\n",
        "We use K-folds for a first approximation (a baseline)to the results that can acchieve some very well known regressor/classification models."
      ]
    },
    {
      "metadata": {
        "id": "oN8Nj0mVgA9P",
        "colab_type": "code",
        "outputId": "eeacbfb4-009e-4c28-b335-3be218c82b38",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 85
        }
      },
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import cross_val_score\n",
        "rf = RandomForestClassifier(criterion = \"gini\", \n",
        "                                       min_samples_leaf = 1, \n",
        "                                       min_samples_split = 10,   \n",
        "                                       n_estimators=100, \n",
        "                                       max_features='auto', \n",
        "                                       oob_score=True, \n",
        "                                       random_state=1, \n",
        "                                       n_jobs=-1)\n",
        "scores = cross_val_score(rf, X_train, Y_train, cv=10, scoring = \"accuracy\")\n",
        "print(\"Scores:\", scores)\n",
        "print(\"Mean:\", scores.mean())\n",
        "print(\"Standard Deviation:\", scores.std())"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Scores: [0.75708685 0.75512666 0.75527744 0.75316647 0.75738842 0.75610676\n",
            " 0.75972557 0.75548518 0.75277087 0.75759632]\n",
            "Mean: 0.755973053772401\n",
            "Standard Deviation: 0.0019886501914290963\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "9CBpTBEsBuLH",
        "colab_type": "code",
        "outputId": "c5fe9e51-791e-46b6-ca78-5f72627ec716",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 85
        }
      },
      "cell_type": "code",
      "source": [
        "decision_tree = DecisionTreeClassifier() \n",
        "scores = cross_val_score(decision_tree , X_train, Y_train, cv=10, scoring = \"accuracy\")\n",
        "print(\"Scores:\", scores)\n",
        "print(\"Mean:\", scores.mean())\n",
        "print(\"Standard Deviation:\", scores.std())"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Scores: [0.69202352 0.69805489 0.68636912 0.69194813 0.69632087 0.6912696\n",
            " 0.69624548 0.69720274 0.69147252 0.68996456]\n",
            "Mean: 0.6930871429419865\n",
            "Standard Deviation: 0.003539352303741588\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "2MBl1L38B2P2",
        "colab_type": "code",
        "outputId": "53ed5c88-17a0-4053-b1c2-7f7dff4e1655",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 85
        }
      },
      "cell_type": "code",
      "source": [
        "knn = KNeighborsClassifier(n_neighbors = 3) \n",
        "scores = cross_val_score(knn , X_train, Y_train, cv=10, scoring = \"accuracy\")\n",
        "print(\"Scores:\", scores)\n",
        "print(\"Mean:\", scores.mean())\n",
        "print(\"Standard Deviation:\", scores.std())"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Scores: [0.66917973 0.66902895 0.66842581 0.6675965  0.67068758 0.67061218\n",
            " 0.67596502 0.67134133 0.67096434 0.66968258]\n",
            "Mean: 0.6703484016001052\n",
            "Standard Deviation: 0.002188963845232583\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "rN8OCBRMCCgv",
        "colab_type": "code",
        "outputId": "a96923a8-cce5-4d64-9191-843ba0ac0199",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 85
        }
      },
      "cell_type": "code",
      "source": [
        " logreg = LogisticRegression(random_state=0, solver='lbfgs',\n",
        "...                          multi_class='multinomial')\n",
        "scores = cross_val_score(logreg , X_train, Y_train, cv=10, scoring = \"accuracy\")\n",
        "print(\"Scores:\", scores)\n",
        "print(\"Mean:\", scores.mean())\n",
        "print(\"Standard Deviation:\", scores.std())"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Scores: [0.67935766 0.67935766 0.6846351  0.6799608  0.67913148 0.68222256\n",
            " 0.6789807  0.68114303 0.67993667 0.68091684]\n",
            "Mean: 0.6805642490691474\n",
            "Standard Deviation: 0.001673658126072596\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "OiYPqhgaCTpe",
        "colab_type": "code",
        "outputId": "db637b88-c8f6-4eeb-d94a-a598495ca694",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 85
        }
      },
      "cell_type": "code",
      "source": [
        "gaussian = GaussianNB() \n",
        "scores = cross_val_score(gaussian , X_train, Y_train, cv=10, scoring = \"accuracy\")\n",
        "print(\"Scores:\", scores)\n",
        "print(\"Mean:\", scores.mean())\n",
        "print(\"Standard Deviation:\", scores.std())"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Scores: [0.68124246 0.67596502 0.68063932 0.67792521 0.67890531 0.68124246\n",
            " 0.67475875 0.68106763 0.67903189 0.67654377]\n",
            "Mean: 0.6787321821719946\n",
            "Standard Deviation: 0.0022516675311903657\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "i_qv1Pw2E_wl",
        "colab_type": "code",
        "outputId": "67d99405-9d1f-42f5-e71a-af58ef066f17",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 85
        }
      },
      "cell_type": "code",
      "source": [
        "linear_svc = LinearSVC()\n",
        "scores = cross_val_score(linear_svc , X_train, Y_train, cv=10, scoring = \"accuracy\")\n",
        "print(\"Scores:\", scores)\n",
        "print(\"Mean:\", scores.mean())\n",
        "print(\"Standard Deviation:\", scores.std())"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Scores: [0.65606152 0.62447226 0.6713661  0.67212002 0.6619421  0.67076297\n",
            " 0.3910585  0.35934555 0.6721707  0.662369  ]\n",
            "Mean: 0.6041668718730749\n",
            "Standard Deviation: 0.11548870623704371\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "F4xp-oYnFTT1",
        "colab_type": "code",
        "outputId": "43722ab2-f6b1-4a4f-bccb-919252d83090",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 85
        }
      },
      "cell_type": "code",
      "source": [
        "decision_tree = DecisionTreeClassifier() \n",
        "scores = cross_val_score(decision_tree , X_train, Y_train, cv=10, scoring = \"accuracy\")\n",
        "print(\"Scores:\", scores)\n",
        "print(\"Mean:\", scores.mean())\n",
        "print(\"Standard Deviation:\", scores.std())"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Scores: [0.69066647 0.69986429 0.68538902 0.69074186 0.6941345  0.69209891\n",
            " 0.69737636 0.69690115 0.69222649 0.69094473]\n",
            "Mean: 0.6930343793153032\n",
            "Standard Deviation: 0.003960697595211536\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "r7Lc1ou1FaNT",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "#Model exploration\n",
        "We will work with the Random forest classifier to see if we can get better results (accuracy). Let's test to retrain the random Forest with the most important features (selected with a threshold of 0.009). \n",
        "Firstly we will fit a RF model with the parameters used above. The exploration of parameters that sklearn offers seems to need more computation availability has it never ends. I can provide more machines in gcloud.\n",
        "\n",
        "param_grid = { \"criterion\" : [\"gini\", \"entropy\"], \"min_samples_leaf\" : [1, 5, 10, 25, 50, 70], \"min_samples_split\" : [2, 4, 10, 12, 16, 18, 25, 35], \"n_estimators\": [100, 400, 700, 1000, 1500]}\n",
        "from sklearn.model_selection import GridSearchCV, cross_val_score\n",
        "rf = RandomForestClassifier(n_estimators=100, max_features='auto', oob_score=True, random_state=1, n_jobs=-1)\n",
        "clf = GridSearchCV(estimator=rf, param_grid=param_grid, n_jobs=-1)\n",
        "clf.fit(X_train, Y_train)\n",
        "clf.bestparams"
      ]
    },
    {
      "metadata": {
        "id": "gCosfrPoIi_7",
        "colab_type": "code",
        "outputId": "a551b2ac-2555-4d2e-dea9-dfaba63366df",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "cell_type": "code",
      "source": [
        "\n",
        "random_forest = RandomForestClassifier(criterion = \"gini\", \n",
        "                                       min_samples_leaf = 1, \n",
        "                                       min_samples_split = 10,   \n",
        "                                       n_estimators=100, \n",
        "                                       max_features='auto', \n",
        "                                       oob_score=True, \n",
        "                                       random_state=1, \n",
        "                                       n_jobs=-1)\n",
        "random_forest.fit(X_train, Y_train)\n",
        "Y_prediction = random_forest.predict(X_test)\n",
        "random_forest.score(X_train, Y_train)\n",
        "acc_random_forest = round(random_forest.score(X_train, Y_train) * 100, 2)\n",
        "print (acc_random_forest)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "89.14\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "68hLEZDvJoru",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "Let's retrain the RandomForest with the best features. Important Features is a function of Sklearn framework. We will use a threshold of 0.009, obtaining a 16 important features.\n",
        "We retry RF model with this features."
      ]
    },
    {
      "metadata": {
        "id": "HpS4n-UjC66h",
        "colab_type": "code",
        "outputId": "b950886b-c466-458f-ea6d-0399e7a73f24",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        }
      },
      "cell_type": "code",
      "source": [
        "importances = pd.DataFrame({'feature':X_train.columns,'importance':np.round(random_forest.feature_importances_,3)})\n",
        "importances = importances.sort_values('importance',ascending=False).set_index('feature')\n",
        "\n",
        "#importances.head()\n",
        "\n",
        "important_features=importances[importances.importance > 0.009]\n",
        "important_features.head(35)\n",
        "important_feat = important_features.index.values.tolist()\n",
        "train_important_df = X_train[important_feat]\n",
        "test_important_df = X_test[important_feat]\n",
        "\n",
        "print(test_important_df.shape)\n",
        "print(Y_train.shape)\n",
        "\n",
        "\n",
        "random_forest = RandomForestClassifier(criterion = \"gini\", \n",
        "                                       min_samples_leaf = 1, \n",
        "                                       min_samples_split = 10,   \n",
        "                                       n_estimators=100, \n",
        "                                       max_features='auto', \n",
        "                                       oob_score=True, \n",
        "                                       random_state=1, \n",
        "                                       n_jobs=-1)\n",
        "random_forest.fit(train_important_df, Y_train)\n",
        "Y_prediction = random_forest.predict(test_important_df)\n",
        "random_forest.score(train_important_df, Y_train)\n",
        "acc_random_forest = round(random_forest.score(train_important_df, Y_train) * 100, 2)\n",
        "print(acc_random_forest)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(10000, 15)\n",
            "(132637,)\n",
            "89.7\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "Us6RuLJ5KHS_",
        "colab_type": "code",
        "outputId": "3af95370-d789-49db-c837-4eabe5948f19",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 538
        }
      },
      "cell_type": "code",
      "source": [
        "importances.plot.bar()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f43b3cf7390>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 33
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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EkzYREZFMMGkTERHJBJM2ERGRTDBpExERyQSTNhERkUwwaRMREckEkzYREZFM6P1oTjmZ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\naSe+XaOv26YAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f43b3cf7e48>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "EbNy0DUIKljR",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "[link text](https://)The final model obtained 89.96 without the using of the text of the documents. Let's have a quick look if we are overfitting the model"
      ]
    },
    {
      "metadata": {
        "id": "RkD4Dk-HcYvv",
        "colab_type": "code",
        "outputId": "ed84b642-657d-4318-9b52-2383a5a45547",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 170
        }
      },
      "cell_type": "code",
      "source": [
        "print(acc_random_forest)\n",
        "from sklearn.model_selection import cross_val_predict\n",
        "from sklearn.metrics import confusion_matrix\n",
        "from sklearn.metrics import precision_score, recall_score\n",
        "from sklearn.metrics import f1_score\n",
        "\n",
        "predictions = cross_val_predict(random_forest, train_important_df, Y_train, cv=3)\n",
        "print('####### Confusion Matrix #########')\n",
        "print(confusion_matrix(Y_train, predictions))\n",
        "print('####### Precission / Recall #########')\n",
        "print(\"Precision:\", precision_score(Y_train, predictions))\n",
        "print(\"Recall:\",recall_score(Y_train, predictions))\n",
        "print('####### F1 Score #########')\n",
        "f1_score(Y_train, predictions)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "89.7\n",
            "####### Confusion Matrix #########\n",
            "[[21049 22438]\n",
            " [10613 78537]]\n",
            "####### Precission / Recall #########\n",
            "Precision: 0.7777865808368408\n",
            "Recall: 0.8809534492428491\n",
            "####### F1 Score #########\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8261617357001972"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "metadata": {
        "id": "xRl7BSInMvyW",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "\"predicting that a document will be signed when in reality it won’t is slightly worse than otherwise\" I understand that as we want to minimize he False Positives. In this case FP is quite big 22246 where wrongly classified as Signed. I am not sure if this model can be used for prediction it depends on the trade-off that has to be considered.\n",
        "\n",
        "79161 where correctly predicted as signed. 9989 where wrongly predicted as signed when there were not. \n",
        "\n",
        "How model has good abilities to predict the documents that are signed (it fits well to the trained dataset) but need to increase the precision as it is a requirement of the customer to not badly predict documents as signed.\n",
        "\n",
        "# precision/recall tradeoff\n",
        "\n",
        "After printing the curves I see an interesting point in stopping at threhold of 0,7 to try to imporve the precision. "
      ]
    },
    {
      "metadata": {
        "id": "t4CvSjV_y3u1",
        "colab_type": "code",
        "outputId": "a97b2b71-c2d8-4ddc-8daf-6ab4c51bf60e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        }
      },
      "cell_type": "code",
      "source": [
        "print(X_train.shape)\n",
        "print(Y_train.shape)\n",
        "y_scores = random_forest.predict_proba(train_important_df)\n",
        "\n",
        "y_scores = y_scores[:,1]\n",
        "\n",
        "print(y_scores)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(132637, 34)\n",
            "(132637,)\n",
            "[0.11347167 0.99785714 0.07760459 ... 0.75490345 0.23634797 0.12137338]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "8ckg-nLJQqbh",
        "colab_type": "code",
        "outputId": "36943d14-2849-47ac-a466-061337f7fbae",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 473
        }
      },
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import precision_recall_curve\n",
        "\n",
        "# getting the probabilities of our predictions\n",
        "y_scores = random_forest.predict_proba(train_important_df)\n",
        "y_scores = y_scores[:,1]\n",
        "\n",
        "precision, recall, threshold = precision_recall_curve(Y_train, y_scores)\n",
        "def plot_precision_and_recall(precision, recall, threshold):\n",
        "    plt.plot(threshold, precision[:-1], \"r-\", label=\"precision\", linewidth=5)\n",
        "    plt.plot(threshold, recall[:-1], \"b\", label=\"recall\", linewidth=5)\n",
        "    plt.xlabel(\"threshold\", fontsize=19)\n",
        "    plt.legend(loc=\"upper right\", fontsize=19)\n",
        "    plt.ylim([0, 1])\n",
        "\n",
        "plt.figure(figsize=(14, 7))\n",
        "plot_precision_and_recall(precision, recall, threshold)\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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z3gPJk7yp/THAUKl4U+Ml38P2Z5CSjsO8YhnMK1fAtGUjBIej1GWoEfXgGDwE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\nTkSuQmCC\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f68bd849f98>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "Kg2MtzUMotl6",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "RDPt8pz2miYq",
        "colab_type": "code",
        "outputId": "580102ae-8dd3-4e74-f134-b169133286d7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 71
        }
      },
      "cell_type": "code",
      "source": [
        "testSign = test_df\n",
        "#I remove columns and just stay withthe important features\n",
        "testSign = testSign[important_feat]\n",
        "print(testSign.shape)\n",
        "\n",
        "predictionsSign = random_forest.predict(testSign)\n",
        "\n",
        "\n",
        "#play with the threshold to asjust the model to the tradeoff of the requirements\n",
        "threshold = 0.8 # less FP\n",
        "threshold = 0.5# more accuracy\n",
        "threshold = 0.67# more accuracy\n",
        "predicted_proba = random_forest.predict_proba(testSign)\n",
        "predictionsSign = (predicted_proba [:,1] >= threshold).astype('int')\n",
        "\n",
        "import requests\n",
        "\n",
        "import requests\n",
        "address = ''\n",
        "data = f\"{{\\\"preds\\\": {list(predictionsSign)}}}\"\n",
        "r = requests.post(address, data=data)\n",
        "print(r.text)\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(10000, 15)\n",
            "{\"TP\":4776,\"TN\":2264,\"FP\":1036,\"FN\":1924,\"precision\":0.8217481073640743,\"recall\":0.7128358208955224,\"f1_score\":0.7634271099744245,\"accuracy\":0.704}\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}