Dev.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "L7qN1YoGRisK"
      },
      "source": [
        "## PREAMBLE"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hUgV7aw-Zywk",
        "outputId": "ad8518b7-cef9-40e7-a58c-d9b053237ad9"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Collecting xgboost==1.5.1\n",
            "  Downloading xgboost-1.5.1-py3-none-manylinux2014_x86_64.whl (173.5 MB)\n",
            "\u001b[K     |████████████████████████████████| 173.5 MB 9.3 kB/s \n",
            "\u001b[?25hRequirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from xgboost==1.5.1) (1.21.5)\n",
            "Requirement already satisfied: scipy in /usr/local/lib/python3.7/dist-packages (from xgboost==1.5.1) (1.4.1)\n",
            "Installing collected packages: xgboost\n",
            "  Attempting uninstall: xgboost\n",
            "    Found existing installation: xgboost 0.90\n",
            "    Uninstalling xgboost-0.90:\n",
            "      Successfully uninstalled xgboost-0.90\n",
            "Successfully installed xgboost-1.5.1\n"
          ]
        }
      ],
      "source": [
        "!pip install matplotlib==3.5.1\n",
        "!pip install xgboost==1.5.1"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qWfbiBMiJOPT",
        "outputId": "93564a78-b7d4-4a24-d36c-3fe0763f8f25"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hWQTf6oMIwnW",
        "outputId": "5e17b30e-e8b5-4b7e-e5ec-5f7cd008b616"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "numpy version: 1.21.5 pandas version: 1.3.4 Xgb version: 1.5.1\n"
          ]
        }
      ],
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import os\n",
        "import sys\n",
        "import xgboost as xgb\n",
        "import matplotlib.pyplot as plt\n",
        "import datetime as dt\n",
        "import missingno as msno\n",
        "import seaborn as sns\n",
        "import sklearn\n",
        "from scipy import stats\n",
        "%matplotlib inline\n",
        "state = 32\n",
        "np.random.seed(state)\n",
        "print ('numpy version:', np.__version__, 'pandas version:', pd.__version__, 'Xgb version:', xgb.__version__ )\n",
        "\n",
        "sys.path.append('/content/drive/MyDrive/Transshipment/code/')\n",
        "datapath = '/content/drive/MyDrive/Transshipment/data/'\n",
        "modelspath = '/content/drive/MyDrive/Transshipment/models/'"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "kcrURA0ZKmVO"
      },
      "outputs": [],
      "source": [
        "from  importlib import reload\n",
        "import utils\n",
        "reload(utils)\n",
        "from utils import *\n",
        "\n",
        "import smogn\n",
        "reload(smogn)\n",
        "from smogn import *\n",
        "\n",
        "import pipeline\n",
        "reload(pipeline)\n",
        "from pipeline import *"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zToi5tiOIwna"
      },
      "source": [
        "## READ DATA"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 661
        },
        "id": "9wYqjq1fIwnb",
        "outputId": "e41ae221-c87c-405c-bbec-b25553050107"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>eta</th>\n",
              "      <th>ata</th>\n",
              "      <th>atd</th>\n",
              "      <th>vesseldwt</th>\n",
              "      <th>vesseltype</th>\n",
              "      <th>discharge1</th>\n",
              "      <th>load1</th>\n",
              "      <th>discharge2</th>\n",
              "      <th>load2</th>\n",
              "      <th>discharge3</th>\n",
              "      <th>...</th>\n",
              "      <th>load4</th>\n",
              "      <th>stevedorenames</th>\n",
              "      <th>hasnohamis</th>\n",
              "      <th>earliesteta</th>\n",
              "      <th>latesteta</th>\n",
              "      <th>traveltype</th>\n",
              "      <th>previousportid</th>\n",
              "      <th>nextportid</th>\n",
              "      <th>isremarkable</th>\n",
              "      <th>vesselid</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2017-09-19 00:00:00+00</td>\n",
              "      <td>2017-09-19 00:00:00+00</td>\n",
              "      <td>2017-09-22 00:00:00+00</td>\n",
              "      <td>109290.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>90173.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Stevedore_104</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2017-09-19 00:00:00+00</td>\n",
              "      <td>2017-09-19 00:00:00+00</td>\n",
              "      <td>ARRIVAL</td>\n",
              "      <td>981.0</td>\n",
              "      <td>731.0</td>\n",
              "      <td>f</td>\n",
              "      <td>2242.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2017-10-02 00:00:00+00</td>\n",
              "      <td>2017-10-02 00:00:00+00</td>\n",
              "      <td>2017-10-03 00:00:00+00</td>\n",
              "      <td>67170.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Stevedore_109</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2017-10-02 00:00:00+00</td>\n",
              "      <td>2017-10-02 00:00:00+00</td>\n",
              "      <td>ARRIVAL</td>\n",
              "      <td>19.0</td>\n",
              "      <td>15.0</td>\n",
              "      <td>f</td>\n",
              "      <td>5462.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2017-09-30 00:00:00+00</td>\n",
              "      <td>2017-09-30 00:00:00+00</td>\n",
              "      <td>2017-10-01 00:00:00+00</td>\n",
              "      <td>67737.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Stevedore_57</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2017-09-30 00:00:00+00</td>\n",
              "      <td>2017-09-30 00:00:00+00</td>\n",
              "      <td>ARRIVAL</td>\n",
              "      <td>19.0</td>\n",
              "      <td>19.0</td>\n",
              "      <td>f</td>\n",
              "      <td>5251.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2017-10-02 00:00:00+00</td>\n",
              "      <td>2017-10-02 00:00:00+00</td>\n",
              "      <td>2017-10-03 00:00:00+00</td>\n",
              "      <td>43600.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Stevedore_57</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2017-10-02 00:00:00+00</td>\n",
              "      <td>2017-10-02 00:00:00+00</td>\n",
              "      <td>ARRIVAL</td>\n",
              "      <td>15.0</td>\n",
              "      <td>18.0</td>\n",
              "      <td>f</td>\n",
              "      <td>5268.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2017-10-02 00:00:00+00</td>\n",
              "      <td>2017-10-02 00:00:00+00</td>\n",
              "      <td>2017-10-02 00:00:00+00</td>\n",
              "      <td>9231.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Stevedore_98</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2017-10-02 00:00:00+00</td>\n",
              "      <td>2017-10-02 00:00:00+00</td>\n",
              "      <td>ARRIVAL</td>\n",
              "      <td>74.0</td>\n",
              "      <td>27.0</td>\n",
              "      <td>f</td>\n",
              "      <td>5504.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8203</th>\n",
              "      <td>2017-11-03 00:00:00+00</td>\n",
              "      <td>2017-11-03 00:00:00+00</td>\n",
              "      <td>2017-11-04 00:00:00+00</td>\n",
              "      <td>9587.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Stevedore_64</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2017-11-02 00:00:00+00</td>\n",
              "      <td>2017-11-03 00:00:00+00</td>\n",
              "      <td>ARRIVAL</td>\n",
              "      <td>5.0</td>\n",
              "      <td>19.0</td>\n",
              "      <td>f</td>\n",
              "      <td>5681.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8204</th>\n",
              "      <td>2017-11-04 00:00:00+00</td>\n",
              "      <td>2017-11-04 00:00:00+00</td>\n",
              "      <td>2017-11-06 00:00:00+00</td>\n",
              "      <td>9654.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Stevedore_110,Stevedore_57,Stevedore_99,Steved...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2017-11-04 00:00:00+00</td>\n",
              "      <td>2017-11-05 00:00:00+00</td>\n",
              "      <td>SHIFT</td>\n",
              "      <td>391.0</td>\n",
              "      <td>102.0</td>\n",
              "      <td>f</td>\n",
              "      <td>4843.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8205</th>\n",
              "      <td>2017-11-08 00:00:00+00</td>\n",
              "      <td>2017-11-07 00:00:00+00</td>\n",
              "      <td>2017-11-11 00:00:00+00</td>\n",
              "      <td>4726.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>3051.0</td>\n",
              "      <td>Stevedore_89,Stevedore_79,Stevedore_75,Stevedo...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2017-11-07 00:00:00+00</td>\n",
              "      <td>2017-11-10 00:00:00+00</td>\n",
              "      <td>SHIFT</td>\n",
              "      <td>1043.0</td>\n",
              "      <td>19.0</td>\n",
              "      <td>f</td>\n",
              "      <td>3115.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8206</th>\n",
              "      <td>2017-11-10 00:00:00+00</td>\n",
              "      <td>2017-11-10 00:00:00+00</td>\n",
              "      <td>2017-11-10 00:00:00+00</td>\n",
              "      <td>13320.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Stevedore_46</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2017-11-10 00:00:00+00</td>\n",
              "      <td>2017-11-10 00:00:00+00</td>\n",
              "      <td>ARRIVAL</td>\n",
              "      <td>54.0</td>\n",
              "      <td>71.0</td>\n",
              "      <td>f</td>\n",
              "      <td>4623.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8207</th>\n",
              "      <td>2017-11-10 00:00:00+00</td>\n",
              "      <td>2017-11-08 00:00:00+00</td>\n",
              "      <td>2017-11-10 00:00:00+00</td>\n",
              "      <td>11020.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Stevedore_28,Stevedore_99,Stevedore_57,Stevedo...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2017-11-08 00:00:00+00</td>\n",
              "      <td>2017-11-10 00:00:00+00</td>\n",
              "      <td>SHIFT</td>\n",
              "      <td>1060.0</td>\n",
              "      <td>14.0</td>\n",
              "      <td>f</td>\n",
              "      <td>4785.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>8208 rows × 22 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                         eta                     ata                     atd  \\\n",
              "0     2017-09-19 00:00:00+00  2017-09-19 00:00:00+00  2017-09-22 00:00:00+00   \n",
              "1     2017-10-02 00:00:00+00  2017-10-02 00:00:00+00  2017-10-03 00:00:00+00   \n",
              "2     2017-09-30 00:00:00+00  2017-09-30 00:00:00+00  2017-10-01 00:00:00+00   \n",
              "3     2017-10-02 00:00:00+00  2017-10-02 00:00:00+00  2017-10-03 00:00:00+00   \n",
              "4     2017-10-02 00:00:00+00  2017-10-02 00:00:00+00  2017-10-02 00:00:00+00   \n",
              "...                      ...                     ...                     ...   \n",
              "8203  2017-11-03 00:00:00+00  2017-11-03 00:00:00+00  2017-11-04 00:00:00+00   \n",
              "8204  2017-11-04 00:00:00+00  2017-11-04 00:00:00+00  2017-11-06 00:00:00+00   \n",
              "8205  2017-11-08 00:00:00+00  2017-11-07 00:00:00+00  2017-11-11 00:00:00+00   \n",
              "8206  2017-11-10 00:00:00+00  2017-11-10 00:00:00+00  2017-11-10 00:00:00+00   \n",
              "8207  2017-11-10 00:00:00+00  2017-11-08 00:00:00+00  2017-11-10 00:00:00+00   \n",
              "\n",
              "      vesseldwt  vesseltype  discharge1  load1  discharge2  load2  discharge3  \\\n",
              "0      109290.0         5.0         0.0    0.0         0.0    0.0     90173.0   \n",
              "1       67170.0         3.0         0.0    0.0         0.0    0.0         0.0   \n",
              "2       67737.0         3.0         0.0    0.0         0.0    0.0         0.0   \n",
              "3       43600.0         3.0         0.0    0.0         0.0    0.0         0.0   \n",
              "4        9231.0         3.0         0.0    0.0         0.0    0.0         0.0   \n",
              "...         ...         ...         ...    ...         ...    ...         ...   \n",
              "8203     9587.0         3.0         0.0    0.0         0.0    0.0         0.0   \n",
              "8204     9654.0         3.0         0.0    0.0         0.0    0.0         0.0   \n",
              "8205     4726.0         5.0         0.0    0.0         0.0    0.0         0.0   \n",
              "8206    13320.0         3.0         0.0    0.0         0.0    0.0         0.0   \n",
              "8207    11020.0         3.0         0.0    0.0         0.0    0.0         0.0   \n",
              "\n",
              "      ...   load4                                     stevedorenames  \\\n",
              "0     ...     0.0                                      Stevedore_104   \n",
              "1     ...     0.0                                      Stevedore_109   \n",
              "2     ...     0.0                                       Stevedore_57   \n",
              "3     ...     0.0                                       Stevedore_57   \n",
              "4     ...     0.0                                       Stevedore_98   \n",
              "...   ...     ...                                                ...   \n",
              "8203  ...     0.0                                       Stevedore_64   \n",
              "8204  ...     0.0  Stevedore_110,Stevedore_57,Stevedore_99,Steved...   \n",
              "8205  ...  3051.0  Stevedore_89,Stevedore_79,Stevedore_75,Stevedo...   \n",
              "8206  ...     0.0                                       Stevedore_46   \n",
              "8207  ...     0.0  Stevedore_28,Stevedore_99,Stevedore_57,Stevedo...   \n",
              "\n",
              "      hasnohamis             earliesteta               latesteta traveltype  \\\n",
              "0            NaN  2017-09-19 00:00:00+00  2017-09-19 00:00:00+00    ARRIVAL   \n",
              "1            NaN  2017-10-02 00:00:00+00  2017-10-02 00:00:00+00    ARRIVAL   \n",
              "2            NaN  2017-09-30 00:00:00+00  2017-09-30 00:00:00+00    ARRIVAL   \n",
              "3            NaN  2017-10-02 00:00:00+00  2017-10-02 00:00:00+00    ARRIVAL   \n",
              "4            NaN  2017-10-02 00:00:00+00  2017-10-02 00:00:00+00    ARRIVAL   \n",
              "...          ...                     ...                     ...        ...   \n",
              "8203         NaN  2017-11-02 00:00:00+00  2017-11-03 00:00:00+00    ARRIVAL   \n",
              "8204         NaN  2017-11-04 00:00:00+00  2017-11-05 00:00:00+00      SHIFT   \n",
              "8205         NaN  2017-11-07 00:00:00+00  2017-11-10 00:00:00+00      SHIFT   \n",
              "8206         NaN  2017-11-10 00:00:00+00  2017-11-10 00:00:00+00    ARRIVAL   \n",
              "8207         NaN  2017-11-08 00:00:00+00  2017-11-10 00:00:00+00      SHIFT   \n",
              "\n",
              "     previousportid nextportid  isremarkable  vesselid  \n",
              "0             981.0      731.0             f    2242.0  \n",
              "1              19.0       15.0             f    5462.0  \n",
              "2              19.0       19.0             f    5251.0  \n",
              "3              15.0       18.0             f    5268.0  \n",
              "4              74.0       27.0             f    5504.0  \n",
              "...             ...        ...           ...       ...  \n",
              "8203            5.0       19.0             f    5681.0  \n",
              "8204          391.0      102.0             f    4843.0  \n",
              "8205         1043.0       19.0             f    3115.0  \n",
              "8206           54.0       71.0             f    4623.0  \n",
              "8207         1060.0       14.0             f    4785.0  \n",
              "\n",
              "[8208 rows x 22 columns]"
            ]
          },
          "execution_count": 10,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df = pd.read_excel('VesselData.xlsx')\n",
        "df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8BsEP_9jIwnc",
        "outputId": "e8a04340-4093-4ed1-a94b-5e8e94c3c6d4"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 8208 entries, 0 to 8207\n",
            "Data columns (total 22 columns):\n",
            " #   Column          Non-Null Count  Dtype  \n",
            "---  ------          --------------  -----  \n",
            " 0   eta             8208 non-null   object \n",
            " 1   ata             8208 non-null   object \n",
            " 2   atd             8208 non-null   object \n",
            " 3   vesseldwt       8206 non-null   float64\n",
            " 4   vesseltype      8208 non-null   float64\n",
            " 5   discharge1      8208 non-null   float64\n",
            " 6   load1           8208 non-null   float64\n",
            " 7   discharge2      8208 non-null   float64\n",
            " 8   load2           8208 non-null   float64\n",
            " 9   discharge3      8208 non-null   float64\n",
            " 10  load3           8208 non-null   float64\n",
            " 11  discharge4      8208 non-null   float64\n",
            " 12  load4           8208 non-null   float64\n",
            " 13  stevedorenames  8206 non-null   object \n",
            " 14  hasnohamis      0 non-null      float64\n",
            " 15  earliesteta     8208 non-null   object \n",
            " 16  latesteta       8208 non-null   object \n",
            " 17  traveltype      8208 non-null   object \n",
            " 18  previousportid  8208 non-null   float64\n",
            " 19  nextportid      8208 non-null   float64\n",
            " 20  isremarkable    8208 non-null   object \n",
            " 21  vesselid        8208 non-null   float64\n",
            "dtypes: float64(14), object(8)\n",
            "memory usage: 1.4+ MB\n"
          ]
        }
      ],
      "source": [
        "df.info()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RN-8INNWM_Vf"
      },
      "source": [
        "### INVESTIGATE TARGET VARIABLES\n",
        "load1, discharge1 --> ore transshipment <br>\n",
        "load2, discharge2 --> coal transshipment <br>\n",
        "load3, discharge3 --> oil transshipment <br>\n",
        "load4, discharge4 --> petrol transshipment <br>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "TN59K5XrM_Vf",
        "outputId": "27c361d3-001f-48e5-a074-e0a3fa8f6072"
      },
      "outputs": [
        {
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>discharge1</th>\n",
              "      <th>load1</th>\n",
              "      <th>discharge2</th>\n",
              "      <th>load2</th>\n",
              "      <th>discharge3</th>\n",
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              "<p>8208 rows × 8 columns</p>\n",
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            ],
            "text/plain": [
              "      discharge1  load1  discharge2  load2  discharge3  load3  discharge4  \\\n",
              "0            0.0    0.0         0.0    0.0     90173.0    0.0         0.0   \n",
              "1            0.0    0.0         0.0    0.0         0.0    0.0         0.0   \n",
              "2            0.0    0.0         0.0    0.0         0.0    0.0         0.0   \n",
              "3            0.0    0.0         0.0    0.0         0.0    0.0         0.0   \n",
              "4            0.0    0.0         0.0    0.0         0.0    0.0         0.0   \n",
              "...          ...    ...         ...    ...         ...    ...         ...   \n",
              "8203         0.0    0.0         0.0    0.0         0.0    0.0         0.0   \n",
              "8204         0.0    0.0         0.0    0.0         0.0    0.0         0.0   \n",
              "8205         0.0    0.0         0.0    0.0         0.0    0.0      3537.0   \n",
              "8206         0.0    0.0         0.0    0.0         0.0    0.0         0.0   \n",
              "8207         0.0    0.0         0.0    0.0         0.0    0.0         0.0   \n",
              "\n",
              "       load4  \n",
              "0        0.0  \n",
              "1        0.0  \n",
              "2        0.0  \n",
              "3        0.0  \n",
              "4        0.0  \n",
              "...      ...  \n",
              "8203     0.0  \n",
              "8204     0.0  \n",
              "8205  3051.0  \n",
              "8206     0.0  \n",
              "8207     0.0  \n",
              "\n",
              "[8208 rows x 8 columns]"
            ]
          },
          "execution_count": 12,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "targets_df = df.filter(regex= r\"load\\d*|discharge\\d*\")\n",
        "targets_df"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7SErTx03M_Vg"
      },
      "source": [
        "Count non-zero values in the target space"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "id": "3Qi7rHe6S_oJ",
        "outputId": "5af08c0a-e2ea-465b-de73-9e1745e3c5f8"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "ax = targets_df.apply(lambda x: x[x > 0].count()).plot.bar(figsize = (10,5))\n",
        "ax.bar_label(ax.containers[0], fontsize = 12)\n",
        "ax.set_title('Non-zero values per target', fontsize = 15)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "L6-OBL6dM_Vh"
      },
      "source": [
        "#### TARGETS INITIAL INSIGHTS\n",
        "Based on a quick search https://bulkcarrierguide.com/cargo.html, I proceeded with an assumption: <br>\n",
        "The handling of ore & coal could be similar (i.e., solid bulk cargo).<br>\n",
        "Likewise, oil & petrol (i.e., liquid bulk) might have similar handling routines in terms of safety, regulations, transportation. <br>\n",
        "Therefore, we can maximize the supervision signal by concatenating similar bulk load and discharge targets, such that:<br>\n",
        "\n",
        "1. concatenate: load1 + load2 --> solid bulk load <br>\n",
        "2. concatenate: load3 + load4 --> liquid bulk load <br>\n",
        "3. concatenate: discharge1 + discharge2 --> solid bulk discharge <br>\n",
        "4. concatenate: discharge3 + discharge4 --> liquid bulk discharge  <br>\n",
        "5. Construct a binary variable ```bulk``` to define the cargo type {'solid': 0, 'liquid': 1}"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "id": "WkBAAM2nM_Vi",
        "outputId": "c68477f6-2965-4c49-d524-73cb34f61cf9"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>bulk</th>\n",
              "      <th>load</th>\n",
              "      <th>discharge</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>solid</td>\n",
              "      <td>NaN</td>\n",
              "      <td>72741.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>solid</td>\n",
              "      <td>4188.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>44</th>\n",
              "      <td>solid</td>\n",
              "      <td>39786.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>62</th>\n",
              "      <td>solid</td>\n",
              "      <td>NaN</td>\n",
              "      <td>50743.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>88</th>\n",
              "      <td>solid</td>\n",
              "      <td>7005.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8188</th>\n",
              "      <td>liquid</td>\n",
              "      <td>NaN</td>\n",
              "      <td>15980.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8191</th>\n",
              "      <td>liquid</td>\n",
              "      <td>4206.0</td>\n",
              "      <td>4196.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8192</th>\n",
              "      <td>liquid</td>\n",
              "      <td>NaN</td>\n",
              "      <td>139431.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8194</th>\n",
              "      <td>liquid</td>\n",
              "      <td>NaN</td>\n",
              "      <td>28127.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8205</th>\n",
              "      <td>liquid</td>\n",
              "      <td>3051.0</td>\n",
              "      <td>3537.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1606 rows × 3 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "        bulk     load  discharge\n",
              "5      solid      NaN    72741.0\n",
              "23     solid   4188.0        NaN\n",
              "44     solid  39786.0        NaN\n",
              "62     solid      NaN    50743.0\n",
              "88     solid   7005.0        NaN\n",
              "...      ...      ...        ...\n",
              "8188  liquid      NaN    15980.0\n",
              "8191  liquid   4206.0     4196.0\n",
              "8192  liquid      NaN   139431.0\n",
              "8194  liquid      NaN    28127.0\n",
              "8205  liquid   3051.0     3537.0\n",
              "\n",
              "[1606 rows x 3 columns]"
            ]
          },
          "execution_count": 14,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "processor = preprocess(dataframe= df)\n",
        "targs = processor.format_targets(targets_df)\n",
        "targs"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ObLyi1DDM_Vi"
      },
      "source": [
        "### FEATURES SELECTION & ENGINEERING"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "I-MZlT86M_Vj"
      },
      "source": [
        "1. Dropping non-informative features, (i.e., id columns): ['previousportid', 'nextportid','vesselid']\n",
        "2. Engineering a new feature ```n_stevs``` outlining the number of stevedores per ship\n",
        "3. Construct a ```process_time``` feature as the difference in days from the 2 dates ```latesteta ``` - ```earliesteta```\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "id": "LeZHSXNNM_Vj",
        "outputId": "d49de004-63fd-4fe9-adbd-7bb67dac1e6a"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>vesseldwt</th>\n",
              "      <th>vesseltype</th>\n",
              "      <th>traveltype</th>\n",
              "      <th>n_stevs</th>\n",
              "      <th>process_time</th>\n",
              "      <th>bulk</th>\n",
              "      <th>load</th>\n",
              "      <th>discharge</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>109290.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>ARRIVAL</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>liquid</td>\n",
              "      <td>NaN</td>\n",
              "      <td>90173.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>74518.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>ARRIVAL</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>solid</td>\n",
              "      <td>NaN</td>\n",
              "      <td>72741.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>17069.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>ARRIVAL</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>liquid</td>\n",
              "      <td>NaN</td>\n",
              "      <td>14044.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>7133.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>ARRIVAL</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>solid</td>\n",
              "      <td>4188.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>158070.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>ARRIVAL</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>liquid</td>\n",
              "      <td>NaN</td>\n",
              "      <td>131231.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8188</th>\n",
              "      <td>25432.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>ARRIVAL</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>liquid</td>\n",
              "      <td>NaN</td>\n",
              "      <td>15980.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8191</th>\n",
              "      <td>4993.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>SHIFT</td>\n",
              "      <td>2</td>\n",
              "      <td>7</td>\n",
              "      <td>liquid</td>\n",
              "      <td>4206.0</td>\n",
              "      <td>4196.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8192</th>\n",
              "      <td>158583.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>SHIFT</td>\n",
              "      <td>2</td>\n",
              "      <td>8</td>\n",
              "      <td>liquid</td>\n",
              "      <td>NaN</td>\n",
              "      <td>139431.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8194</th>\n",
              "      <td>37296.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>ARRIVAL</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>liquid</td>\n",
              "      <td>NaN</td>\n",
              "      <td>28127.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8205</th>\n",
              "      <td>4726.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>SHIFT</td>\n",
              "      <td>4</td>\n",
              "      <td>3</td>\n",
              "      <td>liquid</td>\n",
              "      <td>3051.0</td>\n",
              "      <td>3537.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1606 rows × 8 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "      vesseldwt vesseltype traveltype  n_stevs  process_time    bulk    load  \\\n",
              "0      109290.0        5.0    ARRIVAL        1             0  liquid     NaN   \n",
              "5       74518.0        2.0    ARRIVAL        1             0   solid     NaN   \n",
              "8       17069.0        5.0    ARRIVAL        1             1  liquid     NaN   \n",
              "23       7133.0        3.0    ARRIVAL        1             0   solid  4188.0   \n",
              "27     158070.0        5.0    ARRIVAL        1             0  liquid     NaN   \n",
              "...         ...        ...        ...      ...           ...     ...     ...   \n",
              "8188    25432.0        5.0    ARRIVAL        1             0  liquid     NaN   \n",
              "8191     4993.0        5.0      SHIFT        2             7  liquid  4206.0   \n",
              "8192   158583.0        5.0      SHIFT        2             8  liquid     NaN   \n",
              "8194    37296.0        5.0    ARRIVAL        1             0  liquid     NaN   \n",
              "8205     4726.0        5.0      SHIFT        4             3  liquid  3051.0   \n",
              "\n",
              "      discharge  \n",
              "0       90173.0  \n",
              "5       72741.0  \n",
              "8       14044.0  \n",
              "23          NaN  \n",
              "27     131231.0  \n",
              "...         ...  \n",
              "8188    15980.0  \n",
              "8191     4196.0  \n",
              "8192   139431.0  \n",
              "8194    28127.0  \n",
              "8205     3537.0  \n",
              "\n",
              "[1606 rows x 8 columns]"
            ]
          },
          "execution_count": 15,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "pd.options.mode.chained_assignment = None\n",
        "processor = preprocess(dataframe= df)\n",
        "feature_df = processor.format_features()\n",
        "feature_df"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_7RFAEsoM_Vj"
      },
      "source": [
        "Investigating missing values"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "id": "3Y9HFlm9M_Vk",
        "outputId": "73c4583d-3c19-44a6-b9aa-e551fcee2d7b"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1080x504 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "msno.matrix(feature_df.sort_values(by=['vesseltype'], ascending=True), figsize= (15,7), fontsize = 12)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "id": "6XD01QBrM_Vk",
        "outputId": "5ecf4e14-4304-4e2b-8afe-81d086f63a28"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "D:\\anaconda3\\envs\\omdena\\lib\\site-packages\\pandas\\io\\formats\\format.py:1429: FutureWarning: Index.ravel returning ndarray is deprecated; in a future version this will return a view on self.\n",
            "  for val, m in zip(values.ravel(), mask.ravel())\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "feature_df[['load','discharge']].isna().groupby(by=feature_df['vesseltype']).sum().plot.bar(figsize = (10,5), title = 'NaNs per vesselType')\n",
        "plt.grid(axis = 'y', linestyle='-')\n",
        "feature_df[['load','discharge', 'vesseltype']].groupby(by=feature_df['vesseltype'], dropna= True).count().plot.bar(figsize = (10,5), title = 'Target/Feature dist per vesselType')\n",
        "plt.legend(['load','discharge','num_instances']) \n",
        "plt.grid(axis = 'y', linestyle='-')\n",
        "feature_df[['load','discharge']].groupby(by=feature_df['vesseltype'], dropna= True).sum().plot.bar(figsize = (10,5), title = 'Label Value per vesselType')\n",
        "plt.grid(axis = 'y', linestyle='-')\n",
        "# feature_df[['vesseltype']].groupby(by=feature_df['vesseltype'], dropna= True).count().plot.bar(figsize = (10,5), title = 'Row Count per vesseltype')\n",
        "plt.show()\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "id": "cvKCNzyvM_Vk",
        "outputId": "88db65c4-5447-49df-9839-bb09d2e95251"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1080x432 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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RCSUleCIStH2V9QEPzwQYkNXcgxdkoRXjb9HgceM0ERERkSAowRORoO0rr2tXD156UiwZSbHs1FIJIiIiIiGlBE9EguJp8nGouoHeAS6R0KwgI5E95XVhikpERESkZ1KCJyJBOVjVgLWQ344ePPAneGVK8ERERERCSQmeiASlvUskNCvIdHrwrLXhCEtERESkR1KCJyJBaV7kvN0JXkYitY1NVNR5whGWiIiISI+kBE9EgnKoqgGAXqntT/AAijRMU0RERCRkYiIdgIh0b8XVDbhdhozEwBY5b1aQ6SR4e8rrGFuQ3rGLP9Cxw7qk6dF0MyIiIhIpSvBEJCjFVY1kJ8fhcrVvDbfmHrygCq3M6/ihXc7QaLoZERERiRQN0RSRoByqbiAnJb7dx2Ulx5EQ62KvlkoQERERCRkleCISlOLqBnJT25/gGWPID3YtvPn+Fg22zneaiIiISBCU4IlIUIqrOtaDByFY7PwGf4sGi29wmoiIiEgQlOCJSIdZaymubiQnNa5Dx/fN1GLnIiIiIqGkBE9EOqyyzktjk4/cDvbg5acnUlLTSL2nKcSRiYiIiPRMSvBEpMMOVTtr4HVkDh4cvVSCiIiIiARPCZ6IdFjzIufBzMGDIJdKEBEREZHDlOCJSIcVqwdPREREpEtRgiciHdac4HW0By8vLQGXQWvhiYiIiIRITKQDEJHu61BVA26XISMxtkPHx7pd9E5L6PgQTduxw7qkK6PpZkRERCRS1IMnIh1WXN1ATkocLpfp8DkKMhMpUg+eiIiISEgowRORDiuubuzw8Mxm+RmJGqIpIiIiEiJK8ESkww5VNYQkwTtQWU+TrwNDFKf4WzR4dYrTRERERIKgOXgi0mGlNY0My0sJ6hz56Ql4mizF1Q3kpSW07+DlQV26aymLppsRERGRSFEPnoh0WFltI1lJcUGdo0+6s1SChmmKiIiIBE8Jnoh0SL2nidrGJjKTg0vw8v2Lne+rqA9FWCIiIiI9mhI8EemQstpGALKCTvCcYZnqwRMREREJnhI8EemQ0honwcsMcohmemIsibFu9parB09EREQkWErwRKRDymo8QPA9eMYY8jMS2FehHjwRERGRYKmKpoh0SGltcw9ebNDn6vBaeNcHfemuY0g03YyIiIhEihI8EemQsuYhmkH24AHkpyeycX9V+w+cH/Slu44Z0XQzIiIiEilhG6JpjOlnjHnXGLPBGLPOGPPtVvYxxpi/GmO2GmNWG2MmhyseEQmt5jl4GYnB9+D1yUjgUFUDDd6moM8lIiIi0pOFcw6eF/h/1tpRwEzgm8aY0cfscwEwzN/mAfeFMR4RCaGy2kbSE2OJcQf/YyTfvxbegYqG9h24zN+iQekyp4mIiIgEIWwJnrV2n7V2uf/jKmADUHDMbnOBf1vHIiDDGNMnXDGJSOiU1jQGXWClWfNaeHvbW2hlqr9Fg9emOk1EREQkCJ1SRdMYMxCYBHx6zFsFwO4WnxdxfBKIMWaeMWapMWbpoUOHwhaniASurLYxJAVWwBmiCVoLT0RERCRYYU/wjDEpwDPAbdbaymPfbuUQe9wGa+dba6daa6fm5uaGI0wRaafSGk/oevD8QzT3VWgtPBEREZFghDXBM8bE4iR3j1lrn21llyKgX4vP+wJ7wxmTiIRGeW1j0IucN0uMc5OZFKsePBEREZEghbOKpgEeAjZYa+9uY7cXgK/4q2nOBCqstfvCFZOIhIa1NqRz8AD6pHdwLTwREREROSyc6+CdBlwDrDHGrPRv+wnQH8Baez/wCnAhsBWoBb4axnhEJETqPE00eH0hWQOvWX5GAkVlSvBEREREghG2BM9a+yGtz7FruY8FvhmuGEQkPJrXwMsK0RBNcCppLt5RGrLziYiIiPRE4ezBE5EoVVbjAQhpD16f9EQq671UN3hJiQ/wR9PSkF0+8s6PppsRERGRSFGCJyLtVlrr78FLDs0yCeAM0QTYV17HsLzUwA6aErLLR15WNN2MiIiIREqnrIMnItGlzD9EM1RVNKHlYudaKkFERESko5TgiUi7HZ6DF9Ihmh1Y7Hyev0WDT+c5TURERCQISvBEpN3KahtxGUhLCN0Qzby0BFzGGaIZsAf9LRpse9BpIiIiIkFQgici7VZa4yxy7nKdsFBuu8S6XfRKTdAQTREREZEgKMETkXYrq20MaQXNZn0yErTYuYiIiEgQlOCJSLuV1jSGdA28ZvkZiexTD56IiIhIhynBE5F2K6vxkJEUuvl3zfLTnR48a23Izy0iIiLSEyjBE5F2K61tDGkFzWb5GYk0eH2Hq3SKiIiISPtooXMRaRdrLWU1YZqDl+6shbevop7slPiTHzA55CFETmY03YyIiIhEihI8EWmX6gYvXp8lMxxDNDOctfD2lNcxtiD95AcsC3kIkXNBNN2MiIiIRIqGaIpIu5TXegDICFORFWjnWngiIiIicpgSPBFpl4o6f4KXGPoevOzkOOJiXKqkKSIiItJBSvBEpF3C2YNnjKFPegJ7Au3BM/4WDR43ThMREREJghI8EWmX8jqnwmV6GHrwAPLTtRaeiIiISEcpwRORdjk8RDMMRVYA+mQkaA6eiIiISAcpwRORdmkeohnOHrz9lfV4m3xhOb+IiIhINFOCJyLtUlHnISHWRUKsOyznz89IxGfhYFVDWM4vIiIiEs2U4IlIu5TXNoat9w6cIZoA+yo0TFNERESkvZTgiUi7VNR5yEgMfQXNZgX+tfD2lKvQioiIiEh7xUQ6ABHpXsprPaSHqcAKQJ90fw9eIIVWHghbGJ1vejTdjIiIiESKEjwRaZeKOg/9s5LCdv7UhFhS42MCWyphXtjC6HxDo+lmREREJFI0RFNE2qW81hPWOXjgFFoJeLFzERERETlMCZ6ItEtFnSdsa+A165OREFiRlfn+Fg22zneaiIiISBCU4IlIwOo9TdR5mshICl+RFYA+6YnsDaTIyg3+Fg0W3+A0ERERkSAowRORgFXWhXeR82YFGQmU1jRS72kK63VEREREoo0SPBEJWHknJXh90p2lEgIqtCIiIiIihynBE5GAVfgTvM6YgwewV4VWRERERNpFCZ6IBKy81p/ghXGhcziy2LkSPBEREZH2UYInIgErr20Ewt+D17t5sXMN0RQRERFpFyV4IhKw5iGaaWGegxcf4yYnJV49eCIiIiLtFBPpAESk+6io8+AykBof/h8d+RkJ7D1ZD54Nexid58pouhkRERGJFPXgiUjAyms9pCfG4nKZsF+rICORorLasF9HREREJJoowRORgJXXecK+yHmz/llJFJXV4fOpZ0tEREQkUErwRCRg5bWNYZ9/16xvVhKNXh8Hqxra3mmKv0WDV6c4TURERCQImoMnIgGr7OQePIBdpbWHq2oeZ3mnhNI5yqLpZkRERCRS1IMnIgFzhmh2Tg9ec4K3u1Tz8EREREQCpQRPRAJWXusho5OGaBZkJGKM04MnIiIiIoFRgiciAfH5LJX1ThXNzhAX4yI/PVE9eCIiIiLtoARPRAJSVe/FWkjvpDl4AP2yEtWDJyIiItIOSvBEJCDldY0AnTZEE6BfZpISPBEREZF2UBVNEQlIea0HoNOKrIBTaOVgVQP1niYSYt3H73B9p4USfkOi6WZEREQkUpTgiUhAyuucBK+z5uAB9M92KmkWldUytFfq8TvM77RQwm9GNN2MiIiIRIqGaIpIQCrqOr8Hr59/qYTCYg3TFBEREQmEEjwRCUhFrTMHLz2x84qsDM5JBqCwpKb1HZb5WzQoXeY0ERERkSBoiKaIBKR5Dl5nDtHMSIojMymW7cVtJHhT/a+200IKn9f8N3NlNNyMiIiIRIp68EQkIBV1HpLi3MTFdO6PjUE5yew41EaCJyIiIiJHUYInIgEpr/N06hIJzQblpLCjrR48ERERETlK2BI8Y8zDxpiDxpi1bbx/pjGmwhiz0t9+Hq5YRCR45bWeTl3kvNng3GT2V9ZT0+Dt9GuLiIiIdDfh7MH7F3D+SfZZaK2d6G+/DGMsIhKkirrGCPXgnaTQioiIiIgcFrYEz1r7AVAarvOLSOeqqPN0aoGVZs0JnoZpioiIiJxcpOfgnWKMWWWMedUYMybCsYjICZTXejp1DbxmA7P9CZ4KrYiIiIicVCSXSVgODLDWVhtjLgSeA4a1tqMxZh4wD6B///6dFqCIOKy1lNd5SI9AgpcY5yY/PaH1HrylnR5O+JwfTTcjIiIikRKxHjxrbaW1ttr/8StArDEmp41951trp1prp+bm5nZqnCIC9R4fjV4fGZ24yHlLg3KT2Xao+vg3pvhbNMia4jQRERGRIEQswTPG9DbGGP/H0/2xlEQqHhFpW0Vd5y9y3tLwvFS2HKzG59Mi4CIiIiInErYhmsaYJ4AzgRxjTBFwBxALYK29H/gicJMxxgvUAV+21uq3N5EuqLyuESAic/AARuSlUtvYxJ7yOvplJR15Y57/dX5EwgqtT/03MyMabkZEREQiJWwJnrX2ipO8fy9wb7iuLyKhU17r9OBFYpkEgOG9UwHYtL/q6ATvQf9rNORE2/w3owRPREREghDpKpoi0g00J3iRKLICMKxXCgCbDlRF5PoiIiIi3YUSPBE5qcoIz8FLTYilICORzUrwRERERE5ICZ6InNSROXiRqaIJMKJ3Kpv2K8ETEREROREleCJyUuW1HmJchuQ4d8RiGJ6XyrZD1XiafBGLQURERKSrU4InIidVXuchIykW/8omETGidwqeJtv6guciIiIiAoSxiqaIRI+KOg9pEZp/12xUnzQANuyrZHieU1WTyREMKNQyo+lmREREJFIC6sEzxjxjjLnIGKMeP5EeqKLWE7ElEpoNzU0hIdbFqt0VRzYu87docMEyp4mIiIgEIdCE7T7gSmCLMeZ3xpiRYYxJRLqY8rrGiFXQbBbjdjEmP53VReURjUNERESkKwsowbPWvmWtvQpnQFQh8KYx5mNjzFeNMZH9rU9Ewq681kNmBCtoNhvfN511eyvxqtCKiIiISKsCHnJpjMkGrgO+AawA/oKT8L0ZlshEpMuoqPVEbJHzlib0zaDO08TWQ9XOBuNv0eBx4zQRERGRIAQ6B+9ZYCGQBFxsrb3EWvuUtfZWICWcAYpIZHmafFQ1eMlIjHwP3ri+6QCsLqo4yZ4iIiIiPVOgPXj/sNaOttb+j7V2H4AxJh7AWjs1bNGJSMRV1nkAyOgCPXiDspNJjY/RPDwRERGRNgSa4P26lW2fhDIQEemayrtQgudyGcb1TWf5zvJIhyIiIiLSJZ1wHTxjTG+gAEg0xkziyGyXNJzhmiIS5cprnQQv0lU0m80YlM2f397szAuka8QkIiIi0lWcbKHz83AKq/QF7m6xvQr4SZhiEpEupKKuEYCMLlBFE+CUIdn86S34dEcJ59I70uGIiIiIdCknTPCstY8AjxhjvmCtfaaTYhKRLqS5By/SC503m9AvnfgYF4u2lyrBExERETnGyYZoXm2tfRQYaIz57rHvW2vvbuUwEYkihxO8LjAHDyA+xs3UgZks2l4CD0Q6mhCaHk03IyIiIpFysiGayf5XLYUg0kOV13kwBlITukaCBzBzUDZ3v7WZ8tsbu8zQ0aANnRfpCERERCQKnGyI5gP+1190Tjgi0tVU1DaSlhCL29V1FuE+dWgO//vmZj7YUswlE/IjHY6IiIhIlxHoQud/MMakGWNijTFvG2OKjTFXhzs4EYm88jpPlxme2WxivwxyUuKpvscD8yMdTYhsne80ERERkSAEug7eudbaSuCzQBEwHPh+2KISkS6jvNbTZQqsNHO7DOeM7sWVDw2AGyIdTYgsvsFpIiIiIkEINMFr/u3uQuAJa21pmOIRkS6mvM5Dehec53buGFXQFBERETlWoAnei8aYjcBU4G1jTC5QH76wRKSrqKht7HI9eACnDsmOdAgiIiIiXU5ACZ619kfAKcBUa60HqAHmhjMwEekauuIcPHCWS2hW0+CNYCQiIiIiXcfJlkloaRTOengtj/l3iOMRkS7E57NU1HW9OXjHenHVXr48vX+kwxARERGJuECraP4fcBcwC5jmb1PDGJeIdAFV9V6spUvOwWvp8cW7Ih2CiIiISJcQaA/eVGC0tdaGMxgR6VrK6xoBunwP3uqiClbuLmdiv4xIhyIiIiISUYEWWVkLqGSdSA9TXusB6JJz8ACwUF3vJSMplnve3hLpaIJzpXWaiIiISBACTfBygPXGmNeNMS80t3AGJiKRV17XxRM8ICU+hutPH8zbGw+yuqg80uGIiIiIRFSgQzTvDGcQItI1ldc6QzTTE7v2HLyvnDKABxdu54+vb+LfX5uOMSbSIYmIiIhERKDLJLwPFAKx/o+XAMvDGJeIdAEVXb0Hb4rTUhNi+dZZw1i4pZg31x+IdFQd8+oUp4mIiIgEIdAqmtcD/wUe8G8qAJ4LU0wi0kU0z8FL76pFVpZz+E9NXzllACPyUvnlS+up9zRFNKwOKVvuNBEREZEgBDoH75vAaUAlgLV2C9ArXEGJSNdQXushJT6GWHegPyoiJ8bt4s5LxlBUVsf972+LdDgiIiIiERHob20N1trG5k/8i52r3JtIlCuva+y6vXetOGVINhdPyOe+97axu7Q20uGIiIiIdLpAE7z3jTE/ARKNMecATwMvhi8sEekKKmo9XXf+XRt+cuFIXMbwm5c3RDoUERERkU4XaIL3I+AQsAa4AXgF+Fm4ghKRrqG8rvsleH3SE7nlrKG8tm4/H24pjnQ4IiIiIp0q0CqaPpyiKjdba79orX3QWqshmiJRrry2kYwuvkRCa74+axD9s5K488V1eJp8kQ5HREREpNOcMMEzjjuNMcXARmCTMeaQMebnnROeiERSRZ2H9HD24DV5oGgZ7FgINR3obbve346REOvmpxeNYuvBahYs3xN0mJ1iyPVOExEREQnCyRY6vw2neuY0a+0OAGPMYOA+Y8x3rLV/CnN8IhIh1lrKaz1khKPIirWw+EF4/3dQW+LfaGD0XDjvt5BeENh55rf91rmj8xjfN517393KpZMLun4l0BknuBkRERGRAJ3sN56vAFc0J3cA1trtwNX+90QkStU0NuH12dDPwfP54Lmb4NXvQ95YuOwRuOY5OO3bsOUNuP802BP8enDGGL511jB2ldby3Ipu0osnIiIiEqSTJXix1trjxk1Zaw8B3avygoi0S3mtszJKSOfgWQuv/QhWPQFn/hi+8jyM+RwMmQ3n/AJuWAjxqfDvuVC09OTnW+ZvbZgzqhej+6Txj4U76PLThkuXOU1EREQkCCdL8Bo7+J6IdHPltR6A0M7BW/NfWPwAnHILfOaHYMzR7+cMha++BklZ8NTVUH3wxOeb6m9tMMZw7akD2HSgiiWFZUGHH1avTXWaiIiISBBOluBNMMZUttKqgHGdEaCIREZFnZPghWwOXvVBZ1hm32lwzi+PT+6apRfA5Y9BXTn892vgawrqshdPyCc1IYZHF+0M6jwiIiIi3cEJEzxrrdtam9ZKS7XWaoimSBRr7sHLSArREM03fgaNNTD3b+Byn3jf3mPhorugcCEsfTioyybFxfDFKX15de0+SqobgjqXiIiISFfXxcvKiUiklNf55+CFYojmwQ2w+j8w8ybIHRHYMROvgsGz4a1fQOXeoC5/+bR+eJosr6zdH9R5RERERLo6JXgi0qrDc/BCMUTz3d9CXAqcdlvgxxgDn/0T+Dzwxu1BXX5EXipDe6Xw0qrgEkURERGRrk4Jnoi0qqLOQ0Ksi4TYkwynPJkD62HDC3DKzU7xlPbIGgSnfBPW/hf2ruhwCMYYLh6fz+LCUvZX1Hf4PCIiIiJdnRI8EWlVeW1jaJZIWDwfYhJgxo0dO/60b0NiFrz5c2eZhQ767IQ+WAsvr9nX4XOIiIiIdHVK8ESkVeW1nuDn39WVw+qnYNxl7e+9a5aQDmd8H3Z8ADs/Pvq9pf4WgCG5KYzsncrr67roPLzzlzpNREREJAhK8ESkVeW1nuDn3618DDy1MP364M4z5TpIyoGF/3vMdn8L0Fkje7FsZxmV9Z7g4gmHrClOExEREQlC2BI8Y8zDxpiDxpi1bbxvjDF/NcZsNcasNsZMDlcsItJ+ZbWNZCUHMUTTWlj2CPSdDn0mBBdMXJIzh2/b27B3ZYdPM3tkL5p8lg+3FAcXj4iIiEgXFc4evH8B55/g/QuAYf42D7gvjLGISDuV1TaSGUyCt28lFG+CiVeGJqBp34D4NPjwT0e2zfO3AE3ql0FaQgzvbjwYmphC6dN5ThMREREJQtgSPGvtB0DpCXaZC/zbOhYBGcaYPuGKR0QC5/NZymo9ZAWzyPmqp8AdB2M+F5qgEtJh2tdh/fNQvMXZ9qC/BSjG7eKM4bm8t/kQNoiCLWGx7UGniYiIiAQhknPwCoDdLT4v8m8TkQirqvfS5LMd78Fr8jpLGww/HxIzQxfYzJvBHQufPtDhU5w5oheHqhrYsK8qdHGJiIiIdBGRTPBMK9ta/ZO6MWaeMWapMWbpoUOHwhyWiJTWNgKQldzBIis73oOaQzD+8tAFBZDSC8Z8HlY9AfWVHTrFqUOyAVi0vSSUkYmIiIh0CZFM8IqAfi0+7wvsbW1Ha+18a+1Ua+3U3NzcTglOpCcrrXESvMyODtHc8CLEpcDQs0MYld+MedBYDaue7NDh+RmJDMhOUoInIiIiUSmSCd4LwFf81TRnAhXWWq1ALNIFlNU09+B1IMHzNcHGl2HYuRCbEOLIgIIpTls8v8OnmDkom093lOLzdbF5eCIiIiJBCucyCU8AnwAjjDFFxpivG2NuNMbc6N/lFWA7sBWnTMLN4YpFRNqneYhmh3rwdi1yhmeOviTEUbUwfR6UbOnw4TOHZFFR52Hjfs3DExERkegSE64TW2uvOMn7FvhmuK4vIh0XVA/ehhfBHQ9DzwlxVC2MuRRe/ykM2gGZg9p9+IxBR+bhjc5PC3V0HZOppUBFREQkeJEcoikiXVRpbSNxMS6S4tztO9Ba2PQKDJkN8SnhCQ4gJt5ZX++6qfDegXYfnp+RSP+sJBbvONFKLp3sgmVOExEREQmCEjwROU5ZTSNZSXEY01qx2xMo3gLlO535d+E2+Svg8zoVNTtgyoBMlu8q63rr4YmIiIgEQQmeiByntKaxY8Mzt77pvA4L4/DMZjnDoP+psPzfTs9hO03qn8HBqgb2VdSHITgRERGRyFCCJyLH6XCCt+VNyBkBGf1DH1Rrvv4qfHs57Py43YdO6ucswL58V1moo+qYx43TRERERIKgBE9EjlNW6yGzvQleYw3s/Khzeu+Otfzf7T5kZJ9U4mNcrNhVHvp4RERERCJECZ6IHKe0ppGspNj2HbTjA2hqDM/i5iez/jmoK2/XIbFuF+P7prOiq/TgiYiIiISAEjwROYq3yUdFXQd68La8CbHJMODU8AR2It56WPN0uw+b1D+TtXsrafA2hSEoERERkc6nBE9EjlJe5wHauQaetU6BlcGfcZYw6Gy9x8OK/2v3YZP6ZdDo9bFhnxY8FxERkeigBE9EjtK8yHlmUjsSvNLtUL4LhpwVpqhOYtLVsG8V7F/brsMmD/AXWtmpYZoiIiISHZTgichRSv0JXrt68Ha877wOnh2GiAIw7jJwx8HKx9p1WF5aAvnpCazYXR6euEREREQ6WUykAxCRrqWstgM9eNvfh9R8yB4Spqja8ID/NSkLRlwAq5+Cs38BMYHHPql/ZtcotDL9gZPvIyIiInIS6sETkaOU1rRzDp7PB4ULYdAZYDp5Hbd5/gYw8WqoLYEtr7frFOP6plNUVke5P7GNmKHznCYiIiISBCV4InKU5h68jECXSTi43kmsBn8mjFEFYMhZkNIbVrRvmObY/HQA1u6pDEdUIiIiIp1KCZ6IHKW0ppHkODcJse7ADtjxgfM68PTwBdWW+f4G4I6BCV+GLW9A1YGATzG2IA2ANXsqQh9fe2yd7zQRERGRICjBE5GjlNU0tm8NvB3vQ9ZgyOgXvqDacoO/NZt0NdgmZy5egDKS4uibmcjavRFO8Bbf4DQRERGRICjBE5GjlNY2Bj7/rskLhR/BoAgPz2yWMwz6TneqaVob8GHjCtJZG+kePBEREZEQUIInIkcpq2kMvILmvpXQWOUUWOkqJl0FhzbCnuUBHzK2IJ2dJbVU+Bd5FxEREemulOCJyFFKaxvJDrQHr3Ch8xqJ+XdtGfN5iEmElY8GfMjYAqfQyrpID9MUERERCZISPBE5SlmNJ/A5eDs/gZzhkJIb3qDaIyENRl8Ca54BT11Ah4zNdwqtrFMlTREREenmlOCJyGH1niaqG7yBzcHzNcGuRdD/lPAH1l4Tr4KGCtj4ckC7Z6fEk5+eEPlKmiIiIiJBUoInIoeV1Dhr4OWkBJDgHVjnJFEDTgtzVB0w8HTI6A8rAh+mOaYgPfKVNEVERESCpARPRA4rrmoAIDs5/uQ77/rEeR0QwR4862/HcrlgwpWw/T0o3x3QqcYVpLOjuIbqBm8oIwzcldZpIiIiIkFQgicihxVXOwleTmoACd7OjyC9n9NT1hVNvAKwsOrJgHYfW5CGtbBOwzRFRESkG1OCJyKHlVQHOETTWtj5MQw4tROi6qDMgc5QzQDXxGuupLl2rwqtiIiISPelBE9EDjvU3IOXcpIevJJtUHMo8gVWpvhbWyZdDWU7nGT0JHqlJpCXFh+5Bc9fneI0ERERkSAowRORw4qrG0iJjyEh1n3iHXd+5LxGusDKcn9ry6hLIC414GIrY/PTI1dJs2y500RERESCEBPpAESk6yiubiQ7kAqauz6BpBzIGRb+oIIRlwRjL4U1/4UL/wDxqSfcfUxBOu9uOkhto5ekuCj+8Vh9CDa8ADveh/Jd4G2AlF7QdzqMuhj6jI90hCIiItJB6sETkcNKqhtOPjwTnB68AaeAMeEPKlgTrwZPLax77qS7js1Pw2dhw76q8McVCdWH4MXb4E9j4OXvwp7lkJgFWYOhvgIW3gUPnA6PfhEOboh0tCIiItIBUfwnahFpr+LqBgblJJ94p4oip9dn5s2dE1Sw+k2H7GFOsZXJ15xw1+ZCK+v2VjBlQGZnRNd5Vj0Jr/4QGmucuYnT50GvUUcn6bWlsPwR+OgvMP9MOPfXMO0b3SORFxEREUA9eCLSgjNE8yQ9eDub17/rwhU0WzIGJl7pDCst2XbCXfukJ5CVHBe5Qivh4G2EF78NC25wErqbPoaL/wx5o49P3JKyYNZ34JuLYdAZ8Mr34PWfgs8XkdBFRESk/ZTgiQgA3iYfZbWNJx+iufMjiE+DvLGdE1goTLgCjMvpxTsBYwxj8tNYuydKlkpoqIYnLodl/3ISt2tfgtzhJz8upRdc8RTMuBEW/Q1e/UFAS02IiIhI5GmIpogAUFrbiLWQe7IiKzs/hn4zwHWSSpud4foA90vrA0PmwMonYPZPTxj72IJ0HvxgOw3eJuJjOvEehwR6MwFqrIFHvwBFi2Hu35xhme3hcsH5vwN3HHz8V+drePr/C22MIiIiEnLqwRMRAIqrnEXOTzhEs6YEijd1neGZ8/0tEJOugqq9sP3dE+42Nj8dr8+yeX910OG1y4z5TgsFbyM8dY2T3H3x4fYnd82MgbN/AeMug7d/CZtfD018IiIiEjZK8EQEcAqswEkWOd/lXzC8qyR47THiQkjMhBUnHqY5tiANgLV7u+k8PJ8PFsyDbW/DxX+FMZcGdz6XCy65F3qPc+bxle8OTZwiIiISFkrwRASAkprmBO8EQzR3fgLueMif1ElRncQyfwtETLzTE7XxZagra3O3/llJpCbEdH6hldJlTgvW+7+DdQvgnF+etGpowGIT4LJHoMkLz92koisiIiJdmBI8EQECHKK562PoO9VJlrqCqf4WqIlXQVODs/B5Gw4XWtnbyYVWXpvqtGCsfwHe/71zn6d+KzRxNcseAuf9BgoXwrKHQ3tuERERCRkleCICwKHqBuJiXKQltFF7qaEa9q2G/qd0bmCh1GeCU/3zJNU0x+ans3FfJd6mbtRTdWgTLLgRCqbARXeHZ+26yV+BwWfCm3dA5d7Qn19ERESCpgRPRAA4UFlPXlo8pq3EoGgx2CYY0I0TPGOc3q29K+DA+jZ3G1uQToPXx7ZDNZ0YXBA89fD0VyE2ES5/1BlSGQ7GwGf/DE0eeOsX4bmGiIiIBEUJnogA/gQv9QSJwc5PnLXk+k7vvKDCYfyXwBVzwl68w4VWusuC52/dAQfXwefug7T88F4raxCcegusfhJ2LwnvtURERKTdlOCJCAAHqxrISztBgrfrE6eSYkJa5wUVDsk5MPx8WP2U0xPVikE5KSTGurtHJc3Nr8On98OMm2D4uZ1zzVnfhZTezgLoKrgiIiLSpSjBExEADlY2kJvaRvEUbyMULYH+3XB5hNZMuhpqDsGWN1p92+0yjM5PY92eTi600l5V+52qlnnj4JxOHDIZn+Jcb+9yWPN0511XRERETkoJnohQ0+ClusHbdg/evpXgre/e8+9aGnoOpOTBskfa3GVsfhrr9lbg89lODKwdrIWXvguNNfDFhzq/sum4Lzk9uu/9T5s9oSIiItL5lOCJCAernDXw8tLaSBJ2fuS8drUevKX+1l7uGJhyndODV7Kt1V3GFKRT09hEYUknFVo5f6nTArXuWdj0Mpz1M8gdEb642uJyweyfQtkOWPVE519fREREWqUET0Q4UFkP0HYP3s5PIHsYpOR2YlQBmOJvHTH1a+Byw5J/tPr22Px0gM5bDy9ritMCUVMMr3zfWRJh5s3hjetEhp/vxPD+H8DbELk4RERE5DAleCJyOMHr1docPJ8Pdi+KnuGZzVJ7w5hLYcWj0FB13NvD8lKIi3F1zUqar/4A6ith7t+cJDVSjHF68Sp2w/J/Ry4OEREROUwJnohwsNLpfenVWg/ewfVQX9H1hmcCzPO3jppxIzRUwqonj3sr1u1iVJ80Vu4uD+IC7fDpPKedzIaXYO0z8JkfQq9R4Y/rZIacBf1mwod/dorxiIiISEQpwRMRDlbVkxDrIi0h5vg3d33ivHbFHrwH/a2j+k51hhh++kCr5f4n9ctgTVEF3qZOWApg24NOO5G6Mnj5u05xk1m3hT+mQBgDp/8/qCyCtf+NdDQiIiI9nhI8EeFApbMGnjHm+Dd3fgyp+ZAxoPMD6wwzboSSLbD9nePemtgvgzpPE5sPVEcgsFa8/jNn/t3cv4E7NtLRHDHsHMgb6/TiaV08ERGRiFKCJyIcqKxvff6dtU4P3oBTnJ6aaDT6c86SCZ/8/bi3JvbLAGBVUXmnhtSqrW/Bykednrs+EyIdzdGMgVnfgeJNsOmVSEcjIiLSoynBExEOVTW0Pv+urBCq9kH/Ljg8M1Ri4mDGDbDtbdi36qi3BmQnkZkUy8pd5ZGJrVlDFbx4G+QMhzN+ENlY2jL6c5A5ED682/nDgIiIiEREWBM8Y8z5xphNxpitxpgftfL+mcaYCmPMSn/7eTjjEZHjWWvb7sE7PP+uCxZYCaVp34D4NGeIYQvGGCb0y+i8QittefuXUFEEl9wLsW0sZRFp7hg49VuwZxkULox0NCIiIj1W2BI8Y4wb+BtwATAauMIYM7qVXRdaayf62y/DFY+ItK6y3ktNYxP56YnHv7nzY0jIgNwuUK0xnBLSnXXx1j933MLnE/tlsPlgFdUN3sjEtmsRLH7Q6WXsPyMyMQRq4lWQ3AsW3h3pSERERHqscPbgTQe2Wmu3W2sbgSeBuWG8noh0wN7yOgDyM1pJ8Ao/dHrvXF10NPdkfwuFmTeDKxY+/utRmyf2y8BaWB3ueXiZk53WkqceXrgV0vvBWbeH9/qhEJsAM2+E7e/CvtWRjkZERKRHCudvbQXA7hafF/m3HesUY8wqY8yrxpgxrZ3IGDPPGLPUGLP00KFD4YhVpMc6kuAdM/SvfDeU7YBBZ0QgqgAt87dQSM2DSVfBysede/drLrQS9mGaFyxzWksf/BGKN8PFf4L4lPBeP1Smfg1ik+HjeyIdiYiISI8UzgSvtZJ7x868Xw4MsNZOAO4BnmvtRNba+dbaqdbaqbm5uaGNUqSH21tRD7TSg9c8j2rg6Z0cUQTN+q7z+v7vD2/KSIpjUE5y5xda2b8GPvozTLgChp7dudcORmImTLnOWYy9fFekoxEREelxwpngFQH9WnzeF9jbcgdrbaW1ttr/8StArDEmJ4wxicgx9pbXEes25KYcU2Rlx0JIzIJerU2djVIZ/ZweqJWPHzUXb1K/DJbvKsN2VnXIJi88f4uTLJ332865ZijNvMl5XXRfZOMQERHpgcKZ4C0BhhljBhlj4oAvAy+03MEY09v4V1Y2xkz3x1MSxphE5Bh7y+vonZ6Ay9Wi091a2PEBDDq9686/A2ecQKiX5zv9/0FMPLx7JLGaPiiL4upGth2qCfHFWnjcOA3goz/BvpVwwR8gKSt81wyXjH4w7ouw7BGoK4t0NCIiIj1K2H5zs9Z6gVuA14ENwH+steuMMTcaY2707/ZFYK0xZhXwV+DLttP+RC4i4CR4x1XQLNsBlUU9a3hms5ReTsXKtc8cLhQyY3A2AJ/u6IS/P+1dAe/9DsZ+AcZ+PvzXC5dTbwVPDSx5KNKRiIiI9Chh/dO8tfYVa+1wa+0Qa+1v/Nvut9be7//4XmvtGGvtBGvtTGvtx+GMR0SOt7e8noJj59/t+MB5HfSZzg+oKzjt207P2as/AGsZmJ1Er9R4Pt1eGv5rPzvPWWrgwrvCf61w6j0OhsyBTx9wqoGKiIhIp+jCY69EJNy8TT72V9YfX2Blx0JIyYOcYZEJLNISM2HOz52F3tc8jTGGGYOz+XRHSfjn4RVvhs/9rXsOzTzWad+CmoOw+qlIRyIiItJjKMET6cEOVjXQ5LNHJ3jWOhU0B54OJtQT3LqRSddAn4nwxu3QUMWMQVkcqGxgV2lteK8740YYclZ4r9FZBn0G+kxwlkzw+SIdjYiISI+gBE+kB9tX0coaeMWbofpA117/rjO43M4wyer98M5vmDHI6VELyzDNlssJnH1n6M8fKcbAqd+Cki2w+dVIRyMiItIjKMET6cH2lLeyBt7h+Xc9sMDKsfpNg2nXw6f3MbR6GdnJcSwKdaEVbwP85ytHPo9NbHvf7mj05yCjP3z0l0hHIiIi0iMowRPpwfaUOT14fdJb9OAVLoT0fpA5KEJRtcMD/hZO5/wSsodinr+ZM/vHhrYHz1p4+btO5cx+N8P0cN9MBLhj4JRbYPensOvTSEcjIiIS9ZTgifRgu0pryEqOIzUh1tnQ5IXt7ztzp7rD/Lt5/hZOcUnw+flQtZ9bav/GnvJadpaEaD28D/4IKx6FM74Pp/8Nhob7ZiJk0tVO4ZqP/xrpSERERKKeEjyRHmxnSS0DspOObNi7HOrLYeiciMXUJRVMgbN+yqADb3Cj+0Xe23Qo+HOufBze/Q2M/zLM/mnw5+vK4pKdoa4bX4biLZGORkREJKopwRPpwXaW1DIgq0WCt/UtMC4YfGbEYmqX+f7WGWZ9F8Z+gR/EPkXN8qeDO9fqp+H5bzo9pZfc4/SWbp3vtGg1fR7ExDsVNUVERCRslOCJ9FAN3ib2VtQxIDv5yMatbzu9Vd1lDbYb/K0zGANz/8bu5HHMO/RbGtc837HzLPkHLJgHA06DK56AmDhn++IbnBatUnJh4pWw6gmoOhDpaERERKKWEjyRHqqorA5rOTJEs7YU9iyDoWdHNrCuLDaRnec/wmo7mNhnr4NP/u4USgmEpw5e+i68/P9g2Llw5X+coYs9ySm3gM+ruXgiIiJhpARPpIdqLhRyuAdv+7uAhSGaf3ci00YM4Ou+n7Ex7XR4/cfw5JVQUdT2AdbCtnfh/tNh6UNw6q3w5ced4i09TfYQmHCF04tZuS/S0YiIiEQlJXgiPdTOklqgRQ/e1rchIQMKJkcuqG4gMc7NtOH9+GrtLfjO/pWTvP11Eiy4CTa+AuW7oaYE9q+BxQ/Cw+fB/33OWe/umufg3F87i6j3VGd83+nF+/DuSEciIiISlWIiHYCIRMbOklpS4mPITo7z9zK9A0Nm9+zkI0AXje/DG+sPsLzv1Uz95lyncMjKx2HV48fvnDMCLvgDTLnOKTLS02UNgolXwbJ/wanfgox+kY5IREQkqijBE+mhdpbU0D8rCWOMs9B21T4Yek6kw+oWzhrZi7gYF6+s2c/Ui0fDRXc5PXN7V0DxJme+XUoe5I2F3OGRDrfrOeP7TrGV938Hc/8W6WhERESiioZoivRQO0tqGZjjH5658RVneYTh50c2qG4iNSGWM4bl8uraffh8/iIrsQkw4BSnp27mTTD280ru2pLRz1k2YcVjsG91pKMRERGJKkrwRHogT5OP3WW1RwqsbHwZ+p8KydmRDay9rL9FwMUT+rCvop5PtpeE5oRXWqf1FGd8HxIz4bUfB16JVERERE5KCZ5ID7SzpAZPk2VYrxQo3QEH18HICyMdVrdy3pjepCfG8uSS3ZEOpXtKzICzfgo7P4SNL0U6GhERkaihBE+kB9p8oBqA4XmpsOkVZ+MIJXjtkRDr5tJJBby+dj9lNY2RDqd7mnwd5I6C138CjTWRjkZERCQqKMET6YE2H6jCGBiSm+LMv+s1xqlu2N1M8bcIuXxaPxqbfDyz/ATr4AXq1SlO60ncMXDR/0L5Lnj7V5GORkREJCoowRPpgbYcqKZ/VhKJnnLY9TGMvCjSIXXMcn+LkFF90pg2MJN/flSIp8kX3MnKljutpxl4mlNw5dP7YdeiSEcjIiLS7SnBE+mBNh+oYlivVNj0Mlif5t8F4aYzh7CnvI4XVu6NdCjd15w7nMqaz38TGmsjHY2IiEi3pgRPpIdp9PrYUVzD8LwUWPM0ZA2BPhMjHVa3NXtEL0bkpXL/+9to8qkaZIfEp8Al90LJNnjl+5GORkREpFtTgifSwxSW1OD1Wcan1cKOhTDuMjAm0mF1W8YYbp0zlC0Hq3l6qSpqdtjgzzhLJ6x8FJb/O9LRiIiIdFtK8ER6mM0HqgCYUPEOYJ0ET4Jy0bg+TB2QyV1vbKKq3hPpcLqvM38Eg2fDS9+F7e9HOhoREZFuSQmeSA+zYV8lbpchb+cLkD8JcoZGOqRuzxjDHRePoaSmkT+8tinS4XRfLjdc9i/IHgpPXQ17emDRGRERkSApwRPpYVYXVTAnpxzX/lXdv/fuen/rAsb1Tedrpw3i/xbt5O0NB9p/giHXO62nS8yAq//rvP57LuxeHOmIREREuhUleCI9iLWWVbvLuTxhEWBg7BciHVJw5vtbF/GD80cwsncq3//vanaWtHPh7hnznSaQ3heuewWSc+CRi2H105GOSEREpNtQgifSgxSW1FJT38DMytdgyGxI7R3pkKJKfIybv181GZ+1XPvwYkqqGyIdUveV0Q++/iYUTIFnv+EsoVBXHumoREREujwleCI9yOqics50rSS5/gBM+WqkwwneMn/rQgbnpvDQtVPZV1HPlx74hD3ldYEdWLrMaXJEcg585Xk4/f/Byifg3qnw6QNaK09EROQElOCJ9CCrdldwdey72JQ8GHFBpMMJ3lR/62KmDMji31+bzsGqBi7920d8ur3k5Ae9NtVpcjR3LMz5OVz/NuSOhFd/AP87Al76Duz4ADz1kY5QRESkS4mJdAAi0nn27tzEGWYlZtJ3nF+cJWxmDM7mvzeeyo2PLuOKBxdx/RmD+facYSTF6cduh+RPgmtfhF2fwLJHYOXjsPRhcMdD/kTIGe607KGQ0d9pCWmRjlpERKTT6TcNkR6iwdvE9INPY1wmOoZndgMjeqfy4q2z+NWL63ng/e28tGoft392FOeN6Y3R4vLtZwwMONVpF/7RSfa2vw97V8Dm12DF/x29f2LmkWQvY8CRj7MGQ/YwcHXhQSxV+2HXIqeKaMkWKCuEujJo8oBxQUoepPWB3uOh7zQYeJpzvyIi0uMZa22kY2iXqVOn2qVLl0Y6DJFuZ/GGQkY9OZOaAXPo/bXHIh1OaDTnSN3gx9iSwlJ+tmAtmw5UMX1QFj+7aBTj+2Yc2eFx/81c2Q1upquqK4OS7VCxC8p3QdlO57W5eVvMh4xPcwq49J8JQ+ZAwWRnHb5IqimGtc/CmqehyL88REwC5AyDzIGQnAuuWPB5ofoAVOyGA+vB5wFXDAw6A8Z83qmOG5cU0VsREZHwMsYss9a2OrdDCZ5ID/HuP3/O7J1/oea6t0geOC3S4YRGN0rwALxNPp5aups/vbmZ4upGLp1UwPfPG0F+RqISvHCz1kmgyndB8SYoWgK7l8DBdWB9kJgFQ86CYefCsHMgKatz4mqohk2vwOr/wLZ3wDZB3lgY+3kYdCb0HgcxcW0f76mHfSth06uw4QUo3Q4J6TDxKjjlm86SEyIiEnWU4In0dJ46Sv9nFHvc/Rj304WRjiZ0ulmC16yq3sN9723jHx/uwADXnz6Y75WMdN5Ugte5akth+7uw5U2n1RaDcTvDQEdcCCMvdHrPQqnJ4yRzq//jJHeeWkjvB+O+COO+BHmjO3Zea51hnUsehPXPAwYmXQ2zvgOZA0J6CyIiEllK8ER6uPqF95Dw9s94csz9fPmyKyIdTuh0oQTvzDPP5K677mLq1MArYe4pr+OPr23kuZV7KRz/WQDeznubP/3pbl566aVwhSpt8flg73LY+LLTI3Zog7O91+gjyV6fSR2bu+dtdKp+rl8AG16C+nJnztyYS52krt+M0M4JLN8FH/7ZmZdofTD1a/CZHzpLT4iISLenBE+kJ2usoeHu8SytycN13YucMiQ70hGFTvOycVMiGgXQsQSv2crd5Tzx6rOs21fF/oOW2PWv8Mm7b4QhSmmX0u1OorfxFdj1sZMopfSGftOdOXv5kyFrEKT2OboqrbfRmSN3aCPsXwM7P3aap8aZ+zfiAiexGzLnxMMvQ6FiD3zwR1j+b4hNglm3wcybNUdPRKSbO1GCpyqaItHuo78SX1/M/a5v8Y8BGZGOJrT8iV1hYSHnn38+s2bNYtGiRUyYMIGvfvWr3HHHHRw8eJDHHnuMMWPGcOutt7JmzRq8Xi933nknc+fOpbCwkGuuuYaamhoA7r33Xk499VTee+897rzzTnJycli7di1Tpkzh0UcfDaj65RNPPMFvf/tbrLVcdNFF/P73vwfgpptuYsmSJdTV1fHFL36RX/ziFwDsX7eI5+/+A+6kNMqT+lK2v5yvPLyYn144ihG9U8PztZOTyxrszGM75ZvOUM4tbzhtzzJnvtthBuKSnSTP1wQNlUefJ2c4TLzCSeiGnAWxCZ13D+kFcPGfYeZN8Nad8M6vYMlDcNbPYMKXI19YRkREQk49eCLRrHw39t6pvOadzJuj/oe7L58Y6YjCorCwkKFDh7JixQrGjBnDtGnTmDBhAg899BAvvPAC//znPxk9ejSjR4/m6quvpry8nOnTp7NixQqMMbhcLhISEtiyZQtXXHEFS5cu5b333mPu3LmsW7eO/Px8TjvtNP74xz8ya9asVmNo7sHLz89n5syZLFu2jMzMTM4991y+9a1v8bnPfY7S0lKysrJoampizpw5/PWvf2X48OEMGzaMd955h6FDh3LZl77E5j0lcO4PqWls4tpTBvKdc4aRmqB1C7uU2lLYt8oZClm5xymW4vM48/eSsp2hkLkjnMXZO6tgSyAKP4I3b3eS1LyxMOcOp6iMlu0QEelW1IMn0lO9/hN81vKr+i/z6wl9Ih1N6M3zv/4EBg0axLhx4wAYM2YMc+bMwRjDuHHjKCwspKioiBdeeIG77roLgPr6enbt2kV+fj633HILK1euxO12s3nz5sOnnz59On37OlUIJ06cSGFhYZsJXrMlS5Zw5plnkpubC8BVV13FBx98wOc+9zn+85//MH/+fLxeL/v27WP9+vX4fD4GDRrEsNI/wmL4yjXXMH/+fP7v+7P54xub+OfHO3hx9V5+dtEoLpmQr/XzuoqkLBgyO9JRtN/A0+Abb8O6Z+HtX8Ljl0H/U+HsO5wlI0REpNtTgicSrdYtgA0v8Gru9dT4+jBraG6kIwq9B/2vP4H4+PjDm10u1+HPXS4XXq8Xt9vNM888w4gRI446xZ133kleXh6rVq3C5/ORkHBk+FzLc7rdbrxe70lDamtUxI4dO7jrrrtYsmQJmZmZXHfdddTX1wM4Sds2/82kOMVWMpPj+O2l47h8aj9uf34t335yJY9/uotffW4sw/PCM2yzusHLmqIKNh+oYkdxDYeqGyiraaTJZ3EZQ2pCDLmp8RRkJjKqdxqj+qSRlxavpLO7McZZK2/kxbDi3/D+H+Dh82DYeTDndmdpBhER6baU4IlEo+qD8PL38PaewI/3zubC8b2Jiwlhhb5u6LzzzuOee+7hnnvuwRjDihUrmDRpEhUVFfTt2xeXy8UjjzxCU1NTUNeZMWMG3/72tykuLiYzM5MnnniCW2+9lcrKSpKTk0lPT+fAgQO8+uqrnHnmmYwcOZIdO3aw7QAMyXPm77U0oV8GC24+jSeX7OIPr23igr8s5POTCvjWnGH0ywquUEaj18eSwlI+2HKIRdtLWbungiafk6CmxjvJXGZyHDEug9fnY2dJLUt3llFa03j4HLmp8ZwyOJvThmYza1guBRmJQcUknSgmDqZ9AyZcCYsfgA//BPefDqPnOksr5E+MdIQiItIBSvBEok2TF/77NWis5pl+P6Wq0Ms1p2gNrNtvv53bbruN8ePHY61l4MCBvPTSS9x888184Qtf4Omnn2b27NkkJycHdZ0+ffrwP//zP8yePRtrLRdeeCFz584FYNKkSYwZM4bBgwdz2mmnAZCQkMD8+fO56GsXkJMKsy4dwNq1a486p9tluGrGAC4Y24d739nKo5/uZMGKPZw/tjdXzxzAjEFZAfeiHais571NB3ln40E+3FJMTWMTsW7DxH4Z3HzmEKYMyGRUnzR6pbbdM1dR52Hjvko27Ktk+a5yPt5Wwgur9gIwJj+N88b05rwxvRmel6Leve4gLslJ6KZ8FT6+BxbPh/XPOQVhZn0HBp6uOXoiIt2IiqyIRJs3boeP/4r3kr8z67XeDMpJ5ol5UTq3pgutgxe0x/03E8BC5/sr6nlw4XaeXrqbynovvdMSOH1YDtMGZjEgO4k+6YkYA54mH/sq6tlZUsuaPeUs31nOpgNVAPRJT2D2yF7MHtGL04ZmkxTX8b/3WWvZcrCadzYe5PV1+1mxqxyAgdlJnDemN+eO6c2kfhm4XEoSuoX6Clj6MHzyd6g5CHnjYNrXnPX64lMiHZ2IiKB18ER6jk8fgFd/AFO/zn96f4cf/Hc1D183lbNG5kU6svDooQles7rGJl5du4+3Nxxk4ZZDVNa3PUcwLSGGSf0zmTE4i7NG9mJEXmrYetcOVNbz5voDvL5uP59sK8Hrs+SmxnPO6DzOHZ3HqUNyevyQ4W7BUw+rnnCWVTiwBuJSYcLlzqLpeWMiHZ2ISI+mBE+kJ1j6T3jpOzDyIiou/gdz/vQhBZlJLLjp1OjtOYlAgnfppZeyY8eOo7b9/ve/57zzzgvuxB1I8FryNvkoKqtjZ2ktByud4i1ul6F3egL9MpMoyEiMyL+DijoP721yevbe23SI2sYmUuJjmDEoi1OGZDNzcDbD81I7lPD5fJay2kYOVDZwsKqeg5UNVDd48TT58DT5cLtcJMa6SIqLITHOTVpiLOn+lpEYS1piLO5o/d4IJWuhaImT6K1bAE0NTq/e+Mtg7BedtfYkcpqX7Di0ESr3QtV+qNqHrS3B11hLU2MdviYvTcTgMbE0uROoj8uiKSkXd1ofkvNHkt5vFCZnhLO8h4bjinQLSvBEopnPBwvvgnd/41TB+9Ij/PyVbTy6aCcv3DKLsQXpkY4wfPwLnbMsolGExqv+m7mga93Me++9x1133cVLL73ECy+8wPr16/nRj37UrnMMHDiQpUuXkpKeyUdbi3lrw0EWbS9hR7GzuHyMyzA4N5lhean0So0nOzmO9KQ4wBn+2ej1UVbbSEl1I8XVDRyqbuRgZT2Hqhrw+oL7Pyw1Poa0xFgyklokf0lO8peWEEt8jMvf3MTHOh/H+T/PSo6jV2o8mUlx0ftHlGPVlMCap2HNf5y19DAwcBaM+RyMuBDS8iMdYfSy1kne9q1y2v7VzmvF7sO7+FyxVMXmsN9msacxicqmWOptHE24icVLnPGQTAPZpoIcKsgz5cQbz+Hj62IzaOo9ieTB0zEFU6FgspP0iUiXowRPJFrVlsLzt8Cml2H85TD3b7y07hC3PL6C604dyJ2XaBiVBKdlgtdRzQleTs7Rvyjur6hncWEpm/ZXsml/FVsPVlNc3Uh1w5GhptbXhHG5cbsMWclxZCfHkZsaT25qPHlpCfRq8dorNYG0xBhi3S5i3S581lLb2ESdp4naBi+V9R4q6jyU1zqvzR9XNn9cd2R7Ra2HxiZfQPcX6zYMyE5mWK8UhvVKYXzfDKYMyCQzOa7DX7NuoWQbrPmvk/CVbHG25U+CERfBiAucYZzqDeoYa6FsB+xbfXQyV3PIeRuDN2Mw+5OHs6ZpIO9V9uHt0lxKSMNlDKP6pDG+bwZDcpMZkptCv6xEMpPiSI535to2NvkoqW5kf1kN+3dtpnTXBur2byS3ZisTXNsY7irC5R8aYTMHYfrNgH7Tod8M6DUKXO6IfWlExKEETyTaNHmdv6C/cTvUl8O5v4YZN7K4sIxrHvqUcQXpPHb9DOJj9J9wd1NYWMj555/PrFmzWLRoERMmTOCrX/0qd9xxBwcPHuSxxx5jzJgx3HrrraxZswav18udd97J3LlzKSws5JprrqGmxukZu/feezn11FN57733uPPOO8nJyWHt2rVMmTKFRx99tM05eK+99hq33XYbOTk5TJ48me3bt/PSSy/xr3/9i6VLl3Lvvffy9NNP84tf/AK32016ejoffPABTU1N/PCHP+T111/HGMP111/PrbfeysCBA7n22mt58cUX8Xg8PP3004wcOZLFixdz2223UVdXR2JiIv/85z8ZMWIEDz70MC++9BL19Q3U1dbwn2ee51s3Xc+mTRsZNWoUhYWF/O1vf2Pq1Km88cYb3HHHHTQ0NDBkyBD++c9/kpISfCEQay0NXh+NTT4aPD4avE00en00NDdPE6U1jRyorGdfZT3bD9Ww9WA1O0tqaO5UHJKbzNQBWcwYnMXMwdnkR+sSEtbCoU2w6RWnFS1xtif3gkFnwODPwKDPQKaq+bbKUw/Fm+DAOti/1p/QrYGGCud9Vww2dySVGaPZ5BrMx9X5vHggm22VzvdvakIMUwZkMnVAJpP7ZzKhX8bhRK699pTXsXDzIRau20H5tqWMtVs4NX47U1ybSfGWOTvFpULfKU6y1286FEyFxIwQfCFEpD1OlOBpmQSR7qSx1ilf/sFdULoNCqbAxc9j88bw7PI9/PjZNfTNTOT+a6YouevGtm7dytNPP838+fOZNm0ajz/+OB9++CEvvPACv/3tbxk9ejRnnXUWDz/8MOXl5UyfPp2zzz6bXr168eabb5KQkMCWLVu44ooraP6D2IoVK1i3bh35+fmcdtppfPTRR8yaNeu4a9fX13P99dfzzjvvMHToUC6//PJWY/zlL3/J66+/TkFBAeXl5QDMnz+fHTt2sGLFCmJiYigtLT28f05ODsuXL+fvf/87d911F//4xz8YOXIkH3zwATExMbz11lv85Cc/4ZlnniHW7WL5ksWsXr2arKws7rrrLrKyMlm9ejVr165l4sSJABQXF/PrX/+at956i+TkZH7/+99z99138/Of/zzoZ2CMISHWTUKsGxICP66usYnVReUs3VnGsp1lvLp2H08tdYbQ9ctKZOYgZ97hjMFZ9M0Mbh3DLsMY6DXSaad/F6oOwJY3YMf7sOMDWPtfZ7+0vs6Qv4IpTsufCPGpEQ2901jr9L6VFTqtdAcc2gAH1kPJVrD+9TdjErB5Y6kaNpftMUNY3tiPd0pzWLmvnuqdTs92r9R4pg3K4isDs5g+KIsReakhGyJckJHIl6f358vT+1NZfypvrT/Av1fv4/oth8jz7Wd20g4uTN3FmNKNpOz4I8b6AOP06vWd5k/6ZkD2EPXeikRQWBM8Y8z5wF8AN/APa+3vjnnf+N+/EKgFrrPWLg9nTCLdTk0JFC6Eza/DhhehscopcPCl/8OOvIhluyr4y8OLWbilmOmDsnjg6inRPzSsWTRW0Tx1B4MGDWLcuHEAjBkzhjlz5mCMYdy4cRQWFlJUVMQLL7zAXXfdBThJ2a5du8jPz+eWW25h5cqVuN1uNm/efPj006dPp2/fvgBMnDiRwsLCVhO8jRs3MmjQIIYNGwbA1Vdfzfz584/b77TTTuO6667jS1/6Ep///OcBeOutt7jxxhuJiXH+a8nKyjq8f/M+U6ZM4dlnnwWgoqKCa6+9li1btmCMweM5MhfonHPOOXz8hx9+yLe//W0Axo4dy/jx4wFYtGgR69evP7ymYGNjI6ecckqAX/DwSIxzM2NwNjMGZwPQ5LNs3F/Jou2lfLq9hDfWH+DpZUUA9M1MZMagbGYOzmLKgEwGZCdHR9GX1DyYfI3TrHWKf2x/H4oWO/P2Nrzg39FARj/IGe5vwyBrMKTmQ1qfrpv8WQueOmishoYqaKiEBv/HdWXO0hLVh/yv/la+Ezy1R53GlzGAuowRHMidwxbTjxUN+Swqz2Tz7jpqtzkJX1yMi9F9Yvj85ALG981g2sBM+mcldcr6kmkJsXx+cl8+P7kvFXUe3tl4gDfXH+Drmw5R09hEblwjl/c5yGeSdjCicT2p65/DLH/EOTgxy0nmc0ZA7vAjzzgpW4mfSCcIW4JnjHEDfwPOAYqAJcaYF6y161vsdgEwzN9mAPf5X0V6hiav859+Q+WRXwRqDvr/urvJ+Qtv6XYAbHwq9cM/y97+F7PSNY41W6v44NWFbD9UQ1pCDHdePJqrZw4gxq3y891dfHz84Y9dLtfhz10uF16vF7fbzTPPPMOIESOOOu7OO+8kLy+PVatW4fP5SEhIaPWcbrcbr7ftJRUC+eXx/vvv59NPP+Xll19m4sSJrFy5Emttm8c2X7/ltW+//XZmz57NggULKCws5Mwzzzy8f8sF59uaSmCt5ZxzzuGJJ544abyR4nYZxuSnMyY/na/PGoTPZ9l0oIpF20tYtL2EdzYe4JnlTsIXF+NiSG4Kw/NS6J+VRO/0BHqnJZCXlkB6YiypCTEkxztzDLsN4+/d6TUKuNHZVlMCe5fD3hXOz7nizbDz4+MSIOJSILUPJGY6yV58iv81DWITwRXjb+4jH2Oc3jBfk//Vd+Rzn/fIx02N4G1o5bUBvI3O58dt879664/0uLXBF5OIJyGHuvhsqmP6UJI3mf2uPLZ5c1lbl8WqylT27Af2Hzmmd1oCw/ISuHxaDiPyUhnfN4NheSld4nmnJ8Zy6aS+XDqpL/WeJj7ZVsKbGw7wyrZM7t3ZFzidGJfljMwy5qQUMt63kYIDW0jf8SHupvrD57GxyZi0fKfyalqB83yTspxnnJgJCRnOcM+4ZIhJhNgE59WtAWci7RHO75jpwFZr7XYAY8yTwFygZYI3F/i3df73XmSMyTDG9LHW7gtjXKG37F+w4tHDnzZ4fRQW19Bat4JpR1dD2/u2ct52zqVs/dytn6O1fU2bl2vPPbd1vfbsG9zXoq3Y2vW1P8G5j93bYInFS7xtIIFGYmn9l2wvbnabfHaYvmxyz+QDzyiWVAzAuzQGlgKsIT7GxfRBWVx/+mAumZDf4TkX0v2cd9553HPPPdxzzz0YY1ixYgWTJk2ioqKCvn374nK5eOSRR2hqOvEvoa0ZOXIkO3bsYNu2bQwZMqTN5Gnbtm3MmDGDGTNm8OKLL7J7927OPfdc7r//fs4888zDQzRb9uIdq6KigoICp8T+v/71rzb3mzVrFv/5z3+YPXs269evZ82aNQDMnDmTb37zm2zdupWhQ4dSW1tLUVERw4cPb/d9dxaXyymAMapPGl89zUn4Nh+sYnVRBVsOVLH5QDVLC8t4cdVe2ioQmhDrIjHWTYzbRazL4HYbYl0uYtyGGJcL1zH5gGnxU6pl/n3cz64Wb7b2c+2ocFr8nLWtbz78E/CobYc/TsByCuDvcU1qItuWkN+0nyxbQo6vlGxfCdmVJSRX1JBkd5Nk60iytSTZWhJowE1gRXCaeXHjw0UTLjzE4jGxeIihkdjDnzs/lf3bTBKNpOMhlkZi/K+xNLpiqTFJ1JJIhS+BsqYEyrxxVPgSqCSZYptOLQlQffT142JcFGQkUpCRyKyCRPpmJlKQmcignGSG9EohLSG2XfcTKQmxbmaP7MXskb0AOFhVz9LCMtbvrWTj/iruP1DA3vIpNPksBh/5lDDEtZchZi8F3mL6NpaRX7KXPFaTQ3lAz9GLm0YTRyNxNBnnOTpnd+HDjTXm8LNt/tzitJM7+T6B/FZx8mup57KrMAaG57VjhEBiJlz1dPgCCoNw/kZYAOxu8XkRx/fOtbZPAXBUgmeMmQfMA+jfv3/IAw2aO/6ooSTW5aPh8Ff2+G/o1n4IGNr4AdLGX8Nb/0HSdqrR2qb2pFeB3oeza+CxtflDsz3naMe+bb/X2v21HkabX/tWL3n8Rq+JxWMSaHTF0+hKwOOKp8GVRFVMFlUxWVS7s6iMzabJOP/ZuwyMSYzlVH/59tzUeIb2SmVAdlKX+MuudL7bb7+d2267jfHjx2OtZeDAgbz00kvcfPPNfOELX+Dpp59m9uzZR/WCBSohIYH58+dz0UUXkZOTw6xZs1i7du1x+33/+99ny5YtWGuZM2cOEyZMYOzYsWzevJnx48cTGxvL9ddfzy233NLmtX7wgx9w7bXXcvfdd3PWWWe1ud/NN9/Mtddey/jx45k0aRLjx48nPT2d3Nxc/vWvf3HFFVfQ0NAAwK9//esuneAdy+UyjOydxsjeaUdt9zb5KK5uZH9lPQcq66ms81Dd4KWq3kt1g5e6xia8Ph+eJkuTz+Jp8uFtsnh9vmOSrBYft5GUOe+1fYxpI/FrK1lsbf+jf5a2lXCmUM4AyoEdR203x+9rnKANPmKs/1d724QxFosLn3FhcftfXYerPnY0/paftEyY42Nd5MW6GRjrJjHOTXyMi8Q4N0lxbjKT4shKjjv8mhTn7pShlZ2tV2oCF47rw4Xj+hze1uSzFFc3sLe8zv/v10tVg5eqeg9L6r00eJucf6/eJmK81cR5KkjwVJLgrSC5qYo4W0+sbSTWNhDnf431NRBnG3Dhw1h7OJ0z1ofB4rL+z7G4rA8TQOIY2B/dT7xPYE80GuYRRA+XoX1DwOOCL9zV2cJWRdMYcxlwnrX2G/7PrwGmW2tvbbHPy8D/WGs/9H/+NvADa22bC0GpiqaIHBaNc/A6uNB5NGtqasLj8ZCQkMC2bduYM2cOmzdvJi6uh8w1FREROUakqmgWAf1afN4X2NuBfUREpAerra1l9uzZeDwerLXcd999Su5ERETaEM4EbwkwzBgzCNgDfBm48ph9XgBu8c/PmwFUdLv5dyIi3dSll17Kjh07jtr2+9//nvPOOy9CEbUuNTUVjdwQEREJTNgSPGut1xhzC/A6zjIJD1tr1xljbvS/fz/wCs4SCVtxlkn4arjiEZEo9ECkAwih6Z1/MwsWLOj0a4qIiEh4hW0OXrhoDp6IiIiIiPRkJ5qDpxJ8IiIiIiIiUUIJnoh0X/P9LRpsne80ERERkSBoZWQR6b5u8L/Oi2gUobHYfzNDo+FmREREJFLUgyciIiIiIhIllOCJiIiIiIhECSV4IiIiIiIiUUIJnoiIiIiISJRQgiciIiIiIhIllOCJiIiIiIhECWOtjXQM7WKMOQTsjHQccpQcoDjSQUhE6Nn3XHr2PZeefc+lZ99z6dl3PQOstbmtvdHtEjzpeowxS621UyMdh3Q+PfueS8++59Kz77n07HsuPfvuRUM0RUREREREooQSPBERERERkSihBE9CYX6kA5CI0bPvufTsey49+55Lz77n0rPvRjQHT0REREREJEqoB09ERERERCRKKMETAIwxlxlj1hljfMaYqce892NjzFZjzCZjzHkttk8xxqzxv/dXY4zxb483xjzl3/6pMWZgi2OuNcZs8bdrW2wf5N93i//YuE64bQmCMeZ8/7+JrcaYH0U6HgmMMeZhY8xBY8zaFtuyjDFv+r//3jTGZLZ4L+zf/9I5jDH9jDHvGmM2+H/ef9u/Xc8/yhljEowxi40xq/zP/hf+7Xr2PYQxxm2MWWGMecn/uZ59NLPWqqkBjAJGAO8BU1tsHw2sAuKBQcA2wO1/bzFwCmCAV4EL/NtvBu73f/xl4Cn/x1nAdv9rpv/jTP97/wG+7P/4fuCmSH9N1E7478Xt/7cwGIjz/xsZHem41AJ6dmcAk4G1Lbb9AfiR/+MfAb/3f9wp3/9qnfbs+wCT/R+nApv9z1jPP8qb/zml+D+OBT4FZurZ95wGfBd4HHjJ/7mefRQ39eAJANbaDdbaTa28NRd40lrbYK3dAWwFphtj+gBp1tpPrPNd/G/gcy2OecT/8X+BOf6/8pwHvGmtLbXWlgFvAuf73zvLvy/+Y5vPJV3TdGCrtXa7tbYReBLnuUsXZ639ACg9ZnPL79mW339h//4P9f1J26y1+6y1y/0fVwEbgAL0/KOedVT7P431N4uefY9gjOkLXAT8o8VmPfsopgRPTqYA2N3i8yL/tgL/x8duP+oYa60XqACyT3CubKDcv++x55Kuqa1nKd1TnrV2HzhJANDLv70zvv8lAvxDqCbh9OTo+fcA/iF6K4GDOL9069n3HH8GfgD4WmzTs49iMZEOQDqPMeYtoHcrb/3UWvt8W4e1ss2eYHtHjjnRuaRr0jPrGTrj+186mTEmBXgGuM1aW+mfRtPqrq1s0/Pvpqy1TcBEY0wGsMAYM/YEu+vZRwljzGeBg9baZcaYMwM5pJVtevbdjHrwehBr7dnW2rGttLaSO3D+2tKvxed9gb3+7X1b2X7UMcaYGCAdZ0hYW+cqBjL8+x57Luma2nqW0j0d8A+/wf960L+9M77/pRMZY2JxkrvHrLXP+jfr+fcg1tpynPn256Nn3xOcBlxijCnEmU5xljHmUfTso5oSPDmZF4Av+yskDQKGAYv93flVxpiZ/nHWXwGeb3FMc6WkLwLv+Mdrvw6ca4zJ9FdrOhd43f/eu/598R97oqRTIm8JMMw41U/jcCZVvxDhmKTjWn7Ptvz+C/v3f7hvTI7wP6uHgA3W2rtbvKXnH+WMMbn+njuMMYnA2cBG9OyjnrX2x9bavtbagTj/V79jrb0aPfvoFqnqLmpdqwGX4vylpQE4gJN4Nb/3U5wqSpvwV0zyb58KrPW/dy9g/NsTgKdxJuYuBga3OOZr/u1bga+22D7Yv+9W/7Hxkf6aqJ3038yFOFX4tuEM8414TGoBPbcngH2Ax/89/3WcuRJvA1v8r1kt9g/7979apz37WTjDo1YDK/3tQj3/6G/AeGCF/9mvBX7u365n34MacCZHqmjq2Udxa34wIiIiIiIi0s1piKaIiIiIiEiUUIInIiIiIiISJZTgiYiIiIiIRAkleCIiIiIiIlFCCZ6IiIiIiEiUUIInIiIiIiISJZTgiYiIiIiIRAkleCIiIiIiIlHi/wNZKvbOsEiY9AAAAABJRU5ErkJggg==",
            "text/plain": [
              "<Figure size 1080x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1080x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1008x432 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "load_mean = feature_df['load'].mean()\n",
        "discharge_mean = feature_df['discharge'].mean()\n",
        "feature_df.hist(['load', 'discharge'], bins=20, figsize=(15,6), edgecolor='k')\n",
        "plt.subplots_adjust(wspace= 0.1)\n",
        "feature_df[['load','discharge']].plot.density(bw_method = 0.2, figsize = (15,6))\n",
        "plt.axvline(x=load_mean, color = 'magenta', linestyle='dashed', linewidth = 2)\n",
        "plt.axvline(x=discharge_mean, color = 'orange', linestyle='dashed', linewidth = 2)\n",
        "plt.text(load_mean, y= 0.1e-5, s= 'mean_load')\n",
        "plt.text(discharge_mean, y= 0, s= 'mean_discharge')\n",
        "plt.subplots_adjust(wspace= 0.0001)\n",
        "plt.show()\n",
        "feature_df.boxplot(['vesseldwt','load','discharge'], vert = False, figsize=(15,6))\n",
        "feature_df.boxplot(['load','discharge'], by='vesseltype', figsize=(14,6))\n",
        "plt.subplots_adjust(wspace= 0.1)\n",
        "\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jn6Y8TBCM_Vl"
      },
      "source": [
        "#### FEATURE ENCODING:\n",
        "One-hot encoding the categorical variables, and returning separate load/discharge dataset. Our focus at this stage is predicting discharge."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "id": "GvYMsXrJM_Vl",
        "outputId": "c1131006-2315-44a0-9dab-bbddeaa94403"
      },
      "outputs": [
        {
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>vesseldwt</th>\n",
              "      <th>n_stevs</th>\n",
              "      <th>process_time</th>\n",
              "      <th>discharge</th>\n",
              "      <th>vesseltype_1.0</th>\n",
              "      <th>vesseltype_2.0</th>\n",
              "      <th>vesseltype_3.0</th>\n",
              "      <th>vesseltype_4.0</th>\n",
              "      <th>vesseltype_5.0</th>\n",
              "      <th>traveltype_ARRIVAL</th>\n",
              "      <th>traveltype_SHIFT</th>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8192</th>\n",
              "      <td>158583.0</td>\n",
              "      <td>2</td>\n",
              "      <td>8</td>\n",
              "      <td>139431.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8194</th>\n",
              "      <td>37296.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>28127.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8205</th>\n",
              "      <td>4726.0</td>\n",
              "      <td>4</td>\n",
              "      <td>3</td>\n",
              "      <td>3537.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1184 rows × 13 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "      vesseldwt  n_stevs  process_time  discharge  vesseltype_1.0  \\\n",
              "0      109290.0        1             0    90173.0               0   \n",
              "5       74518.0        1             0    72741.0               0   \n",
              "8       17069.0        1             1    14044.0               0   \n",
              "27     158070.0        1             0   131231.0               0   \n",
              "36     111775.0        1             2    99862.0               0   \n",
              "...         ...      ...           ...        ...             ...   \n",
              "8188    25432.0        1             0    15980.0               0   \n",
              "8191     4993.0        2             7     4196.0               0   \n",
              "8192   158583.0        2             8   139431.0               0   \n",
              "8194    37296.0        1             0    28127.0               0   \n",
              "8205     4726.0        4             3     3537.0               0   \n",
              "\n",
              "      vesseltype_2.0  vesseltype_3.0  vesseltype_4.0  vesseltype_5.0  \\\n",
              "0                  0               0               0               1   \n",
              "5                  1               0               0               0   \n",
              "8                  0               0               0               1   \n",
              "27                 0               0               0               1   \n",
              "36                 0               0               0               1   \n",
              "...              ...             ...             ...             ...   \n",
              "8188               0               0               0               1   \n",
              "8191               0               0               0               1   \n",
              "8192               0               0               0               1   \n",
              "8194               0               0               0               1   \n",
              "8205               0               0               0               1   \n",
              "\n",
              "      traveltype_ARRIVAL  traveltype_SHIFT  bulk_liquid  bulk_solid  \n",
              "0                      1                 0            1           0  \n",
              "5                      1                 0            0           1  \n",
              "8                      1                 0            1           0  \n",
              "27                     1                 0            1           0  \n",
              "36                     1                 0            1           0  \n",
              "...                  ...               ...          ...         ...  \n",
              "8188                   1                 0            1           0  \n",
              "8191                   0                 1            1           0  \n",
              "8192                   0                 1            1           0  \n",
              "8194                   1                 0            1           0  \n",
              "8205                   0                 1            1           0  \n",
              "\n",
              "[1184 rows x 13 columns]"
            ]
          },
          "execution_count": 22,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "load_dataset, discharge_dataset = processor.encode_features()\n",
        "discharge_dataset"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ved6oTApM_Vm"
      },
      "source": [
        "# BALANCING PIPELINE"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "id": "-ee96FlgM_Vm",
        "outputId": "6c6821d0-06b8-4514-f230-5478f64ed310"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "(947, 12) (237, 12)\n",
            "(710, 12) (237, 12)\n"
          ]
        }
      ],
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "X = discharge_dataset.drop('discharge', axis= 1)\n",
        "y = discharge_dataset[['discharge']]\n",
        "\n",
        "############################################\n",
        "xtrain, xtest, ytrain, ytest = train_test_split(X,y, test_size= 0.2, shuffle= True)\n",
        "print (xtrain.shape, xtest.shape)\n",
        "xtrain, xval, ytrain, yval = train_test_split(xtrain, ytrain, test_size= 0.25, shuffle= True)\n",
        "print (xtrain.shape, xval.shape)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yKmRTjW_M_Vm"
      },
      "source": [
        "### 1. SMOGN\n",
        "Below is a demonstration of applying `synthetic oversampling with gaussian noise (SMOGN) on a training split."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "X_PmGQxPM_Vm",
        "outputId": "df8d452b-0aae-4e95-cae9-7abe780d6b04"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "dist_matrix: 100%|##########| 29/29 [00:00<00:00, 89.80it/s]\n",
            "synth_matrix: 100%|##########| 29/29 [00:00<00:00, 39.57it/s]\n",
            "r_index: 100%|##########| 6/6 [00:00<00:00, 375.88it/s]\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "xout, yout = apply_smogn(xtrain, ytrain)\n",
        "traindata = pd.concat([xout, yout],axis= 1).reset_index()\n",
        "\n",
        "fig, ax = plt.subplots(1,1, figsize = (10,6))\n",
        "sns.kdeplot(ytrain.discharge, ax = ax)\n",
        "# # sns.kdeplot(simple.discharge, ax = ax[1])\n",
        "sns.kdeplot(traindata.discharge, ax = ax)\n",
        "plt.legend(['y_train', 'y_train-SMOGN'])\n",
        "plt.title('KDE of Training Targets')\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mucdKq3GM_Vn"
      },
      "source": [
        "### 2. LDS - KERNEL SMOOTHING\n",
        "Label distribution smoothing aims to address the discontinuity in the regression target space, by smoothing and approximating an effective distribution of the targets from their original empirical distribution. <br>\n",
        "\n",
        "Below is a demonstration of estimating the LDS distibution by rebinning the discharge targets and subsequently using this distribution to calcuate `Inverse frequency reweighing`` for each data point."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "PseRsMbpM_Vn",
        "outputId": "6fc0c3f5-3727-4478-badc-047343b24415"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>discharge</th>\n",
              "      <th>bin</th>\n",
              "      <th>inverse</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>5934</th>\n",
              "      <td>96562.0</td>\n",
              "      <td>97000</td>\n",
              "      <td>1.457735</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1085</th>\n",
              "      <td>75279.0</td>\n",
              "      <td>76000</td>\n",
              "      <td>1.341116</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5712</th>\n",
              "      <td>59203.0</td>\n",
              "      <td>60000</td>\n",
              "      <td>1.457735</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6203</th>\n",
              "      <td>16001.0</td>\n",
              "      <td>17000</td>\n",
              "      <td>0.493057</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2376</th>\n",
              "      <td>78448.0</td>\n",
              "      <td>79000</td>\n",
              "      <td>1.117597</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6383</th>\n",
              "      <td>165516.0</td>\n",
              "      <td>166000</td>\n",
              "      <td>1.523996</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4523</th>\n",
              "      <td>100052.0</td>\n",
              "      <td>101000</td>\n",
              "      <td>0.399142</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>878</th>\n",
              "      <td>59010.0</td>\n",
              "      <td>60000</td>\n",
              "      <td>1.457735</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7296</th>\n",
              "      <td>31272.0</td>\n",
              "      <td>32000</td>\n",
              "      <td>0.957940</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7926</th>\n",
              "      <td>24212.0</td>\n",
              "      <td>25000</td>\n",
              "      <td>1.523996</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>710 rows × 3 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "      discharge     bin   inverse\n",
              "5934    96562.0   97000  1.457735\n",
              "1085    75279.0   76000  1.341116\n",
              "5712    59203.0   60000  1.457735\n",
              "6203    16001.0   17000  0.493057\n",
              "2376    78448.0   79000  1.117597\n",
              "...         ...     ...       ...\n",
              "6383   165516.0  166000  1.523996\n",
              "4523   100052.0  101000  0.399142\n",
              "878     59010.0   60000  1.457735\n",
              "7296    31272.0   32000  0.957940\n",
              "7926    24212.0   25000  1.523996\n",
              "\n",
              "[710 rows x 3 columns]"
            ]
          },
          "execution_count": 278,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "from  importlib import reload\n",
        "import utils\n",
        "reload(utils)\n",
        "from utils import *\n",
        "\n",
        "lds = LDS(labels= ytrain.discharge, buffer= 10000)\n",
        "bin_df = lds.bin_dataframe(ytrain)\n",
        "x_wts_df = lds.weight_df(bin_df, weight= 'inverse')\n",
        "x_wts_df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "6oxckMhsM_Vn",
        "outputId": "5c45fa46-8976-4a3a-f796-2d4e460e738e"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "D:\\anaconda3\\envs\\omdena\\lib\\site-packages\\ipykernel_launcher.py:7: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
            "  import sys\n",
            "D:\\anaconda3\\envs\\omdena\\lib\\site-packages\\ipykernel_launcher.py:10: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
            "  # Remove the CWD from sys.path while we load stuff.\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 3600x1440 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "smoothed, emp = lds.effective_dist(smooth= 3)\n",
        "bins, freqs = zip(*emp.items())\n",
        "\n",
        "sns.set(font_scale = 1)\n",
        "fig, (ax0, ax1) = plt.subplots(2,1, figsize= (50,20), )\n",
        "# sns.barplot(x= list(bins), y= list(freqs), ax =ax0)\n",
        "ax0.set_xticklabels(ax0.get_xticklabels(), rotation=90, ha='right')\n",
        "\n",
        "sns.histplot(data= ytrain, ax =ax0, bins = 300)\n",
        "ax0.set_xticklabels(bins, fontsize = 20)\n",
        "\n",
        "bins, freqs = zip(*smoothed.items())\n",
        "p = sns.barplot(x= list(bins), y= list(freqs), ax = ax1)\n",
        "ax1.set_xticklabels(ax1.get_xticklabels(), rotation=90, ha='right', fontsize = 15)\n",
        "plt.subplots_adjust(hspace= .2)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PdNAJzWgM_Vn"
      },
      "source": [
        "# INTEGRATED CROSS VALIDATION PIPELINE"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "SyIX5ZvYM_Vn"
      },
      "outputs": [],
      "source": [
        "from sklearn.experimental import enable_halving_search_cv\n",
        "from sklearn.model_selection import GridSearchCV, HalvingGridSearchCV,ShuffleSplit, PredefinedSplit, LeaveOneOut, cross_val_score, cross_val_predict, KFold, GroupKFold, HalvingRandomSearchCV, RepeatedKFold, cross_validate\n",
        "from sklearn.metrics import make_scorer, mean_absolute_error, median_absolute_error\n",
        "from scipy.stats import t"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CNPSAai2M_Vo"
      },
      "source": [
        "#### FITTING A VANILLA XBGOOSTER"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "-HylOA-hM_Vo"
      },
      "outputs": [],
      "source": [
        "params = {'objective': 'reg:squarederror' , 'learning_rate': 0.3, 'gamma': 0, 'max_depth': 6, 'missing': 0, 'seed': 32 , 'subsample': 0.5, 'colsample_bytree': 0.5,\n",
        "'reg_lambda': 0, 'n_estimators': 3000, 'eval_metric': ['mae']}\n",
        "vanilla = xgb.XGBRegressor(**params, verbosity = 1, validate_parameters = True)\n",
        "eval_sets = [ (xtrain, ytrain), (xval, yval)]\n",
        "# model.get_xgb_params()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "M0kEPhKDM_Vo",
        "outputId": "ba4124f3-6b62-4724-8410-30d87f951b2d"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[0]\tvalidation_0-mae:51711.48047\tvalidation_1-mae:52023.30078\n",
            "[1]\tvalidation_0-mae:44776.72266\tvalidation_1-mae:45961.53906\n",
            "[2]\tvalidation_0-mae:32953.67188\tvalidation_1-mae:33957.05469\n",
            "[3]\tvalidation_0-mae:25371.34375\tvalidation_1-mae:26857.37109\n",
            "[4]\tvalidation_0-mae:24247.29492\tvalidation_1-mae:25892.60156\n",
            "[5]\tvalidation_0-mae:19597.45508\tvalidation_1-mae:21890.28906\n",
            "[6]\tvalidation_0-mae:16318.77441\tvalidation_1-mae:19422.24219\n",
            "[7]\tvalidation_0-mae:16158.78125\tvalidation_1-mae:19325.58984\n",
            "[8]\tvalidation_0-mae:13667.04394\tvalidation_1-mae:17421.49805\n",
            "[9]\tvalidation_0-mae:13528.86914\tvalidation_1-mae:17413.73828\n",
            "[10]\tvalidation_0-mae:11902.56348\tvalidation_1-mae:16202.28027\n",
            "[11]\tvalidation_0-mae:11161.02246\tvalidation_1-mae:15364.45215\n",
            "[12]\tvalidation_0-mae:10534.28906\tvalidation_1-mae:15419.99414\n",
            "[13]\tvalidation_0-mae:10522.11426\tvalidation_1-mae:15389.07129\n",
            "[14]\tvalidation_0-mae:10167.01172\tvalidation_1-mae:15447.85644\n",
            "[15]\tvalidation_0-mae:10130.74414\tvalidation_1-mae:15404.12402\n",
            "[16]\tvalidation_0-mae:9300.78906\tvalidation_1-mae:14888.32129\n",
            "[17]\tvalidation_0-mae:8861.02441\tvalidation_1-mae:14875.05762\n",
            "[18]\tvalidation_0-mae:8760.97070\tvalidation_1-mae:14777.57129\n",
            "[19]\tvalidation_0-mae:8764.45606\tvalidation_1-mae:14824.22070\n",
            "[20]\tvalidation_0-mae:8744.60840\tvalidation_1-mae:14947.17188\n",
            "[21]\tvalidation_0-mae:8672.25586\tvalidation_1-mae:14823.55469\n",
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            "[23]\tvalidation_0-mae:8409.99316\tvalidation_1-mae:14732.86621\n",
            "[24]\tvalidation_0-mae:8193.20019\tvalidation_1-mae:14689.21973\n",
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            "[54]\tvalidation_0-mae:6620.76416\tvalidation_1-mae:14614.35156\n",
            "[55]\tvalidation_0-mae:6576.79736\tvalidation_1-mae:14657.18457\n",
            "[56]\tvalidation_0-mae:6496.65430\tvalidation_1-mae:14492.80273\n",
            "[57]\tvalidation_0-mae:6333.81885\tvalidation_1-mae:14463.06543\n",
            "[58]\tvalidation_0-mae:6273.60645\tvalidation_1-mae:14349.06250\n",
            "[59]\tvalidation_0-mae:6268.57471\tvalidation_1-mae:14363.61523\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "XGBRegressor(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
              "             colsample_bynode=1, colsample_bytree=0.5, enable_categorical=False,\n",
              "             eval_metric=['mae'], gamma=0, gpu_id=-1, importance_type=None,\n",
              "             interaction_constraints='', learning_rate=0.3, max_delta_step=0,\n",
              "             max_depth=6, min_child_weight=1, missing=0,\n",
              "             monotone_constraints='()', n_estimators=3000, n_jobs=12,\n",
              "             num_parallel_tree=1, predictor='auto', random_state=32,\n",
              "             reg_alpha=0, reg_lambda=0, scale_pos_weight=1, seed=32,\n",
              "             subsample=0.5, tree_method='exact', validate_parameters=True,\n",
              "             verbosity=1)"
            ]
          },
          "execution_count": 301,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "vanilla.fit(xtrain, ytrain, eval_set= eval_sets, verbose= True, early_stopping_rounds= 10 )"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "133YgIvcM_Vo"
      },
      "source": [
        "#### SEARCH SPACE"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "v0S6FFLSM_Vo"
      },
      "outputs": [],
      "source": [
        "#### RANDOM SEARCH DISTs\n",
        "# 1. a beta prime distribution for gamma\n",
        "gamma_dist = stats.betaprime(a = 2,b =6, scale = 10)\n",
        "\n",
        "# 2. an exponential distribution for lambda\n",
        "lambda_dist = stats.expon(scale = 3)\n",
        "\n",
        "# 3. a log uniform distribution for learning rate\n",
        "lr_dist = stats.loguniform(.0001, .5)\n",
        "\n",
        "# 4. a negative binomial distribution for maxchild\n",
        "maxchild_dist = stats.nbinom(2, .4, loc = 1)\n",
        "\n",
        "# 5. a beta distribution for colsample_bytree\n",
        "colrow_dist = stats.beta(2.5,1)\n",
        "\n",
        "# 6. a negative binomial distribution for max_depth\n",
        "treedepth_dist = stats.nbinom(20, .5)\n",
        "\n",
        "search_space = {'objective': ['reg:squarederror'],\n",
        "                'learning_rate':lr_dist,\n",
        "                'max_depth':treedepth_dist,\n",
        "                'subsample': colrow_dist,\n",
        "                'colsample_bytree': colrow_dist,\n",
        "                'reg_lambda': lambda_dist,\n",
        "                'min_child_weight': maxchild_dist,\n",
        "                'gamma': gamma_dist}\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yV3Q3aGPIwnq"
      },
      "source": [
        "# EXECUTE PIPELINE"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "7z9QBzLjIwnq"
      },
      "outputs": [],
      "source": [
        "# 1. setting the inner random parameter search loop @ k = 4 folds\n",
        "inner_cv = RepeatedKFold(n_splits=4, n_repeats= 1, random_state= state)\n",
        "\n",
        "# 2. setting the outer error estiamttion loop @ k = 4 folds\n",
        "outer_cv = KFold(n_splits= 4, shuffle= True, random_state= state)\n",
        "\n",
        "# 3. Initializing a set of parameters\n",
        "init_params = vanilla.get_params()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XsNoJGD2Q-JX"
      },
      "source": [
        "Below is the execution of the Integrated balancing and nested cross validation pipeline"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true,
          "base_uri": "https://localhost:8080/"
        },
        "id": "ib4CxKDnIwnr",
        "outputId": "a40588d5-c0d9-44f4-f36b-73e50126f3b0"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
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              "                                               model        score\n",
              "0  XGBRegressor(base_score=0.5, booster='gbtree',...  5477.814453\n",
              "1  XGBRegressor(base_score=0.5, booster='gbtree',...  4901.831055\n",
              "2  XGBRegressor(base_score=0.5, booster='gbtree',...  5762.511780\n",
              "3  XGBRegressor(base_score=0.5, booster='gbtree',...  7893.589844"
            ]
          },
          "execution_count": null,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "from  importlib import reload\n",
        "import pipeline\n",
        "reload(pipeline)\n",
        "import utils\n",
        "reload(utils)\n",
        "from pipeline import *\n",
        "\n",
        "import smogn\n",
        "reload(smogn)\n",
        "from smogn import *\n",
        "\n",
        "pipe = pipeline(vanilla.get_params(), inner_cv= inner_cv, outer_cv= outer_cv)\n",
        "\n",
        "bestmodel, scores_df = pipe.nested_validation(X, y, n_candids= 30)\n",
        "scores_df.to_pickle(datapath + 'scores_df.pkl')\n",
        "scores_df"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QkJEet2AXqML"
      },
      "source": [
        "### CV SCORES"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 174
        },
        "id": "pMDG6q8t9MXS",
        "outputId": "4239ad5c-9220-4dda-b154-a18f70975552"
      },
      "outputs": [
        {
          "data": {
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              "\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>model</th>\n",
              "      <th>score</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>XGBRegressor(base_score=0.5, booster='gbtree',...</td>\n",
              "      <td>5477.814453</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>XGBRegressor(base_score=0.5, booster='gbtree',...</td>\n",
              "      <td>4901.831055</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>XGBRegressor(base_score=0.5, booster='gbtree',...</td>\n",
              "      <td>5762.511780</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>XGBRegressor(base_score=0.5, booster='gbtree',...</td>\n",
              "      <td>7893.589844</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-f4dfb70a-db92-43cf-b97a-3a59bdb9c306')\"\n",
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              "      \n",
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              "      display:flex;\n",
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              "\n",
              "    .colab-df-convert {\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-f4dfb70a-db92-43cf-b97a-3a59bdb9c306 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-f4dfb70a-db92-43cf-b97a-3a59bdb9c306');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "                                               model        score\n",
              "0  XGBRegressor(base_score=0.5, booster='gbtree',...  5477.814453\n",
              "1  XGBRegressor(base_score=0.5, booster='gbtree',...  4901.831055\n",
              "2  XGBRegressor(base_score=0.5, booster='gbtree',...  5762.511780\n",
              "3  XGBRegressor(base_score=0.5, booster='gbtree',...  7893.589844"
            ]
          },
          "execution_count": 25,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "scores_df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "VbVpCKbvIwnr"
      },
      "outputs": [],
      "source": [
        "bestmodel.save_model('/content/drive/MyDrive/Transshipment/models/bestmodel_2.json')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "y3Etm7-4RKxC"
      },
      "source": [
        "### BEST MODEL PARAMETERS"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fv7y2lDHeiMy",
        "outputId": "0824a746-e900-4dfb-ffc3-ea10fa3f44a1"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "{'base_score': 0.5,\n",
              " 'booster': 'gbtree',\n",
              " 'colsample_bylevel': 1,\n",
              " 'colsample_bynode': 1,\n",
              " 'colsample_bytree': 0.8801672539105327,\n",
              " 'eval_metric': ['mae', 'rmse'],\n",
              " 'gamma': 10.182123584678035,\n",
              " 'gpu_id': -1,\n",
              " 'interaction_constraints': '',\n",
              " 'learning_rate': 0.019437673150966728,\n",
              " 'max_delta_step': 0,\n",
              " 'max_depth': 16,\n",
              " 'min_child_weight': 4,\n",
              " 'monotone_constraints': '()',\n",
              " 'n_jobs': 2,\n",
              " 'num_parallel_tree': 1,\n",
              " 'predictor': 'auto',\n",
              " 'random_state': 32,\n",
              " 'reg_alpha': 0,\n",
              " 'reg_lambda': 6.261495311486096,\n",
              " 'scale_pos_weight': 1,\n",
              " 'seed': 32,\n",
              " 'subsample': 0.7212236249826819,\n",
              " 'tree_method': 'exact',\n",
              " 'validate_parameters': True,\n",
              " 'verbosity': 1}"
            ]
          },
          "execution_count": 108,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "bestmodel.get_xgb_params()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 865
        },
        "id": "kJbj_sqNv_iQ",
        "outputId": "5480b026-81e0-4d65-c6c9-f725475f1423"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-1f98cc08-cbb7-4882-813f-5fb986d70522\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>0</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>base_score</th>\n",
              "      <td>0.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>booster</th>\n",
              "      <td>gbtree</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>colsample_bylevel</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>colsample_bynode</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>colsample_bytree</th>\n",
              "      <td>0.880167</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>gamma</th>\n",
              "      <td>10.182124</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>gpu_id</th>\n",
              "      <td>-1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>interaction_constraints</th>\n",
              "      <td></td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>learning_rate</th>\n",
              "      <td>0.019438</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max_delta_step</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max_depth</th>\n",
              "      <td>16</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min_child_weight</th>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>monotone_constraints</th>\n",
              "      <td>()</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>n_jobs</th>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>num_parallel_tree</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>predictor</th>\n",
              "      <td>auto</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>random_state</th>\n",
              "      <td>32</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>reg_alpha</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>reg_lambda</th>\n",
              "      <td>6.261495</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>scale_pos_weight</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>subsample</th>\n",
              "      <td>0.721224</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>tree_method</th>\n",
              "      <td>exact</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>validate_parameters</th>\n",
              "      <td>True</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>verbosity</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>seed</th>\n",
              "      <td>32</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>eval_metric</th>\n",
              "      <td>[mae, rmse]</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-1f98cc08-cbb7-4882-813f-5fb986d70522')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
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              "\n",
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              "\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-1f98cc08-cbb7-4882-813f-5fb986d70522 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-1f98cc08-cbb7-4882-813f-5fb986d70522');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "                                   0\n",
              "base_score                       0.5\n",
              "booster                       gbtree\n",
              "colsample_bylevel                  1\n",
              "colsample_bynode                   1\n",
              "colsample_bytree            0.880167\n",
              "gamma                      10.182124\n",
              "gpu_id                            -1\n",
              "interaction_constraints             \n",
              "learning_rate               0.019438\n",
              "max_delta_step                     0\n",
              "max_depth                         16\n",
              "min_child_weight                   4\n",
              "monotone_constraints              ()\n",
              "n_jobs                             2\n",
              "num_parallel_tree                  1\n",
              "predictor                       auto\n",
              "random_state                      32\n",
              "reg_alpha                          0\n",
              "reg_lambda                  6.261495\n",
              "scale_pos_weight                   1\n",
              "subsample                   0.721224\n",
              "tree_method                    exact\n",
              "validate_parameters             True\n",
              "verbosity                          1\n",
              "seed                              32\n",
              "eval_metric              [mae, rmse]"
            ]
          },
          "execution_count": 111,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "pd.DataFrame.from_dict(bestmodel.get_xgb_params(), orient= 'index')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "C6vVjOTDRPFB"
      },
      "source": [
        "### FEATURE IMPORTANCE"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 518
        },
        "id": "_ipzed2093gH",
        "outputId": "3b339883-d5b1-4f75-ff16-68d18e9a6f99"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1080x576 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "fig, ax = plt.subplots(figsize=(15, 8))\n",
        "xgb.plot_importance(bestmodel, ax = ax)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bUYuoy6pRXs5"
      },
      "source": [
        "### INSTANCES OF THE ESTIMATOR TREES"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 475
        },
        "id": "Mm7R9huG_YzN",
        "outputId": "8984ec98-1653-4a67-cc4c-1811b34c57af"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 2160x1080 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 2160x1440 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "fig, ax = plt.subplots(figsize=(30, 15))\n",
        "xgb.plot_tree(bestmodel, num_trees= 0,  ax = ax, rankdir= 'LR')\n",
        "\n",
        "fig, ax = plt.subplots(figsize=(30, 20))\n",
        "xgb.plot_tree(bestmodel, num_trees= 10,  ax = ax, rankdir= 'LR')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CWmVSEqcYavL",
        "outputId": "fe17b112-d9fe-44f0-cb8b-c049ba75398c"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "XGBRegressor(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
              "             colsample_bynode=1, colsample_bytree=0.8801672539105327,\n",
              "             enable_categorical=False, eval_metric=['mae', 'rmse'],\n",
              "             gamma=10.182123584678035, gpu_id=-1, importance_type=None,\n",
              "             interaction_constraints='', learning_rate=0.019437673150966728,\n",
              "             max_delta_step=0, max_depth=16, min_child_weight=4, missing=0,\n",
              "             monotone_constraints='()', n_estimators=3000, n_jobs=2,\n",
              "             num_parallel_tree=1, predictor='auto', random_state=32,\n",
              "             reg_alpha=0, reg_lambda=6.261495311486096, scale_pos_weight=1,\n",
              "             seed=32, subsample=0.7212236249826819, tree_method='exact',\n",
              "             validate_parameters=True, verbosity=1)"
            ]
          },
          "execution_count": 27,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "m = xgb.XGBRegressor()\n",
        "m.load_model(modelspath + 'bestmodel_2.json')\n",
        "m"
      ]
    }
  ],
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