notebook.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Recovery of gold from ore"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[Colab link](https://colab.research.google.com/drive/1Ews_4ww6Zc7KNz9C-W9BoA_MKUzWc0hC?usp=sharing) for convenience."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data preprocessing\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Importing libraries and loading datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "sns.set()  # set plotting style\n",
    "import warnings\n",
    "warnings.simplefilter(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: feature_selector in d:\\anaconda\\lib\\site-packages (1.0.0)\n"
     ]
    }
   ],
   "source": [
    "!pip install feature_selector --no-deps\n",
    "from feature_selector import FeatureSelector\n",
    "\n",
    "from scipy.stats import ttest_ind_from_stats\n",
    "\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.metrics import make_scorer\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.dummy import DummyRegressor\n",
    "\n",
    "from catboost import CatBoostRegressor, Pool"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_full = pd.read_csv('datasets/gold_recovery_full_new.csv', index_col='date')\n",
    "df_train_full = pd.read_csv('datasets/gold_recovery_train_new.csv', index_col='date')\n",
    "df_test = pd.read_csv('datasets/gold_recovery_test_new.csv', index_col='date')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Datasets description"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Three datasets are provided:\n",
    "1. **df_full** contains all available data.\n",
    "2. **df_train_full** contains the training dataset.\n",
    "3. **df_test** contains the test dataset.\n",
    "  \n",
    "Data shapes:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "df_full.shape = (19439, 86)\n",
      "df_train_full.shape = (14149, 86)\n",
      "df_test.shape = (5290, 52)\n"
     ]
    }
   ],
   "source": [
    "print(f'{df_full.shape = }')\n",
    "print(f'{df_train_full.shape = }')\n",
    "print(f'{df_test.shape = }')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are fewer features in the test dataset than in the full and training ones.   \n",
    "Feature types presented in the task:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{dtype('float64')}"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "set(df_train_full.dtypes)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "All columns, with the exception of `date` (index column), are of the *float64* numeric type. No categorical data, no encoding is needed for machine learning algorithms. \n",
    "  \n",
    "Top features with gaps and numbers of gaps in training dataset:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "secondary_cleaner.output.tail_sol           1605.0\n",
       "rougher.state.floatbank10_e_air              436.0\n",
       "rougher.input.floatbank11_xanthate           428.0\n",
       "primary_cleaner.output.concentrate_sol       286.0\n",
       "secondary_cleaner.state.floatbank2_a_air     217.0\n",
       "final.output.concentrate_sol                 211.0\n",
       "rougher.input.feed_size                      144.0\n",
       "Name: count, dtype: float64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gaps = len(df_train_full) - df_train_full.describe().loc['count']\n",
    "gaps[gaps > 100].sort_values(ascending=False)  # selecting only features with more than 100 gaps"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Only 7 features out of 86 in train dataset have more than 100 gaps (100 gaps is less than 1% of dataset). Only one column has more than 500 gaps (3.5% of dataset)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Taking a closer look at the `date` (index) column:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['2016-01-15 00:00:00', '2016-01-15 01:00:00', '2016-01-15 02:00:00',\n",
       "       '2016-01-15 03:00:00', '2016-01-15 04:00:00'],\n",
       "      dtype='object', name='date')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train_full.head().index"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The index column contains information about date and time of the observation. In accordance with task decription, the data is already ordered chronologically and no sorting is needed. At the same time, the `date` column contains unique date and time values and can be used as an index.  \n",
    "  \n",
    "Checking the fulfillment of the uniqueness condition in the full dataset:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# checking if the count of unique values in the \"date\" column equals the count of all values\n",
    "display(df_full.index.nunique() == len(df_full.iloc[:,0]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "All date values are unique.  \n",
    "As this is a model task, both train and test dataset are derived from the full dataset.  \n",
    "  \n",
    "That means:\n",
    "1. that count of rows in (train + test) should equal the count of rows in full;\n",
    "2. all `date` values in (train + test) should be present in full.\n",
    "  \n",
    "Checking these conditions:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# checking if the numbers of rows in (train + test) and full are equal\n",
    "display(df_test.shape[0] + df_train_full.shape[0] == df_full.shape[0])\n",
    "\n",
    "# checking if all indices of (train + test) are present in full\n",
    "display(\n",
    "    pd.concat((df_train_full, df_test)).index.isin(df_full.index).sum() == len(df_full.iloc[:,0])\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "All `date` values from the combined dataset (test + training) are in **df_full**. So, **df_test** and **df_train_full** are non-overlapping samples from **df_full**. That means we can refer to full dataset if any additional information is needed for the task."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Features not available in the test sample"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The training dataset contains 86 features, while the test dataset contains only 52. Let's find out what is the reason for this difference:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The following features are absent in the test sample:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "['final.output.concentrate_ag',\n",
       " 'final.output.concentrate_pb',\n",
       " 'final.output.concentrate_sol',\n",
       " 'final.output.concentrate_au',\n",
       " 'final.output.recovery',\n",
       " 'final.output.tail_ag',\n",
       " 'final.output.tail_pb',\n",
       " 'final.output.tail_sol',\n",
       " 'final.output.tail_au',\n",
       " 'primary_cleaner.output.concentrate_ag',\n",
       " 'primary_cleaner.output.concentrate_pb',\n",
       " 'primary_cleaner.output.concentrate_sol',\n",
       " 'primary_cleaner.output.concentrate_au',\n",
       " 'primary_cleaner.output.tail_ag',\n",
       " 'primary_cleaner.output.tail_pb',\n",
       " 'primary_cleaner.output.tail_sol',\n",
       " 'primary_cleaner.output.tail_au',\n",
       " 'rougher.calculation.sulfate_to_au_concentrate',\n",
       " 'rougher.calculation.floatbank10_sulfate_to_au_feed',\n",
       " 'rougher.calculation.floatbank11_sulfate_to_au_feed',\n",
       " 'rougher.calculation.au_pb_ratio',\n",
       " 'rougher.output.concentrate_ag',\n",
       " 'rougher.output.concentrate_pb',\n",
       " 'rougher.output.concentrate_sol',\n",
       " 'rougher.output.concentrate_au',\n",
       " 'rougher.output.recovery',\n",
       " 'rougher.output.tail_ag',\n",
       " 'rougher.output.tail_pb',\n",
       " 'rougher.output.tail_sol',\n",
       " 'rougher.output.tail_au',\n",
       " 'secondary_cleaner.output.tail_ag',\n",
       " 'secondary_cleaner.output.tail_pb',\n",
       " 'secondary_cleaner.output.tail_sol',\n",
       " 'secondary_cleaner.output.tail_au']"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print('The following features are absent in the test sample:')\n",
    "display([column for column in df_train_full.columns if column not in df_test.columns])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The test set lacks **output** parameters (metal concentrations), calculated characteristics (**calculation**) and **recovery** metrics. This is logical since the metal concentrations and characteristics calculated from the gold content are directly related to the metric we want to predict (**recovery**). They cannot be present in the test sample. If they were, there would be no task!\n",
    "  \n",
    "The same features will be eliminated from the training sample before training the ML models. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Correctness of `rougher.output.recovery`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`rougher.output.recovery` is a characteristic calculated from other features. To ensure that the provided dataset is correct, the values of this feature should be compared to calculation:"
   ]
  },
  {
   "attachments": {
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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "where:  \n",
    "***recovery*** is the recovery percentage (`rougher.output.recovery` or `final.output.recovery`);  \n",
    "***C*** is the share of gold in the concentrate after flotation/cleaning (e.g. `rougher.output.concentrate_au`);  \n",
    "***F*** is the share of gold in the raw material/concentrate before flotation/cleaning(e.g. `rougher.input.feed_au`);  \n",
    "***T*** is the share of gold in final tailings after flotation/cleaning (e.g. `rougher.output.tail_au`).  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "C = df_full[\"rougher.output.concentrate_au\"]\n",
    "F = df_full[\"rougher.input.feed_au\"]\n",
    "T = df_full[\"rougher.output.tail_au\"]\n",
    "recovery_calculated = C / F * (F - T) / (C - T) * 100"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Calculate MAE:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MAE = 1.14e-14 %\n"
     ]
    }
   ],
   "source": [
    "MAE = abs(df_full['rougher.output.recovery'] - recovery_calculated).mean()\n",
    "print(f'{MAE = :.2e} %')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The metric is negligible, which means that the correct recovery value is present in the training sample."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data cleaning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Several lists of features for further analysis are prepared:\n",
    "\n",
    "1. Lists of positive features (for logscale plotting) for training and test datasets;\n",
    "2. Lists of negative features (**...level** features);\n",
    "3. List of important features, outliers from which will not be specifically deleted. These are the features that are marked important for random tree regression, see section 3.4.1 for details."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "columns_all_train = df_train_full.columns.tolist()\n",
    "columns_negative_train = df_train_full.filter(regex=\".*level\").columns.tolist()\n",
    "columns_positive_train = df_train_full[columns_all_train].drop(columns_negative_train, axis=1).columns.tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "columns_all_test = df_test.columns.tolist()\n",
    "columns_negative_test = df_test.filter(regex=\".*level\").columns.tolist()\n",
    "columns_positive_test = df_test[columns_all_test].drop(columns_negative_test, axis=1).columns.tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "important_features = [\n",
    "    'rougher.state.floatbank10_c_air', \n",
    "    'rougher.state.floatbank10_a_air',\n",
    "    'rougher.state.floatbank10_f_air',\n",
    "    'rougher.state.floatbank10_a_level',\n",
    "    'rougher.input.feed_ag',\n",
    "    'rougher.input.feed_pb',\n",
    "    'rougher.input.feed_size',\n",
    "    'primary_cleaner.input.sulfate',\n",
    "    'secondary_cleaner.state.floatbank2_a_air',   \n",
    "]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Set the x limits to display and prepare function for plotting boxplots:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_lim_norm = [-1000, 0]\n",
    "x_lim_log = [10e-4, 10e4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_boxplot(data_to_plot, column_labels, title, x_lim=None, data_to_compare=None):\n",
    "    f, ax = plt.subplots(figsize=(14, len(column_labels)))\n",
    "    sns.boxplot(data=data_to_plot[column_labels], orient='h')\n",
    "            \n",
    "    if data_to_compare is not None:  # second boxplot on the same figure for comparison\n",
    "        sns.boxplot(\n",
    "            data_to_compare[column_labels], \n",
    "            orient='h', \n",
    "            width=0.5, \n",
    "            color='white', \n",
    "            saturation=0.2, \n",
    "            fliersize=2\n",
    "        )\n",
    "        \n",
    "    if x_lim:  # choosing the scale (log or normal)\n",
    "        plt.xlim(x_lim)\n",
    "        if x_lim[0] == x_lim_log[0]:\n",
    "            ax.set_xscale(\"log\")\n",
    "        \n",
    "    ax.set_yticklabels(column_labels, size=12)\n",
    "    plt.title(f'Boxplots for {title}', size=20)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "All data types in datasets are valid. The `date` column could be transformed to the *datetime* type, but this is not required in the current task. It remains to work with duplicates, gaps and outliers."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Duplicates"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "All values of the `date` index are unique, so there cannot be complete duplicates. However, for example, there is a possibility that the observation for the new hour duplicates the observation for the previous one. Let's see if there are any such duplicates:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Duplicates in training dataset: 0\n",
      "Duplicates in test dataset: 0\n"
     ]
    }
   ],
   "source": [
    "print('Duplicates in training dataset:', df_train_full.duplicated().sum())\n",
    "print('Duplicates in test dataset:', df_test.duplicated().sum())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are no duplicates, all objects in the datasets are unique."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Gaps"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "According to the task description, \"parameters adjacent in time are often similar.\" This means that the gaps can be filled with the *ffill* or *bfill* methods, or the `df.interpolate` function can be used. Use it for both training and test datasets:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_train_full = df_train_full.fillna(method='bfill')\n",
    "df_test = df_test.fillna(method='bfill')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Illogical values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, let's take a look at the min and max values in columns. Following conditions should be met:\n",
    "1. All features with concentration should have values in the [0, 100] interval;\n",
    "2. All features except **...level** (see extended notebook for details) should not contain negative values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "min_max_train = df_train_full.describe().loc[['min', 'max']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1mTrain dataset, number of features with max concentration features values > 100%:\u001b[0m\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# check if any concentrations values are > 100%\n",
    "print('\\033[1mTrain dataset, number of features with max concentration features values > 100%:\\033[0m')\n",
    "display(\n",
    "    (df_train_full.filter(regex=(\".*concentrate.+|tail|recovery\")).max() > 100).sum()\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1mTrain dataset, min values in columns with negative values:\u001b[0m\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "rougher.calculation.sulfate_to_au_concentrate        -4.223520e+07\n",
       "rougher.calculation.floatbank10_sulfate_to_au_feed   -4.865987e+02\n",
       "rougher.calculation.floatbank11_sulfate_to_au_feed   -2.649828e+02\n",
       "rougher.calculation.au_pb_ratio                      -1.054761e-02\n",
       "rougher.state.floatbank10_a_air                      -4.107792e-02\n",
       "rougher.state.floatbank10_b_air                      -6.533838e-01\n",
       "rougher.state.floatbank10_c_air                      -3.511233e-02\n",
       "rougher.state.floatbank10_d_air                      -5.542987e-01\n",
       "rougher.state.floatbank10_e_air                      -1.970275e+00\n",
       "rougher.state.floatbank10_f_air                      -2.430587e+00\n",
       "secondary_cleaner.state.floatbank5_a_air             -3.720540e-01\n",
       "Name: min, dtype: float64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# check if there are negative values where they shouldn't be\n",
    "print('\\033[1mTrain dataset, min values in columns with negative values:\\033[0m')\n",
    "display(\n",
    "    min_max_train.loc[:,(min_max_train < 0).any()].drop(  \n",
    "    columns_negative_train, axis=1\n",
    "    ).T['min']\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "All concentration features are OK. Some **...air** and **calculation** features have negative values, which will be deleted for this task. \n",
    "  \n",
    "The same analysis for **df_test** dataset:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "min_max_test = df_test.describe().loc[['min', 'max']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1mTest dataset, number of features with max concentration features values > 100%:\u001b[0m\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# check if any concentrations values are > 100%\n",
    "print('\\033[1mTest dataset, number of features with max concentration features values > 100%:\\033[0m')\n",
    "display(\n",
    "    (df_train_full.filter(regex=(\".*concentrate.+|tail|recovery\")).max() > 100).sum()\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1mTest dataset, min values in columns with negative values:\u001b[0m\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "rougher.state.floatbank10_a_air            -0.042069\n",
       "rougher.state.floatbank10_b_air            -0.721806\n",
       "rougher.state.floatbank10_c_air            -0.056636\n",
       "rougher.state.floatbank10_d_air            -0.992219\n",
       "rougher.state.floatbank10_e_air            -1.982295\n",
       "rougher.state.floatbank10_f_air            -2.593390\n",
       "secondary_cleaner.state.floatbank6_a_air   -0.079426\n",
       "Name: min, dtype: float64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# select all columns with negative values except \"level\" columns and display min values\n",
    "print('\\033[1mTest dataset, min values in columns with negative values:\\033[0m')\n",
    "display(\n",
    "    min_max_test.loc[:,(min_max_test < 0).any()].drop(  \n",
    "    columns_negative_test, axis=1\n",
    "    ).T['min']\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As for the training dataset, all concentration features are OK and some **...air** features have negative values."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Remove negative values where they are illogical (all but **...level** features):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "for column in columns_positive_train:    \n",
    "    df_train_full = df_train_full.drop(\n",
    "        df_train_full[df_train_full[column] < 0].index\n",
    "    )\n",
    "for column in columns_positive_test:\n",
    "    df_test = df_test.drop(df_test[df_test[column] < 0].index)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "No other values in datasets seem to be illogical (e.g. no concentration values that are >100%). However, it is possible that all concentrations at a certain process stage make up for more than 100%. Checking it:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "# returns all column names for concentration features (au, ag, pb, sol) for a passed stage \n",
    "def select_columns(stage_name):\n",
    "    return df_train_full.filter(regex=(f'{stage_name}.*')).columns.tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "# get a dictionary for easier plotting\n",
    "all_stages_concentrations = {\n",
    "    'rougher_input': select_columns('rougher.input.feed_([ap]|sol)'), # this stage has some more \"feed_\" params\n",
    "    'rougher_output': select_columns('rougher.output.concentrate'),\n",
    "    'primary_cleaner_output': select_columns('primary_cleaner.output.concentrate'), \n",
    "    'final_output': select_columns('final.output.concentrate'), \n",
    "    'rougher_output_tail': select_columns('rougher.output.tail'), \n",
    "    'primary_cleaner_output_tail': select_columns('primary_cleaner.output.tail'),\n",
    "    'secondary_cleaner_output_tail': select_columns('secondary_cleaner.output.tail'), \n",
    "    'final_output_tail': select_columns('final.output.tail'),    \n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "aggregate_concentrations_per_stage = pd.DataFrame()\n",
    "\n",
    "# get dataframe with sums of all component concentrations for given stage\n",
    "for name, stage in all_stages_concentrations.items():   \n",
    "    aggregate_concentrations_per_stage[name] = df_train_full[stage].sum(axis=1)    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "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>rougher_input</th>\n",
       "      <th>rougher_output</th>\n",
       "      <th>primary_cleaner_output</th>\n",
       "      <th>final_output</th>\n",
       "      <th>rougher_output_tail</th>\n",
       "      <th>primary_cleaner_output_tail</th>\n",
       "      <th>secondary_cleaner_output_tail</th>\n",
       "      <th>final_output_tail</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2016-01-15 00:00:00</th>\n",
       "      <td>51.680034</td>\n",
       "      <td>66.42495</td>\n",
       "      <td>72.640924</td>\n",
       "      <td>63.644396</td>\n",
       "      <td>25.841287</td>\n",
       "      <td>27.054192</td>\n",
       "      <td>30.565842</td>\n",
       "      <td>30.354855</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     rougher_input  rougher_output  primary_cleaner_output  \\\n",
       "date                                                                         \n",
       "2016-01-15 00:00:00      51.680034        66.42495               72.640924   \n",
       "\n",
       "                     final_output  rougher_output_tail  \\\n",
       "date                                                     \n",
       "2016-01-15 00:00:00     63.644396            25.841287   \n",
       "\n",
       "                     primary_cleaner_output_tail  \\\n",
       "date                                               \n",
       "2016-01-15 00:00:00                    27.054192   \n",
       "\n",
       "                     secondary_cleaner_output_tail  final_output_tail  \n",
       "date                                                                   \n",
       "2016-01-15 00:00:00                      30.565842          30.354855  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check dataframe structure\n",
    "aggregate_concentrations_per_stage.head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1400x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_boxplot(\n",
    "    aggregate_concentrations_per_stage, \n",
    "    aggregate_concentrations_per_stage.columns, \n",
    "    'aggregate concentrations (%) for different process stages'\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "No mistakes here, all aggregate concentrations are <100%."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Outliers"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The situation with outliers is quite complicated, as there are 86 features in the dataset.   \n",
    "To study outliers in other features, we will use boxplots. Here we randomly select 10 non-negative features and plot distribution boxplots for them. A more thorough study is presented in *notebook_extended*."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "from random import sample\n",
    "# select features for boxplot\n",
    "features_for_boxplot = sample(columns_positive_train, 10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot selected non-negative features using log scale:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1400x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_boxplot(\n",
    "    df_train_full, \n",
    "    features_for_boxplot, \n",
    "    'train dataset (selected features, logscale)', \n",
    "    x_lim=x_lim_log\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By analyzing this \"partial\" boxplot it is clear that for the given dataset it is common that there are quite a lot of explicit lower outliers and comparatively less upper outliers.\n",
    "\n",
    "A rather lazy brute force approach for outliers cleaning is proposed: simply cut off 0.2% of the upper and 0.1% of the lower outliers in each column (taking into account the fact that the same observation can be an outlier for several features at once).\n",
    "\n",
    "The advantage of this approach is that it saves a lot of time, and the main disadvantage is that we lose quite a lot of useful information. However, this approach might come in handy for random forest or linear regression models.\n",
    "  \n",
    "The new information will be stored in new dataset `df_train`, whilst the data without cleaning will be stored in `df_train_full`. We will later compare model performance on both datasets and conclude if this cleaning method is justified."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "index_to_drop = []  # list of indices to drop\n",
    "\n",
    "for column in [column for column in columns_all_train if column not in important_features]:\n",
    "    c_1 = df_train_full[column] < df_train_full[column].quantile(0.999)  # get Series of True/False with this condition\n",
    "    c_2 = df_train_full[column] > df_train_full[column].quantile(0.002)  # if False, it's an outlier\n",
    "\n",
    "    for ind in c_1.index[c_1 == False]:  # append indices of outliers\n",
    "        index_to_drop.append(ind)\n",
    "    for ind in c_2.index[c_2 == False]:\n",
    "        index_to_drop.append(ind)\n",
    "        \n",
    "index_to_drop = set(index_to_drop)  # remove indices duplicates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "len(index_to_drop) = 2622\n"
     ]
    }
   ],
   "source": [
    "print(f'{len(index_to_drop) = }')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The approach is quite extravagant, as we cut off 20% of the train dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_train = df_train_full.drop(index_to_drop)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "How the feature distributions look in the training dataset after the outliers were removed:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1400x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_boxplot(\n",
    "    df_train,\n",
    "    features_for_boxplot, \n",
    "    'train dataset after cleanup (selected features, logscale)', \n",
    "    x_lim=x_lim_log\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The number of explicit outliers visually was significantly reduced.  \n",
    "The data preprocessing for training dataset has been completed. If any preprocessing is required for the test dataset, it will be covered in the next chapter."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To gain a deeper understanding of the technological process, analyses of some features are conducted in this section. The mean concentrations of metals at each process stage, the distributions of aggregate metal concentrations at each process stage and the equality of distributions of features in training and test datasets are investigated."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Mean metal concentrations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following code is used to calculate the mean concentration of each metal on every stage of technological process:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "def return_concentrations(metal):\n",
    "    '''\n",
    "    Function selects corresponding features for the given metal.\n",
    "    Returns a list of mean concentrations for every stage for the given metal for every obervation.\n",
    "    '''\n",
    "    mean_conc = df_full[\n",
    "        [\n",
    "            f\"rougher.input.feed_{metal}\",\n",
    "            f\"rougher.output.concentrate_{metal}\",\n",
    "            f\"primary_cleaner.output.concentrate_{metal}\",\n",
    "            f\"final.output.concentrate_{metal}\",\n",
    "        ]\n",
    "    ]\n",
    "    mean_conc = mean_conc.apply('mean')\n",
    "    return mean_conc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "# get 3 dataframes with all calculations for the given metal\n",
    "mean_au = return_concentrations('au')\n",
    "mean_ag = return_concentrations('ag')\n",
    "mean_pb = return_concentrations('pb')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "# get xlabels\n",
    "stages = ['rougher.input', 'rougher.output', 'primary_cleaner.output', 'final.output']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot figures\n",
    "plt.figure(figsize=(10,5))\n",
    "plt.plot(stages, mean_au, marker='o', label='au mean concentration')\n",
    "plt.plot(stages, mean_ag, marker='o', label='ag mean concentration')\n",
    "plt.plot(stages, mean_pb, marker='o', label='pb mean concentration')\n",
    "\n",
    "plt.legend(fontsize=12)\n",
    "plt.xlabel('process stage', fontsize=14)\n",
    "plt.ylabel('concentration, %', fontsize=14)\n",
    "plt.title('metal mean concentration vs process stage', fontsize=16)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The figure shows the change of average concentration of gold, silver and lead at different process stages.\n",
    "Mean concentration of gold experiences a stable increase (from 8% in raw materials to 44% in the final product). The concentration of lead, at the same time, grows only slightly, while that of silver decreases towards the final stage after a rise from 8% to 12% during first two process steps. \n",
    "  \n",
    "The process is designed to concentrate gold. It is useful to know, if this process is robust and allows to get higher concentrations of metals consistently, or there are many cases of failed enrichment. To get a better understanding of this, we will investigate the distributions of aggregate metal concentrations at each stage."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Aggregate metal concentrations distribution"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When gold is recovered from ore, impurities (in particular, the sol) are removed. If the process works correctly, the aggregate metal concentration should increase with each process stage."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To verify this, histograms of the total concentrations of metals at each stage of the process are constructed:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "stage = 'rougher.input' \n",
      "Columns to sum: ['rougher.input.feed_ag', 'rougher.input.feed_pb', 'rougher.input.feed_au'] \n",
      "\n",
      "stage = 'rougher.output' \n",
      "Columns to sum: ['rougher.output.concentrate_ag', 'rougher.output.concentrate_pb', 'rougher.output.concentrate_au'] \n",
      "\n",
      "stage = 'primary_cleaner.output' \n",
      "Columns to sum: ['primary_cleaner.output.concentrate_ag', 'primary_cleaner.output.concentrate_pb', 'primary_cleaner.output.concentrate_au'] \n",
      "\n",
      "stage = 'final.output' \n",
      "Columns to sum: ['final.output.concentrate_ag', 'final.output.concentrate_pb', 'final.output.concentrate_au'] \n",
      "\n"
     ]
    }
   ],
   "source": [
    "aggregate_concentrations = pd.DataFrame()\n",
    "\n",
    "for stage in stages:   \n",
    "    # add up all possible concentrations of metals for each stage and add to dataframe\n",
    "    aggregate_concentrations[stage] =\\\n",
    "          df_train.filter(regex=f'{stage}.concentrate_[a-z]{{2}}$|{stage}.feed_[a-z]{{2}}$').sum(axis=1)    \n",
    "    \n",
    "    # checking the correctness of regex\n",
    "    print(f'{stage = }', \n",
    "      '\\nColumns to sum:', \n",
    "      df_train.filter(regex=f'{stage}.concentrate_[a-z]{{2}}$|{stage}.feed_[a-z]{{2}}$').columns.tolist(), \n",
    "      '\\n')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The mean values of aggregate concentrations at each process stage:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "rougher.input             19.881900\n",
       "rougher.output            39.663343\n",
       "primary_cleaner.output    50.676499\n",
       "final.output              59.722799\n",
       "dtype: float64"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "aggregate_concentrations.apply('mean')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It's clear from this table that mean aggregate concentration of metals grows with every process step."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot the histograms:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for column in aggregate_concentrations.columns:\n",
    "    aggregate_concentrations[column].hist(label=column, alpha=0.7, bins=30)\n",
    "\n",
    "plt.title('aggregate metal concentration histogram for different process stages')\n",
    "plt.xlabel('aggregate metal concentration, %')\n",
    "plt.ylabel('frequency')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As it was indicated earlier, the total concentration of metals, in general, increases with the stage of the technological process.\n",
    "\n",
    "At the same time, there is a rather strange anomaly in the test sample: after the primary cleaning stage, the total concentration of metals can drop sharply there is a column near 0 on the corresponding histogram). This happens after the lower 0.2% data in the column was cut off. Let's study these anomalies:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "outliers = aggregate_concentrations[aggregate_concentrations['primary_cleaner.output'] < 1].index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "77"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# number of outliers after primary cleaning process stage:\n",
    "len(outliers)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "rows with outliers:\n"
     ]
    },
    {
     "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>rougher.input</th>\n",
       "      <th>rougher.output</th>\n",
       "      <th>primary_cleaner.output</th>\n",
       "      <th>final.output</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2017-04-12 01:59:59</th>\n",
       "      <td>20.778967</td>\n",
       "      <td>39.537504</td>\n",
       "      <td>0.706329</td>\n",
       "      <td>59.773652</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-07-13 04:59:59</th>\n",
       "      <td>17.132616</td>\n",
       "      <td>40.443428</td>\n",
       "      <td>0.096607</td>\n",
       "      <td>58.069369</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-02-17 17:59:59</th>\n",
       "      <td>21.388213</td>\n",
       "      <td>39.668877</td>\n",
       "      <td>0.030000</td>\n",
       "      <td>59.582703</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-02-17 18:59:59</th>\n",
       "      <td>20.805423</td>\n",
       "      <td>40.164465</td>\n",
       "      <td>0.030000</td>\n",
       "      <td>60.155330</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-02-17 19:59:59</th>\n",
       "      <td>20.047464</td>\n",
       "      <td>40.654229</td>\n",
       "      <td>0.030000</td>\n",
       "      <td>60.751214</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-02-21 14:59:59</th>\n",
       "      <td>15.128186</td>\n",
       "      <td>42.388053</td>\n",
       "      <td>0.030000</td>\n",
       "      <td>12.536783</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-02-22 07:59:59</th>\n",
       "      <td>20.664600</td>\n",
       "      <td>43.491038</td>\n",
       "      <td>0.030000</td>\n",
       "      <td>60.647697</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-02-22 22:59:59</th>\n",
       "      <td>17.927621</td>\n",
       "      <td>39.981321</td>\n",
       "      <td>0.030000</td>\n",
       "      <td>60.570342</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     rougher.input  rougher.output  primary_cleaner.output  \\\n",
       "date                                                                         \n",
       "2017-04-12 01:59:59      20.778967       39.537504                0.706329   \n",
       "2017-07-13 04:59:59      17.132616       40.443428                0.096607   \n",
       "2018-02-17 17:59:59      21.388213       39.668877                0.030000   \n",
       "2018-02-17 18:59:59      20.805423       40.164465                0.030000   \n",
       "2018-02-17 19:59:59      20.047464       40.654229                0.030000   \n",
       "2018-02-21 14:59:59      15.128186       42.388053                0.030000   \n",
       "2018-02-22 07:59:59      20.664600       43.491038                0.030000   \n",
       "2018-02-22 22:59:59      17.927621       39.981321                0.030000   \n",
       "\n",
       "                     final.output  \n",
       "date                               \n",
       "2017-04-12 01:59:59     59.773652  \n",
       "2017-07-13 04:59:59     58.069369  \n",
       "2018-02-17 17:59:59     59.582703  \n",
       "2018-02-17 18:59:59     60.155330  \n",
       "2018-02-17 19:59:59     60.751214  \n",
       "2018-02-21 14:59:59     12.536783  \n",
       "2018-02-22 07:59:59     60.647697  \n",
       "2018-02-22 22:59:59     60.570342  "
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print('rows with outliers:')\n",
    "aggregate_concentrations[aggregate_concentrations['primary_cleaner.output'] < 1].head(8)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There is a number of anomalies, many of which are consecutive in time. They are observed only after the primary cleaning stage. Either there is some error / feature in the technological process, or an error in the concentration calculation. This can also be the result of `ffill` gaps fill method. Since we have already got rid of 0.2% lower outliers, these values seem to be a pattern that should not be ignored, so they won't be removed from datasets."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Equality of distributions in train and test datasets"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In order to make sure that our models will work for the test dataset, the feature distributions for test and train datasets should be compared. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Selected distributions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's compare distributions of a subset of features first and then investigate feed size (one of the most important features) distributions in more detail. Plotting boxplots for both test and training dataset on one figure (training dataset is white):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "features_for_boxplot = sample(columns_positive_test, 10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1400x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_boxplot(\n",
    "    df_test, \n",
    "    features_for_boxplot, \n",
    "    'test vs training (indicated white) datasets (positive features, logscale)', \n",
    "    x_lim=x_lim_log,\n",
    "    data_to_compare=df_train\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "All the selected features are distributed similarly. However, it is seen that many features in test dataset also have the outliers. To improve the final metrics on test dataset, these outliers can also be deleted. \n",
    "  \n",
    "The comparison of all dataset features are in the *notebook_extended*. \n",
    "  \n",
    "For this task no additional preprocessing will be conducted and all observations are supposed to be a sample from the same population. One of the distributions will be investigated in more detail as an example."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Distributions of feed size"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Input granule feed size for rougher process is an important feature for recovery prediction (see 3.4.1 for details). Significantly different distributions can lead to greater errors in model predictions. To verify that there is no significant difference in distribution, kde plots were constructed:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.histplot(\n",
    "    df_train, \n",
    "    x='rougher.input.feed_size', \n",
    "    label='train',\n",
    "    kde=True\n",
    ").set_title('rougher feed_size distribution (train vs test)')\n",
    "\n",
    "sns.histplot(\n",
    "    df_test, \n",
    "    x='rougher.input.feed_size', \n",
    "    color='green', \n",
    "    alpha=0.5, \n",
    "    label='test',\n",
    "    kde=True\n",
    ")\n",
    "\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The feed size distribution in the training and test samples in the initial raw material visually differs only slightly. We can perform a stat test to verify that this difference is significant. \n",
    "\n",
    "**Let H0: \"Mean input feed sizes for training and test samples are the same\"**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "# get numbers of observations\n",
    "nobs1 = len(df_train['rougher.input.feed_size'])\n",
    "nobs2 = len(df_test['rougher.input.feed_size'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "# calculate p-value for ttest for training and test data\n",
    "def ttest(parameter):\n",
    "    mean1 = df_train[parameter].mean()\n",
    "    std1 = df_train[parameter].var()\n",
    "    \n",
    "    mean2 = df_test[parameter].mean()\n",
    "    std2 = df_test[parameter].var()\n",
    "    \n",
    "    _, p_val = ttest_ind_from_stats(mean1, std1, nobs1, mean2, std2, nobs2, equal_var=False)\n",
    "    print(\"p-value:\", p_val)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "p-value: 0.6250676160024302\n"
     ]
    }
   ],
   "source": [
    "ttest('rougher.input.feed_size')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The p-value for comparison of rougher input feed size for training and test datasets is quite high. We don't reject the null hypothesis. Mean input feed size for rougher process is the same for training and test datasets.\n",
    "  \n",
    "Perform the same plotting for `primary_cleaner.input.feed_size`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.histplot(\n",
    "    df_train, \n",
    "    x='primary_cleaner.input.feed_size', \n",
    "    label='train',\n",
    "    kde=True\n",
    ").set_title('input feed_size distribution (train vs test)')\n",
    "\n",
    "sns.histplot(\n",
    "    df_test, \n",
    "    x='primary_cleaner.input.feed_size', \n",
    "    color='green', \n",
    "    alpha=0.5, \n",
    "    label='test',\n",
    "    kde=True\n",
    ")\n",
    "\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The grain size distributions in the training and test samples are visually equal. Running a statistical test:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "p-value: 1.2290406694510654e-35\n"
     ]
    }
   ],
   "source": [
    "ttest('primary_cleaner.input.feed_size')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The p-value is very low, reject the null hypothesis. Mean input feed size is different for training and test datasets for the primary cleaner input."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Though a statistically significant difference for feed sizes for train and test samples for rougher process was found, it was decided to not to perform any additional preprocessing, as the distirbutions are visually very close. \n",
    "  \n",
    "*P.S. Another option is to center the data before the machine learning, but this doesn't provide good results in the given task.*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Machine learning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After preprocessing and analyzing the data, it's time for predictions. In this chapter datasets are split, functions for calculation of task metrics are written and the best model on cross-validation is found. The model is then used to predict values on test dataset.\n",
    "  \n",
    "Following models are in scope:\n",
    "1. Linear regression (*sklearn LinearRegressor*)\n",
    "2. Random forest regression (*sklearn RandomForestRegressor*)\n",
    "3. Gradient boosting (*CatBoostRegressor*)\n",
    "  \n",
    "To work with the models, the dataset splitting should be performed."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Train and test datasets splitting"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will use **different** samples from training dataset to train models for prediction of either `rougher.output.recovery` or `final.output.recovery`. We can predict the latter using the full train dataset, while for the former it is reasonable to use only the **rougher...** columns. All other columns contain information for further processes that is gained already after we know `rougher.output.recovery` for current observation. These columns should not be used for training models to predict `rougher.output.recovery`.\n",
    "\n",
    "This is a custom function that provides the desired split. It receives a dataset and returns split data as a dictionary:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "def custom_split(df, suffix=None):\n",
    "    X_rougher = df.filter(\n",
    "        regex=\"rougher.*\"  # other columns contain excessive info for prediction of rougher\n",
    "    ).drop('rougher.output.recovery', axis=1)  \n",
    "    y_rougher = df['rougher.output.recovery']\n",
    "    \n",
    "    X_final = df.drop(['rougher.output.recovery', 'final.output.recovery'], axis=1) \n",
    "    y_final = df['final.output.recovery']\n",
    "    \n",
    "    # print the shapes of split dataset for check\n",
    "    print(f'X_rougher_{suffix}.shape = {X_rougher.shape}')\n",
    "    print(f'y_rougher_{suffix}.shape = {y_rougher.shape}')\n",
    "    print(f'X_final_{suffix}.shape = {X_final.shape}')\n",
    "    print(f'y_final_{suffix}.shape = {y_final.shape}')\n",
    "    \n",
    "    return {\n",
    "        'X_rougher': X_rougher, \n",
    "        'y_rougher': y_rougher, \n",
    "        'X_final': X_final, \n",
    "        'y_final': y_final\n",
    "    }"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Columns that are not in the test dataset are not needed for the task. These extra columns of the training sample are to be removed. \n",
    "  \n",
    "We will use two datasets to investigate if the proposed outlier removal strategy is correct:\n",
    "1. Dataset with outliers (**df_train**)\n",
    "2. Dataset without outliers (**df_train_full**)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "columns_to_leave = df_test.columns.tolist()  # leave only columns that are in df_test\n",
    "columns_to_leave.extend(('rougher.output.recovery', 'final.output.recovery'))\n",
    "\n",
    "df_train = df_train[columns_to_leave]  # dataset without outliers\n",
    "df_train_full = df_train_full[columns_to_leave]  # dataset with outliers"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Test dataset split"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In order to estimate the model quality we should know the true values of the target labels. They can be obtained from **df_full**:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_test = df_test.join(df_full[['rougher.output.recovery', 'final.output.recovery']])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Checking that the columns are added correctly:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>secondary_cleaner.state.floatbank6_a_level</th>\n",
       "      <th>rougher.output.recovery</th>\n",
       "      <th>final.output.recovery</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2016-09-01 00:59:59</th>\n",
       "      <td>-499.709414</td>\n",
       "      <td>89.993421</td>\n",
       "      <td>70.273583</td>\n",
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       "    <tr>\n",
       "      <th>2016-09-01 01:59:59</th>\n",
       "      <td>-499.819438</td>\n",
       "      <td>88.089657</td>\n",
       "      <td>68.910432</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-09-01 02:59:59</th>\n",
       "      <td>-500.622559</td>\n",
       "      <td>88.412756</td>\n",
       "      <td>68.143213</td>\n",
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       "    <tr>\n",
       "      <th>2016-09-01 03:59:59</th>\n",
       "      <td>-498.709987</td>\n",
       "      <td>87.360133</td>\n",
       "      <td>67.776393</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-09-01 04:59:59</th>\n",
       "      <td>-500.856333</td>\n",
       "      <td>83.236367</td>\n",
       "      <td>61.467078</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     secondary_cleaner.state.floatbank6_a_level  \\\n",
       "date                                                              \n",
       "2016-09-01 00:59:59                                 -499.709414   \n",
       "2016-09-01 01:59:59                                 -499.819438   \n",
       "2016-09-01 02:59:59                                 -500.622559   \n",
       "2016-09-01 03:59:59                                 -498.709987   \n",
       "2016-09-01 04:59:59                                 -500.856333   \n",
       "\n",
       "                     rougher.output.recovery  final.output.recovery  \n",
       "date                                                                 \n",
       "2016-09-01 00:59:59                89.993421              70.273583  \n",
       "2016-09-01 01:59:59                88.089657              68.910432  \n",
       "2016-09-01 02:59:59                88.412756              68.143213  \n",
       "2016-09-01 03:59:59                87.360133              67.776393  \n",
       "2016-09-01 04:59:59                83.236367              61.467078  "
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test.iloc[:,-3:].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5288, 54)"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Split test dataset into features and target for rougher and final recovery predictions:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_rougher_test.shape = (5288, 22)\n",
      "y_rougher_test.shape = (5288,)\n",
      "X_final_test.shape = (5288, 52)\n",
      "y_final_test.shape = (5288,)\n"
     ]
    }
   ],
   "source": [
    "test_dataset = custom_split(df_test, 'test')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Training dataset split"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2 training datasets are used to train the models (**df_train**, **df_train_full**) to evaluate the proposed outlier removal strategy. **df_full** is additionaly used with feature selection to investigate if this technique can improve model quality. The following training datasets are obtained:\n",
    "1. Dataset with outliers (**df_train_full**)\n",
    "2. Dataset without outliers (**df_train**)\n",
    "3. Dataset without outliers with feature selection (**df_train_fs**)\n",
    "\n",
    "\n",
    "The results of models trained on different datasets will be compared and the best approach for training dataset preparation will be chosen."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Split of training dataset containing outliers (df_train_full):**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A separate dataset is used to evaluate the outlier removal efficiency. This dataset contains all outliers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_rougher_train_full.shape = (13959, 22)\n",
      "y_rougher_train_full.shape = (13959,)\n",
      "X_final_train_full.shape = (13959, 52)\n",
      "y_final_train_full.shape = (13959,)\n"
     ]
    }
   ],
   "source": [
    "train_dataset_full = custom_split(df_train_full[columns_to_leave], 'train_full')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Models trained on this dataset will be labeled with \"full\" in this chapter (e.g. LR (full) etc.)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Split of training dataset without outliers (df_train):**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Training dataset is split into two X_train and two target y_train samples (for rougher and final stages recovery):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_rougher_train.shape = (11337, 22)\n",
      "y_rougher_train.shape = (11337,)\n",
      "X_final_train.shape = (11337, 52)\n",
      "y_final_train.shape = (11337,)\n"
     ]
    }
   ],
   "source": [
    "train_dataset = custom_split(df_train, 'train')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Models trained on this dataset will not have any naming indications throughout this chapter (e.g. will be labeled just as LR or RFR)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Split of training dataset with feature selection (df_train_fs):**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As there are many features in dataset, some may be uninformative. It might be useful to remove those features to improve model performance. A simple FeatureSelector library is used for this task."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "fs_rougher = FeatureSelector(data=train_dataset['X_rougher'], labels=train_dataset['y_rougher'])\n",
    "fs_final = FeatureSelector(data=train_dataset['X_final'], labels=train_dataset['y_final'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***Collinear features:***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5 features with a correlation magnitude greater than 0.95.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "fs_rougher.identify_collinear(correlation_threshold=0.95)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7 features with a correlation magnitude greater than 0.95.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "fs_final.identify_collinear(correlation_threshold=0.95)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***Zero and low importance features:***"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Gradient Boosting Model\n",
      "\n",
      "\n",
      "0 features with zero importance after one-hot encoding.\n",
      "\n",
      "51 features required for cumulative importance of 0.99 after one hot encoding.\n",
      "1 features do not contribute to cumulative importance of 0.99.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "fs_final.identify_zero_importance(\n",
    "    task = 'regression', eval_metric='r2', n_iterations=10, early_stopping=True\n",
    ")\n",
    "fs_final.identify_low_importance(cumulative_importance = 0.99)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Gradient Boosting Model\n",
      "\n",
      "\n",
      "0 features with zero importance after one-hot encoding.\n",
      "\n",
      "21 features required for cumulative importance of 0.99 after one hot encoding.\n",
      "1 features do not contribute to cumulative importance of 0.99.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "fs_rougher.identify_zero_importance(\n",
    "    task=\"regression\", eval_metric=\"r2\", n_iterations=10, early_stopping=True\n",
    ")\n",
    "fs_rougher.identify_low_importance(cumulative_importance=0.99)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['rougher.input.floatbank11_sulfate']"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "['rougher.input.floatbank11_sulfate']"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(fs_rougher.ops['low_importance'])\n",
    "display(fs_final.ops['low_importance'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***Removal:***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "All the collinear and low importance features will be removed for the resulting dataset:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Removed 5 features.\n",
      "Removed 7 features.\n"
     ]
    }
   ],
   "source": [
    "X_rougher_train_fs = fs_rougher.remove(methods=['low_importance', 'collinear'])\n",
    "X_final_train_fs = fs_final.remove(methods=['low_importance', 'collinear'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "# building a dictionary for the FS dataset\n",
    "train_dataset_fs = {\n",
    "    'X_rougher': X_rougher_train_fs, \n",
    "    'y_rougher': train_dataset['y_rougher'], \n",
    "    'X_final': X_final_train_fs, \n",
    "    'y_final': train_dataset['y_final']\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train_dataset[\"X_rougher\"].shape = (11337, 22)\n",
      "train_dataset_fs[\"X_rougher\"].shape = (11337, 17)\n",
      "\n",
      "train_dataset[\"X_final\"].shape = (11337, 52)\n",
      "train_dataset_fs[\"X_final\"].shape = (11337, 45)\n"
     ]
    }
   ],
   "source": [
    "# compare the numbers of features in \"regular\" and FS datasets\n",
    "print(f'{train_dataset[\"X_rougher\"].shape = }')\n",
    "print(f'{train_dataset_fs[\"X_rougher\"].shape = }')\n",
    "print()\n",
    "print(f'{train_dataset[\"X_final\"].shape = }')\n",
    "print(f'{train_dataset_fs[\"X_final\"].shape = }')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Models trained on this dataset will be labeled with FS (feature selection) in this chapter (e.g. LR (FS) etc.)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Metrics calculations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The sMAPE metric is set in the task description to determine the quality of the model. Smaller metric value means better predictions by model:"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": 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3N6MsBiCpu4dai0EKrxN5rojRE/JPselc18VoIAAAiDMEGgAAoAtayfpkMhk4gBwghJrUcPv2be3R8CKR55C5iZDxAQAAMYdAAwAA9C6bzY66CTBmtGwXM1B1znvR5vZZWFgQt9XaFlGeCwAAMCooBgkAAF2THZ5sNru4uGjbtrZArCZ6uHfv3qibAMfUdIZMJuM4jvpovV5v14s+J7T6JisrK/K2iCPs7u62y/hAbRQAAIgJBBoAAKB3ruvm8/nAMg3r6+vxiTVADDmOY+45gSUbojiTF/PX1tbUSqtCoVAIf1aUqS4xHSYAAAwUAg0AAHAqhULBcZy1tTX1ztnZ2Z2dnTt37qgF8+E8SyaTavJCIpFYWVnRcmFc1z3Pkymk0+m5uTnZ/3/nnXdSqZS2TLVaDZynQ4oYQUCUAQAAAADg3HEch59Ur9dH3ai2bNuu1+vcEJjpAGfD9b8r3brLb6TCOr2qUqlk7iGajY2NrtowLl+QiO7fv+/7fvgm0kaahEsmk+pzi8VilGe9lKzcusuvvp7r9X0AAACgGCQAAJya53nLy8tm8kI2m1VHmAOEqNVq+Xx+1K0YJcZY+ACQfD7f83wcgSkMZ3K8CQAAxAGGTgAAQB+IXqKZwlAoFPb29kTvCMPCzzO1T6tNkkpErusuLy93u045HcN5sL29nct1zjJQv2XValXdRGooUCyG7yMAAAAAwDkyXkMnpJ2dHTPZe39/f9Ttgv7reeiENjqg0WjkcrnZ2dlBNHK8rtgHjj8Smyibzfb2Xnp4FoZOAADA6SGjAQAA+mZ1dTWRSGjDyBOJxObm5vr6+qhaBXHguq6sYsgYq9frh4eHtVqtXC4PrmLoeF2xr9Vq9XpdhgZc1z08PCyXy7VardtNJPMaxmsLAAAAAADAAI1pRgMZ9efkFexkMjnqpkE/dZvREK6/qQfjlcgQN8hoAACA00MxSID+O/05bldrUBfG6TWMXLVa1Ur6id1yZ2enq4L5EHOME5GvXjA/za9Pfy+84zJ+Nyzjhs8YcYYDSuxE+URY+Dfx1B/pSPYK+aLDf3V8CwB6hqETAL1YWVmRxx7Oubxdq9VqtVrEc1zHccQ1XjXHVayqq1xirar/4eHhzs5OxOfKNoTgnB8cHBCR67pRCp5nMpmIr97OQFOpxwXnXOaZCz1Xmx++XC63uLio7Vq2bReLxVQqNapWQX8dfvqrC3O3H/7zFhERI+KEzv148onEJ+iLv5/86YOpme89vF9EvGaEGAV8oaJ8Ijzk+eLhdg9FE96GwIq/iUQikUjYtk1Enue5rtvtaCC5TnGaJOZ5Feucn58/ODhoNBr37t3r7SgpI+CBT5ev2NU6UfkYAAB6FFivi3NeqVTkMh2j4MVisd16oieZp9Np8+mJRKLjE0XzzCx3M+PdfI/ZbDbk0nTICiNCjv24Y4wtLCw0Gg3zw81ms6NuHQyOhWt/YynoY2NsouMyMFD6R9Dt8sEfmdX+oQ4ymUy9pVAodFw+mUxubm4GHgjEuUT0yxLyhMq27Y2NjXZlU+v1eiaTiZg65zhOsVh88OCBfHqj0SgWiz1k3jmOU1fcv38/nU53uxIAAAAiosAeuCBi9lG0O1KqPW0zWqHdI6MVapPkFGgdgx2BgYaQd6cqFovz8/PmOqM8NxwCDSHGKI2zXRgLn+9ZMja7I7TBzFR7pj4q/jCHV8BoBH3jjj8dNrAPSPSl1ROAkIVt297c3IxyuFeH1HU8uqXT6XZhC5UIN4SvamVlRVuVPPNpNBpRwgRqa/f399VVhW8cAACAMCFHuIgR+kQiEbKSdj0x7TBs1guUh8mIbyQ8o6Gjer0uThHUhp1mhQI6omdGoVAI3G2ix+MAYLDMzl2Ue2BYmodXJv8MTnPo2Es/zWfoOE5IX9o8M5ELR7lu4bpuSBKBXPnGxkZXJxIbGxvtVqWdO1UqlWKxqL7BRqMRJTNUrFM0TL5TeV4EAADQi8CjmjjMlEqlKGvI5XIhh2FZuyF8JZlMpt0hNmJfXQQaIqYwBNrf34+ycbpyngMNY5SwEMhsv3Z6KkQvIwJjZrz3X1AEfpT4fEdk5saSNdldfJZNHS/f25HFcZx0Oh04/CHkor34zVfPKxqNRqVSyeVyuVxuZ2fHTOc0O+dag9fW1szjSKVSKRQKuVyuUCiYBxrf99uN1FMbIDNAiSifz6srD9xuHa/3nL5MFQAAnGuB3WNxWI2YTRDY+5Ii9rQrlUq7NZgducDzjFQqFf7Stm2LkkvpdLpUKgUO91CP0+bGKZVKyS7hcneIMYpEiKY6jhNSrGGM3g4YkEV/1qi1/eWICsaYhQDDSF2eX7l1l9/8YY1N2SEfhXyEMTY5c3P+R+7172/38HJRcgfee++9iM/d2dnRjum2bctrLZJa4koTONnz4uKitpgoIaEtaSYmqImc5XJZe1Q9rUomkx2PUNorYtAEAACcVvgBuGOYQDtqqt0wEa1Q19DuOLewsBDShkajEaW7bg6dCG+8TIlUL1ZoSQ3aCrUwBHRrrLviovErKyuBu2jE3FSIL0ZE6ISOPRb6K4OPdyRklOfy/Morb/Nbd49eu8tv/rA2NTUnHmNsot1HMzlzc/5H9Vt3+Stv8+vfV7u+kYKDZhTAJHvU6r5jRgRCktfMVzFzAcTKtWsqIWMTzCLElUpF27vVAX1mLYZsNisf7Vi6WIuqiFGBY33IBgCAfop4SNAW046OWnpCx2rM2pAHMzEhSkaDNmnFzs6O+mdI0qCq20ADEdm2bV6gVqtCag8NM9DACCfFMYViDQAA3bq8kLl1l4uQgbhx84c1bQyFdtCbnHZu/rj+6k/5a61nnYw1dGaGAMzxlYGX7rXTEll8IfBEizFmRhDMxcyzlPCxCeZUXNpZjfqiZrBbjZWEpyeYUZWIg14BAOBcSCaT2Wz2vffeq1Qq1Wq1Uqn88pe/lHMjhRwttKOLCIHLI7E5FbO2Ku3gapZaiBJo0BL2EomEtlqZiBjyRnoINJDRafR9X70soK1wRBkNPSV14/RgYGzbDhx3s7m52e4pOF0DgPPsBfuOCDG0ogxHr7zNX/2pjDUETAUyOe3M/8h99afNqIR4+mt3+bV/FzzSIVA6nS4Wi6VSqVgsFovF9957T6uVyIM64aLjrYYk2kUE5G97lDMQLXhRr9c7HhrUcyHf97VBGeobMZ9r27Z81CxBpS6mHdHM2pMhbxzgnJgcdQMARiOTyWxsbJjZd+KwxxgrlUr5fP7g4CDwUKRpNBrValUeIOfn5xOJRK1WkwuoK7FtWz2Uuq5brVa7bX8ymVQb77purVbb3d2VaxYHbNGMKG8hEGMs8LnqWxOLxeG6NJuyv3N70z/Fcfzrh//l8J9Ev9ci8hlRjxsODJ7nLS8vVyoVbVfJZrMHBwcyCUjd5cSNdjshAMDZ9rW33/hd/tt/veEzYoyILM6JM5qau/1yqvqHapI/8xhjnPti+clp56Xkb6YuzZMvlifOifnkW/T86e/FMlGOa+Vy2SxekMvlwge7qdfzxXmUuRJB/qRXq1X13ImIVlZWtDMibXSDqNcY3n71XIgxlkgkbNv2PE/cE366IhcLX1I7gXRdN5/Pyz/lRtCehWMZnDeo5ATnjm3bYiqjdmP8xBFCVBVaW1uLEoFmjO3t7an3hEzCrAXsu4oyyMasrKyo929tbYngiLZklLmgQ5gHRbFakbLR7oJAuzYPmnXBnvle5vJC5sr3MvZC5nL3/8382x+3Gu0zxprvDVcg+qRWq6mnYpJ6xoYzMwAA6cHHub/8Li9+BZlPFieLExFNzd3+brLKpmz5Czk57dxIVS9cXmA+EfkixGBx4hb96+/yjY9+IRYb3O/p0tKSvM0Yq1araqe9nZBzJ8aYWRw6yimTNvmXdnWnN+qZTCaT0YamplIp9c+IZ0cAZx4CDXDu7O/vq4ec8IPB5ubmO++8E2W12oHNrIcsqQdjItrd3Y2yfkG01rZtLSOxXC5zzj3P047Ba2tr0VfeVQNI1A+LEEQY7uHWnyDinHzWPBvrkkVExIgRE7cZIauhnwqFgnmNy7btX//61yNpDwBAzD34ONf4XZ75xC3yGXGrma0gYg1iDMXUzMJLb1UuTM9zTkS+zyyi5sIP/lFEGZRsv8FEz2W+gzjoRzy30U5abNuWJZ/MkRSu67qu2/HEw/M8bQRryCmZSkvPVFciz2Qcx9FGSeTzeXPArFxhlNcFOKsQaIDzxRwusbu7u7q6eufOnTt37qRSqfX1da0cUaFQmJub67jmg4MD9UgTMkejeuD0PK9dbmEILU+hWq3Kl97ePjGXlQzk9/dop8VKeFBZiog6TlIdnUWMMUucTDHf72kMhU9ExMX1iCNxExkN/SI+2dXVVXNvuXPnTkixBgCAc0geDR98nPvLx3miZgBdRu+n5v7mRrLygn37peRvXrjoiBEWPrOIfPHUB/+Yf/BxThzauFxhr9HzkMsGc3Nz8uRKvIo2xLIdsw7CnTt35O3bt2+rD8lsyo6r1RIlFhYW5G21YTdv3iRlO3PO1SUPDw/NNedyOTUFz3XdkEJUyGuAcw41GuB80RIBlpeXd3d31SNBtVotFAqlUklMyCfC22trax0rGnLOt7e31Th3JpMxp5/Qaivs7e11269mjGnjJtTgQrlc3tzcVGMca2tr1Wq1j0c7x3G0iwyMsZBAw+3bt0MqQpkNU+MmXeF0xDlNEB0R+cyyOHUba2BsorUqpW04T+gTsT09z1tdXTWnTM9ms+VyWTs7BAA4t7g8/DBqfJRjnL71xgbnxNhxrOHFucTL//63pITELU4+szinxu9ElIFE1SHqpt8rj4DqYTrkdEWLCNDJYgchHj586HmeetKiniNpF2zu3bsXZZ3qq4sTObV59+7dk1dr3nrrrVKppG4WuSTn3LwOlMlk5AkY5/zw8FAbNCGhuhAAwPmiFhPmyqQMprm5OXUGx0ajoS2g1UkWHWmtfnLg+rX5GjKZDGPMrNXcbjyhXFil5WiIo6ba+JCCRt3OOuE4jqjYrLb2N7/5TcjGCWdOmhU+c1Uw8+SnpzQE5C4MjZyrXN0BGo1Gu8opAADnluzhf+uNvDbbpZhgQpmZonnP1ddz1NdkRm3OS23WCfNcIkoqqKBN36Be1xEPycNE9Ems1tbW1HWqZ3FqU83zNLUx2vHIcRytqevr6+ZLO46TyWRyuVw2mw1JbgU4D5DRAOeFOvSOcy567O0WbjQaavJeR5xzIhKlj+SrJBKJ2dlZLfVOGygoaivIFkZ5Ia0fLq//y/C5SMeg1tsUBR203Aqttr/60ktLS+aWYYy9+eabiURCreQsn/v+++93bHm7t9Of0yD9soFFrSrcp1pNZ83LRERkTdoz302zcbuA4X/94NG/fDD81y0UCul0enFxUd0BbNsuFovyAlEMrghZxHxram7mxpL8ZDmjsfuUAWDkHv+5cvT0gCjaxA8nl5K/hP/60YZP/OobzdxJmbin/lL67DiXYWg/oeZpg3mFph3tNEnWaCClIJT4M3q2o7ZOtbevTnWRTCY3NjZklWJ1WESpVJIvJw5GWhHxUqmkjfgTa5OTl4k7Pc8rFAqBhZABzjwEGuC84Jy7risCAeIA4DhOLpczA+TiiCJ77xThUC2PKNvb27L+om3biURCpoIzxsS0l9TqYEesyaxpN25CNlLGO2SrlpaWtECDaABXEiPlwmot5fBAgHj61taWVggzuoHVSfIp6rmcwWLkn3xe2Ip8am3Ab/93hcvzK4wR87sesjFCFqcv7v2998kI6iNkMpn9/X1x/if3tOiFWofCZ5xeSlZesG8TiXRlf4Kso1E3CwDGzrVn3v3yVc55xCNTu6UaH+WISMQatEOoOJg/+CjX+DjfOnIdR8NjKyQkMTs7O4hXXF1dlUefXC6XyWRqtVoikZBxBG26Ss752tqaODyJo5W2ABGtrKzIcyEtgJ7L5dLpdCqV6uGUDweJaZQAACAASURBVGCsIdAA58vu7q7aUX/33XfFpM17e3u1Wk2UCFK7N912dcrlsjrRw/Lysgw0cGVUgjgIaYUbo9BKPJBSsVm9/KvGO8SzEomEVpnJzGgwwwrhgQDG2Pb2tjbJU3z02En1jed1WpHYkl8/+O3Fb8tO8sh7yF149vj+SF7Xdd3V1dWdnR2KcWluTvTILU68miUS8TjyOfOR0gAAXXr0x13jjCJSFEDGuptHeUZf1ktXX1vjF+yjk/UamM+ePXEffvIr1sxxGE2UQX2bp0xM69ehQWvDwcFBKpXa2dkRJ1SO46j1HQ8ODpaXl9XsCcdx5NUa0SRtpgnHcdQrLsVi8cMPPxQVvsSaE4nEu++++/d///d9eTsA4wKBBjhfcrnc0tKSTKITAygymYwcj1CtVmu12t7eXlfpBmY2gfhzZWVF7YdryQjarE5RxhFoa9jd3T04OBAHcvU4qsU7iCidTrcrAS1ftKsjugjnR8ll2NraMotihugt5M8Y0eSVa4mtHp4rPXv4j41/+qWx5gkxA0W4w09/dfjpr07z6ufQu+++O+omdOZ9suV9cqr9CgBAw4h4N1EGEicJRBMXnRupqjVp+60ZKESsgTEiTlOXnBvJyh+qSXrmtU/uG2wAQj2XiBJl0NIW1FEPruuqF1ei131o1yR5vlSr1VKpVC6Xe/PNN+VQWdd1t7e3C4WCdh6ilXIolUrahSK1EPgvfvEL+WepVNrf3xdvYX19/YMPPtBO/ADONgQa4HwRF1GLxWLI3JOLi4siOlAqldSgdUhgXj2sqkkTtm2nUilxiJIzTQrm3Aod+/mzs7PaxJZqiQdVtVo9PDyUB2+R9Re9ilI7nufVarV79+6Vy+XoB0tzRutB4ERTF74162SIyGfNDNJuV/L0iz0z0MCZH56gwNgE8SNc4+7Wu+++a1ZCGcKuAgAwchEPGa0aTs1bE9POjVR1cmaeMyLerNEg/uW8OWf9C/bt7yarf6gm+TOPiIhNkB4r73+Uwfzpnp+fPzg40O4MHI6qhQ8CJ5UUrly5QtGyJLTcT9k89Ymu68qLTI7jtDv6aNOii6ss6nrE9Sr5qBp08Dwvn8/LwpnpdBqBBjhXEGiAc6dcLtdqtVwup2UHSLLDLzIdcrmcGIkXMf2vWq2qa15cXBSBBi1GsLu7223Ll5eXtfiIKDNBSpBeNr5er4t6ENQqhJlMJs1xFuabWl5eDsx96LkHOISsePF2RDyAW8enX90mbFrmuAmijmuJku8AmnQ6HVgca3V1dfiNaUeb1y0GNSMA4EyJ+sMiogwzzo1k5cL0vN86Lol6QD4j5jejDJwTZ/Ti3O3vJqt/rCT9595wjlBmTGFubs68M/AXVQsK1Ot1ebtWq6mPittRtph2stTxBMZcQLRQnmVJ6+vr2sLyXIuCTu3K5bIMNGjlwAHOPAQa4DwSYexcLre4uJhOp9UKQKZcLnd4eBg9+b9cLm9ubsqD3NLSkjhKaQeYHqLaZmREDZx3HHmxtrYmXzRkqou+JyAMoXvGiRNZTNTfbp5+MeZTtzkNahHH43TT8HSG8arH0NZQh/I6jrO5uWnusfl8PlZXe06Ua2l+zmNQWQ0A4q515OhYGVJ2yCdmnJeTlckZR+QyqKUZnvy5evF6UtxJRIyRT/SCfftGsvJZNeUfeUM4SqnRAfHbblaGUslfV7WXLqhnIFp2g7lwO2+++ab6Z0iWRHgLtUETW1tb5XJZW1JtlXkIE+dUslJDt80AGGvWqBsAMDJiMN7y8vLCwsLc3FwqlQrs54ipm9VJHNoRC3iet7+/L+9MJBJiriY1o8F1XfUAHOWav+M46siLdq8e4izP58yJyOeMGGueafUa3GiOaKXu8lpbP6QxLWjYAWNM7TwP4U0Ui8X5+XltjxV5RubCI6wTyRg73hxc/IkoAwD0ghEdHyyO6y50OA8X3d3JaeflZGXqkkPk85NRhsZHuT9VU42PcvKXUiY7vDiXeDlVnZqco2YAYmIQP6byJ1qe1Yh7tE51u19yNUlB/KueHWmhioWFhYit0sbl9RbCNgdNaAcpOYWZvEcWd1Dfrxo6USfvBDjzkNEAQETkeZ6YWplal1tlXIAxNjs7m0wmzTC2Rkbod3d3U6kUteL6YjCC2snXkuuiXPNX4xRRykaabNvOZDJd1WUcL0eP3X/+3/pwHtV9jKLV+RzP3AZt9xv0mxDTjGt3uq67vLwcuPwIByzoWwZDJwCgV6L7b9zdIXbJ1LoMnCxuMUaiJhBj9OAf842P80T04KM8cTb3180kR94aPzg1d/s7qcpnlSR/9nBAYyjkD+Pe3p4MLnDOtSzOdr+fS0tL1OqWM8ZqtZpaiFGkiMo/xcmYGjUwx54wxm7fvq1dVgnJrWgnmUxqYQVzfsrAN2XW5wY4t5DRAOeIbduOQjsOya676PNo8e+QsRUmWY5YrHNpaUkb9dAxZmFSZ5Ho+RqvOKIDDJncY81TN8Ec9QoAcK6JFIgZ50aqemF6XuTr+YxktODB73IPPs7J7uyDj3ONj5q/rpbSyX3Bvv3y4v9pTV4ZdHvVExsxdELElMPPWLS484cffqiuxHVd7dCglbsyowyi+rV6p+u63WY0OI4jCysI2nyWIRBiAJAQaIBzJJ1O1xU7Ozvqo9qx4d69e+qfXQ06EPkR8s9kMqkeSuUxL+ToyxhTH00mk1qkI5VKsQi0g2symRzyEMHeki/gjBFfLvPUTcjn8z2E3gAAzjJObMp+OVmZujQvahtbrSoMRPTgo9yDj/R6ug8+yotYg8+aJYcYI4vThat/eyNZsS70ODdkROak4OISS0jHO5PJaOc2In9BnZxCm0hyZWUl5HxMlG/UghdRogzaiUrHQRMqz/Pkewxsm3qnmJK8Y3sAzgYEGuAcMXvdIXkK2jg6Le7QkTY4Qn0hsyKjScu70xIiOobn5WHMLICsXQ3oL/PwiQMqyH2gWCya37jwEzgAgHOLP/Me/K758yjqMogCDY2PcmLERBOT/7cefJRXAxDMPy5yPIQr7VtbW+qfmUwmpLaU4ziioLVsWKlUElkDalMLhYKIX4g7bdtWy2BLcvDF2tqadqAJnOFIo76imHFMfVQMhm2nVqvJw5ysIqGe/MgLPGIEB1IeAADOpkqlIvrwvu9zzuv1emCsYWNjg5+kRam1R7VjEhE5jiNfRWMed+fn58OXqdfr6qOBl4U14iBn23aj0VCf22g01MWSyWTH5nVLW+H+/n6xS10NVIGYE7ui+Z0K+QICAIBwyVl55W1+6y6/dZe/+lN+9fX2kVnRt7XY1Tc2Xv0pv3WXv/I2f+VtfvOHNWvSptOF/nO5XMfzENu2tdOVdj/yjuPs7+9rR4R2hwPtpTnngbEGCjrQRDlf0hqmvYWOcQr1fE+bpYKIFhcXe24MAACMk0QioXW8OeelUimbzSaTyUwmk81mZTBCMq+4aguYgQYiMg+i3OjnC+IopVJ7+5lMRnu0q+EP5XJZfa7v++rKhxBo6EH41QMYO4lEIvCDXl1dHXXTAADi7pKzcusuf+Vt/+rruTahAktLUr76eu61u/zVnx5HGU6ZxRwl0EAnTypE37vRaKgjEWzbXl9f1zrzPOhES5qdnTWXLxaL2smMefJWr9e7neWhWCyqazADB4HUl9ZOotQVnv78CgAAYk302wNzDbSjo6DOVSlpywcGGswYPA86NjPGwgMN2rFTna06imQyqb4d3/fV8fD9DTTIcZWnhIPx2SD2h/n5efMckYeeVgIAgOqSszL3xsnfTKb8G2TujdzNH9YmpubkYnJ8QQ8NCA80qOsMPPnhnHuep13pkScnpVIp/NXNRAOp3f2NRqPbjLm1tTV1Dffv35draLfRxP3qqVS9XhfnMLZtb25uqvd31RgAABhLoiqkeVgyow+lUkkMmtCOMdpigYEGsw8fuGR4oMF8qNvuWeDoCTkSJJ4ZDQg0nCU7OzviY1W/X+KUCyU8AADaifb7aBGRpS3b+suatFuJDOq/vYiY0RC4cDit3GM7IbEGU71e77b0teM44mRJHqpWVlaiH6QKhUJIe3qIegAAwLian58vFoshB61KpRLS3dUWzmQygUcjc5hGYEViM5ogxw6srq5qD8lSQ9GZx79sNiseMgMNYu7r0/QAQ461EWnzb59V4VdITrOG+NBGzPq+7/t+o9HoYTc+h9TPV714Ke+P58cf0Kp4NvQUGJvQbiiP9fFV+r/hxv+jsIjOwtsYO10FGqhNXEC7nNNoNAKv07SzsLBQKpU6nkIUCoWuZgoTtOGuPdRTaBdeMQtVxP/YDdAX2NHhvBMzPjqOMzs7e3h4KCZtNmdpCiembo5yZ2yNV2vPgHYnMXt7ex2TSCWRmandGZ/CB+JE07x/dXU1+ns8hxib4PxIpDirBcpfsG8//+bB0ZM/jLBtUTCiVputi9d/8OyR+/yJq9x5tjC6eO2tZ4/uP3/qnuYdqr/A4b/GffmttibtF+w7T/+yx4g4+adc21AxIt48eT2be1RcJZPJVCol971arRY+LbHYUUX1q9u3b6v5BeIsa3d3t9tzLcFxnHQ6vbS0lEgkZEDB87xarVatVre2tnpbpxbykFNg9LAeebHEdd3d3V3M3wwAAAADJ69jhFyNiZhgGTjoJj7RIvVylriKJf4tFAqjbtpYmrmxdOsuv5HS5rWN9RzVk9POrbt8YTmgAu6ZwaauvHaX37rLW/X2hvKiPV0OVZ9z84e/feVtfvHaGOaODf4CGa42d9TzJpJxgb5sZLGSISQL9Gud2LXgvIn1OQpAnDHGwo8ZpzmiDO5oFFgLKvDl4nBEjEMb+is8ECAebTdxV8dVjTbKoH1YuVxOK6PFGHNdVw7bgRBii1k0Ibfp1OQcEU1OzyvLTFCcr0UzmpxZIKKJC8PrgQ/f5NRVnxERWcrbjPjD1e0Prxqm7KqRzWepf7wwR8QnZ271sJ7Rmn5paf5Hrv3ael/WFri14xOuja2eN5HMNRBr6PYQb1bLIiIt72AQH99p1qm2GbsWnDcINMB51Jfua8erx6c5ooTnzWo3+rt+ueY4HBHj0IY+ihicymQyUcphmqsabVyGcy4b8O67766srGgLeJ6HiUsjEnu+T0fyMC16s5xzeUWX8yMa9YfenkWciPyYtq6PGGM+WSd/qDr+cIX8xoY8V/2KnRITvbw4B6rauOJkpi7NT7/0E7MAYre0QShx/SqdBSfKzXTf8T5liG34n2zH94idDc4JBBrgPOr2WBXPQ0KPF7VOPivwfPeMde/jI/rQhihJDVFW1TG0MQiO4+TzefP+fD4vLz3F8zsVQ2L8vNhYE0QTjDN+Ins8rt/WZrNj2rr+8enIZ8ROvNXOHWDzU3Mcp1Kp1BX7+/vpdLrjE3s2dh8NY2xy2pm5keacvjzYZoyfskgDDnxDo27bHrbzKT+a4X+yHV8ROxucEwg0AHQWq0NCXxpzyusDMGjJZLIvc3wOuWoD59y27UqlYj60tbWlVmfoLW/23OJEPvEjoua18zH51nIii49fh7YrYtYJzkkJ6fndfkCJRELMc+QoEonEzs6OmM+4798UxhiR71PfUiSGgHM+fT1JRP4z71G9hINXHPQ8gHSMdjyp5zaP45sF6AsEGgD6I3rtg44rGcJi7U7RAt9FTMSzVYMTsVJD3GxubprFLF3XFf0lDboK4dQ9/rRp4qNjna0PWf8Y+BFjxC3izOfmoxGsrKxUKpXAErCc842Njc3Nzb5/UzjnjFns9FkBwzX7V+tE9OSzXd7a0P06LiwsLMzPzzuOY9t2nI+DcdPVANLxqlZg7gA9j12K/5sFGBAEGgD6QxxITpmKGXH5/i5mPiWeB8V4tmoQxDvtV1LDEMhzr7W1NXNSdFGaoYf5xs6m9h0ks3vT3z2+4ylyV+fQURZm3PJZs7rE4FnmKc0g+opGCVaLK1kb3X5km5ubpVJJluLXiCIC2Wx2f39fRCL69Y44a7W5mxazE/8bnOBT08mZmxdm3ySih25JNrur44K69Wzbzmazv/71r8XkOPfv33ddt16vNxoN3/f39/ffe++9kF9ghCF6MF4H8W5LqAAAAADEgjktZaFQ0O4JHIOgitX0lmZjxHyWmGZCesG+c3khc3l+9YqTEf81H4hwefaKk7l1l8//qN5ugRj2fC5+O3XrLr919yyfmospPG/d5ZMzN0MWMz8dMchI+8rU6/V0Oq1ODas+pE3jcho3f1y/dZdfXtDDgoEmpx25x57Yb4eGERF9O7EV/hXosI7WRnMcp1gsmr+cger1ulnXlmL5dQMAAAAAOBFoEB3y+fl5s3dhJgio4hNosG37/v37ZmOKxeJI2hNDk9PO/I/qt+4evfI2f+Vtfusu/zf/7njjdOy1dAw0xNCAAw2WmsXAGAu+Ej7g/mDEQINGhBLEF1/K5XJqakMmk9F+EDzP61fYrqtAgzVpLyw3bt1t7revvO1ffT1gJNSgLfyo/upP+bduFzov2t7a2lqj0YgYZZA/zmqUBwAAIpocdQMAAM47eXFsfX19Z2dHfWhjY6NUKo2iUR2IpG45P1wul1tYWBAP8dZA1nq9vr7en+nuz4Crf5ObnHGIiHOyOHFGf/m4OTeHNs3eKcmPhs5+ou/x7Iycc3MYQH83bA8CGyCS9qvVqnqnOieLUCqVqtVqNpudnZ2Vd96+fTuRSNRqtcG12cSfe4f/tDX31xvMJ86I+Wz2r9YeuqXnT9xWcGdg02SKn0ZuXfz24tSM4zPyPtnseWXFYjE8dBvw+owRkeM4+/v7qVRqyFseAAAAAKA75qWz+fl5IjKzqUOuYcYko2FtbS3wYiCuAUqT085rd7nMZXj1p/z697fVBcYio6HbdPHBZzS06O2SaQ4Tg3npY71lNIxcVxkNTElqEHkNr/6Uv5TsMLDrdE4mpzC69t8Xb93lL//73/a8RlFTM4SZUGYugN80AIDokNEAABAj+Xx+cXFR7dGJpIbDw8N4Xp12HCefz/Ogctwda0wMiNmY3d3d0ZaKmPvrjSM6vuLOLXrwu3fVBeL20QZeio/ZHqhcRefETmzD5kOcHw23SX2g5Qpp9w+5MRYxnzgn4s+9h5/8yn7jXSLinMiii9eTL15b/OqLveG07dKNNBEdfvqrE/eyDt8c2bC1tbXAXwDP87a2tkql0sHBgVgykUgkEomNjQ0zpjA/P18qlcalRi8AwMgh0AAAECPVanVvby+ZTMreski0DpweMg42NzfV1G5VfK7+tWvhcIhaevJPi9NhvfT86cEIm9RRzGIK7bW6mrxTnzM+ZO83cHgLbzP1z0g+EV/Zpo1PC/ZrP6cpe4LoiBMRXX0j96dqaoBta32ml52MNWUT+U//XD3xaKcYh3jIcRzx+6mFILe3t7PZrDYhTq1Wq9Vq1Wp1Z2cnkUicaA5jP/jBD5LJpDbsBQAAAiHQAAAQL/l8PplMqifEa2trhUIhnjNEbm1tpdPpUbci1q6+scEYZz4TEz1yiw4/bVvQ7vQXh9PptDldYrVa1UoABHIcx7xgW6/X9/b2TtOkfrOI/OaGErkMjCYvOj7xCWK8tcDzpy7x0ZdpoNZnuri4KOuYSAcHBxETf8yP1fO8crnct1aGsoj53zQefla+4mRkloia1NBvPtFx5OjyS0uM0ZM/fvD8iUsy/iACTBHCSxsbG+Y3olwuh9RrcF03lUrJWUUlxhgCDQAAESHQAAAwYtp1tmq1Wq1W1f5enJMazNbGR+CAjkGyGPn8ZLBgambhyvcynBNnZHHyGX3t1b727rVbxel7xSsrK0tLS9ob39raijJ4JJlMmhOFrK6ufvjhhyPvrit8UjYUJ5q86Mz/WK9e8V//97iUwxRtuHPnzuamXsiwVqvduXOn4xrm5+e1MrFEVCqVtEBD36MqMk1EpDY8drft+QxjdNR8Obp0438aTKCBmuEkImvGuXjjJ0T08LPmm+XHLWt+48IF/jp1rFMrRlXIj0z+mCwuLnbzLgAAzq+guaAAAGCIzM6weRK8trZmXpSLQyeKiOIZAaERzHXf7PPIz4UxdvFa6vhh5jNG3idbA23E1taW+caXlpaiPHdlZUW7x/O87e1tc08b+rYde6VSyUxKSiQSogpsuOXlZfVP8XFsb29ri/X9B0Fb3ZPPq/yoIaMMnNPlhXfYlP67dFrNPcsXDZi5lrS4dfSN98hV3i+3GBFFizLIrAS505ZKpSgJPoEJI/EZEQYAEHMINAAAxILac6vValovwrbtjY2NkKeM0N7eHnKJA3HO7df/k3KHxXw6Mc58AKrV6uHhodoGInIcJ5FIhO8wgeMmyuVyYPc1JkGuMeJ53u7urnn/8vIyYyz8o9HiRIwx13XFl24oPwLH54qN//q+eEHx+U9O2nPOap9f7eSeZb+a9Zn/9A9Kn59RlBCDIBJGtN018IMwqcGImPzYAgCMEQydAAAYPXNmylwut7S0pGYxZLPZra2tKBfihm91dTVuF/rUcRMj2mjWi9f+x6lLC5yTmEmAiL729sU484EqlUpra2vittwI6XS6VquFPCuVSpl3mpfN5WoRa+hWqVQyc0aWlpYKBb1mh7p5RQBIrR9JRDK0FzhFRb8wIk6WOsHH4z/t2H+1xhgxnxgj36IXb/yEPt2kk4v1y9TMwtTcbYvTofv+8b2t9xqlAKhIGNHCBPv7+x1fGns4AMApIdAAADB68jxYnt26rru1taVmMXDOc7mcWsAsPhfZXNeNZwREGNKGavV75Ic481Iz451zJgo0PP78wyE0pFwuy0CD1HFs+U9+8hPtHnnZ3IQ+WA+q1arnedoYqGQyadu2NqpC3bwizUTbh9UA0AA/C8aI+6Ts0l99sUfPPD5lc9EcThevJ9mUzZ8NpFSt/cZ/IqKvntSf/qVCjIi3whksWh1IIm1ri7jMwUHnOV845+ZoNRpZ1BIAYPwg0AAAECNqn6FQKKilGRhjKysrpVJJvZg5giaOoSFtKC7/36ygd+nlNBFNEPmsOf/i038pRytg1yPRIaxWq7VaTZucL7BDK9m2bc4eghExfbe9vf3zn/9cfEwydpDJZAqFgrxT213NJIiQANAgsONvkEXkf+Xde/HaosWJW80BFLMLq94nep3LUzhOjpi+9pbF6eln/5mIiNPxNyfyF6hcLvccGgjM8UGgAQAgIgQaAABGL7AnLMqei6QG2S3Z2NhA9y/uxPD1mZtTl+Y5p6PmPT5j1lcPaoOLMpCyI33wwQci0GB2aAOfGFiZv924CeiZTDZRMxTE6Anx2Wk/BbZtmx+NKDEwhNz+ZpOO7/CJ6PFnO9PfXvQZEW+WhHzBvt3Xl21GGV68tnhh2vEZeZ9sinQGTj5FGzEh7e7umnNzRHyuyPGR0R/xxIj1HQAAAMUgAQBGr925b6FQ8DxP7Ssmk0mZA4+MhrhRP8Xpa2+Jz8dqjqewvnmw7z/3iIixiUG3ZHNzUxvVzzkPmXtCPKTuUeplc7GS+AzVGV/VanVvT58PUiSbBG5eM82EiES0aFRf/68aNd48efSZT0T+pRuR5jTp1pXvrfqMnjX2nz9xGTHWCkB09bbNrRSy3dSPwHEckUui7vye5yHOCwAQEQINAADxJZIaxDmuPD+W00mi4xeuYzH/vlN7MC9+JylCDNwi0YpvGv/YXIwfDbolnudpHVrG2OLi4uzsbEiHVt3T1Cu35sX2+Ox7ajss7k8oD7Au2zmctxTYU02n04EdYBkbko/WarXRZu9/7d3jvLlL+4yILGvSnpzufy3Yyy8tMUaNf/4ViXK5fX8BI4KmvsjGxoa585TL5XaDjwAAQINAAwBArAUmNQQmuoPGnMtjCOTHdGH2Tb81EaD476uHNXOxwTFzvBljq6ur5jYRV9S1tpVKpUG3sC/km7E4cYuJEM4ENUtldLUD8KGcFAWOXgnMXCBlSIvcYba2tgbUsIj4M+/5Y5f5RGSJUJrP6OJ3+veLxIgRXXEy1qTNOX01yOlgA4erEJHjOGrZXSmfzzfbGJtAGwBAbCHQAAAQa57nra+va+e16mwU51yszvjVYfMv2Hes5k1/gsji9E3jHp3MGhioUqlkXn1VR0/ITWeWG6zVaoFzYQZe+x0Upt5QT1f0UxdlH2jeONL+jvIqRIOYoNEUmH6/uLg4Nzen3ZlOp82JD+KQuv/88J7I0+FWc2TQhSuJTk+KjBMnuryQ8Rk9/qz8bPDTwWps265UKub9hUJB5pJg2BoAQEcoBgkAEHelUmljY8NxjpOTk8lkJpOJQ5dDM/zJ5zu+3DCbpEYZJog4+YxZzLeOGBGjo2cNGmIXxfO8Dz744J133lHvTCQScu4J2RIzQaZdGcihfriitsWU/YJ9Wya4i6qaZDF2PMfH0dTMgpg9VJYnJKIXr6UYJ2I+8eBrKoyxrxq/bVbN6HLk/2kwxra3t7Vtbtv27du3tW+0WVOjWq3GYdaDZ8+8F+X2Z0REU1Oz/Vo5I5qYufnitUUievTHD5p3DXG/035sBdd1ZToDAABEgUADAECsiX7y6uqqdpFNnX5CHVgxWqIjOj8/L9u2sLAwoNcyIwiO41QqFcbY2tqaHDgwkmuP1tSVIyJiFmuOYyciev7YpeEGPorFohZoENNYbm9vq1EGs1slC/UHtnaYb+HyjfR3vl/kFnHuT5DFOfmMGDsOIHBOE0RH1KwaINt1I/Wbjis/+D8WRKCBH3dmrUHnNXDOy+Xy5uamlq2QTqfFt0Zu3nYBIHX7Dz+0R0TPH9WJqBncIWKMJq/2b+IJxqavvUVE9Mx7dFBsvsH+xRrCt9jGxoaYFkTzs5/9DNUZAAC6gqETAACxJs6Jq9WqdrXTcZzYVoVkjDkt5kP9epXAKIPjOPPz8zILfVRbZnL6JrW6vuLfCRp2RgMRVatVs3e0uLiotsGsDlCtVg8ODsTtwNYOuWcr5jYgso6oI9a2kwAAFDtJREFUGWXg3PdZs/IFtQZKcE5iEoToa+bHYyya3djh7DCe54lAmLol5QAWGWUwvz6iwKf6rIifRX/f1/PHB6RE0DiniSl93EfPOOezf7VORF/+sUzyDfZvjwvZYisrK7lcztxW+Xw+cDAFAACEQKABAGA8/OIXv6A2PZPRUs/LzXN0ec+AahPkcrnNzc1KpbK/v292zIbZJVbf+eSlW1azBqQvmvD1Y9dcbAjMQRDpdFotB6Dm54vNpeY7qOr1OlcMpr0BLG4xvxmvERUBJshq3fYZI4s3/+MWyRObjjOIMkZMXaqZEDHwCUEEUWtTrXlh27acuZYxZn67d3d36/W6uapcLqd+LoGVYvv7efHWTiw2OxFNzvRt1onJaefC7JsTRA/d95W7+3a+2i7msrKyElgAdXt7W4Z0AQAgOgQaAADGg0hqiFvyAgVdX/U8L5VKJZPJVColHx1Q13RjYyObzWrzJoyE+vYYJ7/Zi7WoldRgLjZQYleRgyAkUQ5A3E4kEmp0RjwlbrU/uEU+I859EvUZWhn7PiPGLLlbiRwHi5MIPXTc3Tgn1kp/YDTUKgBkJJuILb+8vNxqW0C8wPwoR8X/piFvM0b9/U26+sYGEX39+OCrL9Qkgr6NZwn8IWoXZajVatlsNoa/ugAA8YdAAwDAeOCcj0s1MlFXf29vb6BdVnH2L15FiNsg6qPWFXgaxeG23aAbUoZLmL3Z3d1ds9zgqDpajMgnzvnx8BNLHe1AxLkv4g5+K6+hOaWoRWJahPCGyyvzfOiZJhQ0UaXMYlADQDJ4127C0aF/OtbRN8dfNDmAxZpsRfpO0RxGdPF6koge/XFQURXGmLbFQqIMqVRKzC48oMYAAJxhKAYJABAj4YXKRKcxMDU6VhzHkTne6tCJPudvGwXzKpXKCDeOVqJPTH/gywqFfGQXRff29hYXF9XO1crKSjabpaDRN4GXzUfT0RIb8OvDr75oFiZgjInATbMGoc995jM2wTmf/k7Sp2Y6g2jsV1/scX5EZIX0w4++eShvD/8dVqtVbZ5a27aTyWS1Ws1kMvLOdpkp0tA/nePkAlEyQ0z2YV2w/SOvm+0YUHfzxW+nLkw7xOnw04K8s78/HWJVcp1ra2uFQsFcTEYZ+vW6AADnDQINAAAx0vF8enV1NXCcdqwEdu1C3tqAyjcMWav9FpHPOefcl3kMjJHPZAxiYmiFAIRCoRDYoXVdN5FIaAuHdGgHN4FIME5E9PS/7T75F9GktvNBTE473/txvRnWaW32zypvRcm3H+7MiSeYcUMxYqJarcpiDVK7CUeJKJfLDbWIAAtIWWA+PX/y++YfUTeoL9amLn/51gpj9FXj3vMnx2k1g/hlEOvc2NgI3HSIMgAAnB6GTgAAxFG7y7Cu64Z0OWKi247BkIsLDpjs3Fqtz9DnnBhrHnCHHGWg1kgW7c50Om1mf5RKpZDOlXNS/xsahHOZChIWNfBZc7ZLxiyR9cBYhxySZnhrpCdCYhYJUi6zr6ysOI6jBYBc1w0ZhWTb9lA/F07WBZtOVh7xGfVQRoGxCTXKwIhmXlryGT34ZEtZpp+pQOraNjc3A6MM29vbd+7cQZQBAOCUEGgAAIijkI53LpcLOQke2XD6nl63UqnwCIrFYh9fdAiOjg5Fv4sxEpfiJ6auiIdG0mYzT2FpaSlwXoPAp4s2VyqVer1er9fv378/zLQarcxFxM0nSwe0XYBzNUtiJHuSTNqXe4XjOGJUi/oLIKIM7facbDZbVwxh9JB1YY5a9UeaFTSeeT1sQS3odtn5mTVlW5zUMpAi0tTbp2NuMbFVbduuVCpiO2vy+bw6bgUAAHqGoRMAACMgJ7cTI88554eHhxGf67ru+vp6MpkMDEaMKjVAvpFun3XKFz3N0weHf91gzJKjJyxu+VN2awLFE0PEh0NM0adOzDE/P69d/XZd14xHiHZqTR1RfKcZETC3Gmc+5zRBxDn5shoGP/EsE2NMzGTRWgkRbzs6Y0BEsokWGlhbW6OTG1kkMbXbYYb/LZiYmiWiI/InyDoi4py+flxnvOdBKBYjnxNdcv4XxujRH8vquAk6xVCjwC0zPz9frVbN1A/P89bX19Vf5t5eFAAABAQaAABGYHV1tavltRPfUqnUrgT9CMkudMTlGWO///3vDw4OOp7Tq1EYreZiPPsDXx3uy+ESsiTk1EXnWasHNbRmi03ked7u7q6awmB+TIHJ+THavEHVFMRklzJtoVkkkh1P9hFCj55w4sONMgjb29sy0CCidfIh8We9Xg+fvWX4cZ/J6e8REWMW98kin1vM/+YwYJNHLYDhc6KJGWf6O0nm08PPTka7eo0yyB8H9VcimUzu7OyYU+G6rru8vFyr1ZqvGZ/dHgBgbCHQAAAwBsboxDd6Uznnq6ur/UqCiMkwCtGref64WRhPbSx7YY6e6DNHDprcXKVSSQ00aH1aCi03GPKs4TqZcWAx7vvi/4yRT0Rihkvyj8gKKR55jBEj1hxDwTgNN/ojbpfL5c3NTdH11bat+PODDz4YTqsiYmxicuamuO0zIrIs33/WuCfCCj0X15y5liQi/7n32BX74WmzS+QWljfalX6sVqvLy8tyPNpYhDIBAOIPNRoAAGBkejuJl/0x9ekx6Q+IZvjfNNg3HvNponW/xenC7JsjbFi1WlVLe2jbMLzcoPmsEeCk11PwWz1Jn3FOTExi0CqKEambyuVu4w9z/1Ffy/O88BDP6HKXTpwiKjvMkTV1hYg4b5VpsKxnT+siwHBiI3azRedeW+ecvvxjufWkfmaX2LZdLBYDowzr6+tvvfWW+tWI4a8KAMA4QkYDAACMTCaTiVInv1arifIBWsmAOF9vfPRFdeZGmlrF+X2iF2bvPKJRzhiytbWlzXNJrQ5klChDfLQ+/uYtznwi8hmJvAbLJ5+N03WUcrks6jJIMnOkVqvJfP6hO9HV5/w4XeFF+47FyWfNbc58/6tGr41kRJwmp51J+80Joodu/6MqjuNUKhXzd8Z13Z/97GeVSiUmyVAAAGcMAg0AANBn0U/cV1ZWohTJL5VK7eoUdt24AZOp4994tZkbacaO8/GnLs2Prl1ERNVq1Qw0CFtbW4H3x1prwzJ+HFbgnE5Oazns+o4dmfutSDZRCwfIb1C85rLlREQv2LetF+b85hyixHzymfW1d6+H9bU2hXX1b3JE9NWj+ldf7PWrsWLl6XS6WCzati1jN+KGCLqJ4i9Rfkbi+WsDABBnCDQAAECfDe6MPP5ZzbJNT7/4h7mTNRpmrg983sFw1WrVnOOAiFzXHd1l81Npdv+YLy6wi2R+bhHzZc8wXlEGarPfin6vWQLDjK8NTWAxRSKanP6e/MvixImefl7lz9pOuBuiuVrmv/itHxDRkz/951O3+sTKM5mMnBlXbljGmOd5t2/f7nnbep63vLzcn1YCAJxdCDQAAMDI5PP5KNdsXXfYNRRP7+sHv6VnnjVpi/Ryzomm7MmZm88f/3GEvd+9vT0z0FAoFEbSmNNr9lS55be65yLK4DPZie21OOFwiWQTLcpQrVZHuOcfBxdORkZevLY4Qc2wjs+IEz33/stpXujitbemLi0Q+d4nm6dZj6ndsKzZ2dkoiVSSnLtXfEDj+HMEADB8CDQAAMAIiBP3AZUGYIyNPNXZf+49bdSmv5NkrZ7aBNHF6299WS/RiDKxGWPlctkcPbG7uzvklvTX0TPv8f1SK9ZwcqzEOEQZKGj0BMVh3AQj4vomvPid5BE1NyzziTN6+NmvT/Mil53/mYi++uIfng9+ThYRLOi2KINYHqUcAAC6gkADAAD0WXgXWivoOKAGjHxgBSN68qcPLraGS0wQHRHNfGtRBBpG0jzOuVZ0kEZ92bwnFjGfuMxWsPgz77/9v6ujblUvZLwpnU5rUQYKqtDZbjjDgBs5QUScHzGiiWnnBft2637ijJ4/cr/6Yu80iSOXXv6PRCS+F4OGYAEAwNAg0AAAAH12eHiYSqXaPTq4DtL6+rrorY2w4gBjjHMmpl78sl76VuKX4v4jIiKa+W6a/r/VUV1mdxzHzBgf/WXzLh13sZub8Th/Qe/uxnLchBojkDeWlpa0xUqlUrsA0HCiDDITh3Ox8xInmr7+FlFzKJB49Os/V+kUm/mK887ExKxP9OTzD0/fZgAAiA8EGgAAoM88zxvJdIlxqGjIubjUTkR09Kzxzed7F64visuookzDi99Off2XaruOImMTsl/XdysrK9qoddd1S6VhXEnuo5A+tv5A/KIMFNR+x3EymYy6AGMsMAAknjucXAb5Gmq4ZvrGfyClaANj5B28H76etru0xcjnl5xVn9HTz8rPn9zvQ6MBACA2xmmiaQAAgDHAjq+x/+XjHBFx7jOfJogsTpde/kkz+z3gmdZpogzhaeFra2u5XE7+KUaXjF06wzjSPhftz1QqJaJyMnzAGBPzgwytheFklGFy2pm5kbY4yXfw7NHBV19UOjydH5lnm4yIfD457YixRV/+abe5TF9HNuRyOTYACwsL/WwlAMAZhYwGAACAvuIkSxI+/bz6/PHB1KV5zoh88hnNOpl//V2eP/N4QGL/qWajUC90J5NJWfTRtm3HcWzbVmdPZIwdHByMXTrDOFI/l9nZWXVWRcdxZI6JGoDI5/NDa15UrDlugpSMhn/9SC8s2oa+Y4udf/rfpIjo+eOD4wINsUxCAQCAHiDQAAAA0HeyZ2UdfrolKjVwRkTELtizC6uHn2we558TUb97WPPz88lkUo0skHEtPZfL9VYGUlstRDc3N9dxYsVSqRSfdAaSQyc4zb3xLmPkk29xy2f07FH9kbutT/MRuh6S+zknIpp9NcsYPf28Il5j0CU1Rj4TDQDAuYJAAwAAQF+d6DD5D+8Xv/XGuzTVnFOAc5q5seR9stlawOLk973X3nFCvq2trW7TGWq12rjNTzFKvXVr6/V6t+kMruuqgQnP807TgJMsUdaUyHrx2uLkzHeZ75NlibjCI/d9RiT2Xh4t3MBb6ySiiZmbL1550+f0pVuyOOM08BjAabYGghQAAN1CoAEAAKCvTvZH+NHhg0+2rr6xQa2gwovXFi9eTz79XHQOfSKLN28MydbWVjab7fZZy8vLg2jMWRXYLw0J/XDO7927t7y83G00p1QqBcaM1Ikwe+0ny33Sv+K8M0EWb42beP744KFbUmb/iBJlsNR1fuv1vM/o+eODp1/siXsYEfW1GGofowOn2IYAAOcUikECAAD0Awu+zTlvfJR7/tglOq6iN/e6OrjdH06UQcwGkkqleogyaDB0ojfteqrVavVnP/vZnTt3uooyBJaZ1O7sy0QVk9POle9lfEY+a0bFHnyce/7k983XjbqaE/v5xespxujRH0XFCouIuDKVZl/0Ny6AKAMAQFeQ0QAAANAPSjeEEePyb0bE6Yv/Z/XfJiuiF2Vxung9efH6W08//83xIn0dob63t7e6ukqtK7FuS+DCPVyqRaerN57nic+FiBhjjUZDfC5yvAN183HIy+zi30FMfin2yut3fsk5EfmMWcT954//IMo3Nl832oqUrwRdvP7Whel58unw0wIRiQDEoGs0AADAMCHQAAAA0GcnOl+ciOjJ59UvD/7XF7/1P7QuOLO5N94VgYZmx1I8Q+9snRj6Hj2PoF6v1+v1XlrbP8w6Y93GgDIEPPADad9j9jyvY2mMrj6O3oILFrGIz+JEMy8tT9l/++yJyzjj3CfL+uw3i1pTOwdH+IlNcnnhHW7Rkz9Xnz9xlUUQawAAODsQaAAAABiGz//vdyioS2ZGJUzNGSvUJePaJ5NvkPvM4uSzM3OxOmB4i+ixTxBxtaBn7N8q59zikeJWjOjxn3Ye/2mn4wqjv7o1aV9+aYlz+vJgW19P9LUAAEC8oUYDAADA8PDji+BRDsHNzi3jSi93EPNh9o/sc3ImptNoDr8/qxijIyI2xFqePVIjIYzxaPkmwSkb3b+0GtSY+W7amrSJ6Omfq2SdLDPRj1cDAIA4QKABAABggIy+Uyt2wGTa+YTyaOBx2RL3N5/J494fk82bCFvqLOB0xHyisaiOqU2G0gwhhJ4HMiKu7J9M+3+npysvraY8XHHe8Rk9/az8/InbnD3z+AVjvxkBACAaDJ0AAAAYIK0jJf8+vvJ/otK+L4ceiL4r55zI58yfIHoeuM74aZWb8Ps5hUAsMZoQEQafM4p7rgnR8cAWbjU7/xGmpWztn7Keo/IGOz/9ZKFTi8h//Nkum7L//NvjqU/kPu/HeuMBAEAXEGgAAAAYuGZXq303SvbFjgMQSgE9xq0jIutEIntAbcLYsIh84mc/a5LTkd+87M8o3iEGQe5dfsfUgeYeebybtZ57csfrVH7j5G7vE5H3yZb3yVbr2RYnnzCJCQDAmYNAAwAAwMB17EjJBY6rRSrP+drbf/7YffqnD8SfFjE/vlEGEv1J0eZnj8W0AnEOi/SOf/Pls0cu56TOnhB/X33xD1Mv2U//XA1bqBWRIL2C6YnPkfHewivN/YE3/wUAAAAAAIAuhV4/Dr7yHzTsfxxyBAJG8p9x5+edKo53xehv/+QuPQ47MwAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAD8/+3BIQEAAACAoP+vvWEAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAFgInHQ/fnhAweAAAAAASUVORK5CYII="
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "def calculate_sMAPE(y, y_pred):\n",
    "    return 200 / len(y) * (abs(y - y_pred) / (abs(y) + abs(y_pred))).sum() "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The resulting sMAPE is calculated based on sMAPE for `final.output.recovery` and `rougher.output.recovery` predictions: "
   ]
  },
  {
   "attachments": {
    "smape.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![smape.png](attachment:smape.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following class serves for the purpose of this calculation:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Resulting_sMAPE:\n",
    "    def __init__(self, error_rougher, error_final, model_name='*not stated*'):  # receives calculated sMAPE for rougher/final\n",
    "        self.error_rougher = error_rougher\n",
    "        self.error_final = error_final\n",
    "        self.model_name = model_name\n",
    "    \n",
    "    def score(self):  # returns resulting sMAPE value\n",
    "        return round(abs(0.25 * self.error_rougher + 0.75 * self.error_final), 2)\n",
    "    \n",
    "    def print_score(self): # prints resulting sMAPE value\n",
    "        print(f'Resulting sMAPE score for {self.model_name} = {self.score():.2f}%')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create a scorer to pass the metric as an argument to the models:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "scorer = make_scorer(calculate_sMAPE, greater_is_better=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "All the metrics for different models will be added to a dictionary for final comparison: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "metrics = {}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is a reusable code that fits the given model on rougher and final samples. Returns fit models (as *GridSearchCV* objects) and sMAPE object:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "def model_fit_gridsearch(model, parameters, dataset, label=\"*not specified*\", CV=5):\n",
    "    \n",
    "    \"\"\"\n",
    "    This function returns GridSearchCV objects for rougher and final recovery predictions by passed model.\n",
    "    It also returns the sMAPE metric object and adds sMAPE for current model to metrics dict as a side effect.\n",
    "    Dataset is passed as a dictionary.\n",
    "    \"\"\"\n",
    "\n",
    "    grid_rougher = GridSearchCV(model, parameters, scoring=scorer, cv=CV, n_jobs=-1)\n",
    "    grid_rougher.fit(dataset['X_rougher'], dataset['y_rougher'])\n",
    "    \n",
    "    grid_final = GridSearchCV(model, parameters, scoring=scorer, cv=CV, n_jobs=-1)\n",
    "    grid_final.fit(dataset['X_final'], dataset['y_final'])\n",
    "\n",
    "    sMAPE = Resulting_sMAPE(\n",
    "        grid_rougher.best_score_, grid_final.best_score_, label\n",
    "    )\n",
    "    if label != \"*not specified*\":\n",
    "        metrics[label] = sMAPE.score()  # add named metric to metrics dict\n",
    "\n",
    "    return grid_rougher, grid_final, sMAPE"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "All the preparations are done, let's see the performance of simple model from sklearn to get an understanding of sMAPE magnitude."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### LinearRegression (LR)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "# instantiate LR object and set \"hyperparameters\" for grid search (not much of them)\n",
    "LR = LinearRegression()\n",
    "parameters_LR = {'fit_intercept': [True]}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Train LR model on the 3 datasets and compare resulting metrics:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: total: 688 ms\n",
      "Wall time: 6.74 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "# fit on train dataset with outliers\n",
    "grid_rougher_LR_full, grid_final_LR_full, sMAPE_LR_full = model_fit_gridsearch(\n",
    "    LR, parameters_LR, train_dataset_full, label='LR (full)', CV=10\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: total: 1.02 s\n",
      "Wall time: 851 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "# fit on train dataset without outliers\n",
    "grid_rougher_LR, grid_final_LR, sMAPE_LR = model_fit_gridsearch(\n",
    "    LR, parameters_LR, train_dataset, 'LR', CV=10\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: total: 938 ms\n",
      "Wall time: 748 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "# fit on train dataset with feature selection\n",
    "grid_rougher_LR_FS, grid_final_LR_FS, sMAPE_LR_FS = model_fit_gridsearch(\n",
    "    LR, parameters_LR, train_dataset_fs, 'LR (FS)', CV=10\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Resulting sMAPE score for LR (full) = 10.80%\n",
      "Resulting sMAPE score for LR = 7.94%\n",
      "Resulting sMAPE score for LR (FS) = 7.81%\n"
     ]
    }
   ],
   "source": [
    "# print scores for all three datasets\n",
    "sMAPE_LR_full.print_score()\n",
    "sMAPE_LR.print_score()\n",
    "sMAPE_LR_FS.print_score()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Conclusion:** \n",
    "1. Removing outliers is crucial for LinearRegression model in this task. It improves the key metric by almost 3% (10.8% for model trained on dataset with outliers vs 7.94% for dataset with outliers). \n",
    "2. Feature selection also helps to improve score on 10-fold CV, but only by 0.13% (resulting sMAPE is 7.81%). "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### RFR"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "RFR model is trained on the same 3 datasets. Only a small subset of hyperparameters is used to save time. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "# instantiate RFR object and set parameters for grid search\n",
    "RFR = RandomForestRegressor(random_state=11, n_jobs=-1)\n",
    "parameters_RFR = {\"max_depth\": range(3, 6, 2), \"n_estimators\": range(10, 51, 20)}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: total: 45.3 s\n",
      "Wall time: 1min 27s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "# fit on train dataset with outliers\n",
    "grid_rougher_RFR_full, grid_final_RFR_full, sMAPE_RFR_full = model_fit_gridsearch(\n",
    "    RFR, parameters_RFR, train_dataset_full, label='RFR (full)'\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: total: 39 s\n",
      "Wall time: 1min 17s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "# fit on train dataset without outliers\n",
    "grid_rougher_RFR, grid_final_RFR, sMAPE_RFR = model_fit_gridsearch(\n",
    "    RFR, parameters_RFR, train_dataset, 'RFR'\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: total: 32.9 s\n",
      "Wall time: 1min 5s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "# fit on train dataset with feature selection\n",
    "grid_rougher_RFR_FS, grid_final_RFR_FS, sMAPE_RFR_FS = model_fit_gridsearch(\n",
    "    RFR, parameters_RFR, train_dataset_fs, 'RFR (FS)'\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Resulting sMAPE score for RFR (full) = 10.24%\n",
      "Resulting sMAPE score for RFR = 7.49%\n",
      "Resulting sMAPE score for RFR (FS) = 7.46%\n"
     ]
    }
   ],
   "source": [
    "# print scores for all three datasets\n",
    "sMAPE_RFR_full.print_score()\n",
    "sMAPE_RFR.print_score()\n",
    "sMAPE_RFR_FS.print_score()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Conclusion:** \n",
    "1. Removing outliers is crucial for RFR model as well as for LR model. It improves the key metric by almost 3% (10.24% for model trained on dataset with outliers vs 7.49% for dataset with outliers).\n",
    "2. Resulting sMAPE on regular dataset (without outliers) for RFR in a 5-fold CV mode is 7.49%. This result is better by around 0.5% than predictions of LR model. \n",
    "3. Feature selection has almost zero effect on RFR model results on cross-validation.\n",
    "  \n",
    "A more radical feature selection can be performed in case of RFR, as we can train the model only on the top important features.  In the next using these features for RFR predictions is investigated."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Top important features"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Getting pd.Series of important features for rougher and final recovery predictions:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "feat_importances_rougher = pd.Series(\n",
    "    grid_rougher_RFR.best_estimator_.feature_importances_, \n",
    "    index=train_dataset['X_rougher'].columns\n",
    ")\n",
    "feat_importances_final = pd.Series(\n",
    "    grid_final_RFR.best_estimator_.feature_importances_, \n",
    "    index=train_dataset['X_final'].columns\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Constructing plots:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot for rougher recovery predictions\n",
    "plt.figure(num=None, figsize=(8,6))\n",
    "\n",
    "feat_importances_rougher.nlargest(10).plot(kind='barh')\n",
    "plt.title('Feature importance for rougher recovery prediction')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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XLz70Krdz585l/vz5BAYG0qJFCwoWLAhgXO3OqvT39O5Fe65evcqhQ4eoUaMGBQsWpEGDBvTp0yfD8Xcnm/dTuHBh/v77b27dumV2XHrCcb/EIrv8+eefDBo0CE9PT+bMmWM8jmTJkiX8/PPP/6rurJ6XBQsWzHQxr+3bt1OmTBkKFy7M6dOnM+y/ePEi8H9xK1euHDVq1DDu3/3777+Nq9Pp7fyb97Fjx44EBAQQGxvLtm3bsvSIqLs/E3D7M3C/L0Sy+numSJEiGRb7MplMZosV3e1BsX7Q59DSn2ORR6UpviIiIkCFChV47rnnOHv2LK6ursa/kiVLMnXqVGN1zj179tCiRQs8PDyM6Xdbt24FMFZrzWwanoODA3///bfZ6rh79+59YL8KFChAlSpVOHnypFm/KlWqRFhYWIZpd9ll06ZNZj//+OOP2NjY4OHhgZubG/b29qxdu9aszO7duzl37hy1atW6b93pV1/S7dmzh4oVK9KpUycjOT1//jzHjh0zWxH3QSpUqEDRokWJiooy27527Vr69+/PzZs3qVu3LidOnKBy5cpGbKtVq8b8+fONKZ9ZUbduXVJTUzNcwVuzZg1Ahimsj8N///tfbt68iY+Pj9mzMtOT039ztSur52Xt2rXZv3+/2UJFV65coX///kRFRVGnTh3i4uIyTItds2YNuXPnpnr16sa2tm3bsnXrVr777jtq1KhB+fLljX3/9n1s0aIF+fPnZ9SoUeTNm5fmzZs/8Jg///yTEydOGD+fP3+e/fv3U79+/Xsek9XfM/Xr12fr1q3GdHW4/b7db1r2g2INGT9rd/q3n2MRS9EVVBEREW4nlR988AGjRo3C1taWJk2aEB8fz6xZszh//jxVq1YFoHr16qxdu5aqVatSsmRJ9u3bx5w5c7CxsTH+mExPqrZv346TkxNubm40adKERYsWMXz4cDp37szx48eZN2/efe8pSzd06FDeeecdPvzwQ9q2bUtqairz5s3jwIEDxmqy2e3gwYMMGzaMdu3acfToUWbMmEGXLl2MqyrvvPMOYWFh5M6dG09PT86ePcv06dOpWLEib7755n3rLlSoEPv27WP79u1UqVKF6tWrM2vWLObOnUuNGjU4ffo0c+bMITk52ewP9gextbVl8ODBjB07lsDAQJo1a8apU6cICQmhW7duODo6MnDgQLp27YqPjw/dunUjT548LFu2jI0bNzJjxowst/XKK69Qr149Ro8ezYULF6hSpQo7d+4kPDycDh065MgzaatWrYqdnR1BQUF4e3uTnJzMqlWriI6OBv79/YJZOS979+7N6tWr6du3LwMGDCBPnjzMmTOH4sWL0759e/LkycPSpUvx9fXlvffe44UXXmDTpk2sXLkSX19f44oj3L4P+rPPPmPdunV88sknZn35t+9jvnz5aN26NcuWLaNLly7kzZv3gceYTCYGDhzIkCFDsLW1JSwsjEKFCpk92uluWf09M2jQIDZu3Ejfvn3p168ff//9N8HBweTOnfuedT8o1vB/n7Vdu3YZz9dNV6RIkX/1ORaxFCWoIiIi/1/nzp1xcHDgiy++YNmyZeTPn59atWoxZcoUIxGbOHEi48aNY9y4cQCUL1+eMWPGsGbNGnbv3g3cvrrUp08fli1bRnR0NNu2baNhw4b4+fmxaNEifvrpJ6pWrUpYWBhdu3Z9YL8aNWpEREQEYWFhvPfee+TOnZuqVavy5ZdfZlgkJ7v06tWL8+fP4+vrS9GiRRkwYAA+Pj7G/sGDB/P888+zePFili9fTpEiRXj99dcZMmTIA6ch9+jRg//+97/079+fzz77DB8fH/7++28WLlzIzJkzKVWqFO3atcPGxoY5c+Zw7do1Yzrig/To0YP8+fMTERHBihUrKFGiBN7e3sbzR1966SWWLFlCcHAwH3/8MSaTCWdnZ2bOnGk8zzYr0vs2Y8YMFi5cyJUrVyhTpgwffPBBptNOH4dy5coxdepUwsLCePfddylcuDA1atRg0aJFeHl5sXv37gwLBj2MrJyXpUqVYunSpQQFBREQEIC9vT1169YlKCiIIkWKALBo0SKmTp3KjBkzuH79OhUqVGD8+PEZpnQXKVKExo0bs2XLlgyLUVnifWzSpAnLli3LciL2n//8hz59+jBhwgQSExNp0KABn3/+uTGue8nK75ny5cuzePFiJk6cyAcffMBzzz2Hn58fEydOvGe9WYn1gAEDmDVrFv3798/0ft1/8zkWsRQbk+5mFhERkftwcXHB19eXwYMH53RXRLJNYGAge/bsyTDFNTP+/v7s3Lkzw9R3Efn3dAVVRERERJ5ZCxcu5OTJkyxbtixLiyOJSPZSgioiIiIiz6zdu3fz888/4+Xl9cBnn4pI9tMUXxEREREREbEKesyMiIiIiIiIWAUlqCIiIiIiImIVlKCKiIiIiIiIVVCCKiIiIiIiIlZBq/iKyBPPZDKRlqb13iwpVy4bxdTCFFPLU0yzh+JqeYqp5SmmlpedMc2VywYbG5sslVWCKiJPPBsbG+Ljb3DrVlpOd+WpYGeXi6JFHRRTC1JMLU8xzR6Kq+UpppanmFpedsfU0dEBW9usJaia4isiIiIiIiJWQQmqiIiIiIiIWAUlqCIiIiIiImIVlKCKiIiIiIiIVVCCKiIiIiIiIlZBCaqIiIiIiIhYBSWoIiIiIiIiYhWUoIqIiIiIiIhVsMvpDoiIWIKtbfZ+35aWZiItzZStbYiIiIg865SgisgTz2QyUahQvmxtIzU1jatXbyhJFREREclGSlBF5IlnY2PDlCV7OHs+IVvqL1OiIMN6uJMrl40SVBEREZFspARVRJ4KZ88nEBN3Lae7ISIiIiL/ghZJEhEREREREaugBFVERERERESsghJUERERERERsQpKUEVERERERMQqPJEJ6qpVq3Bxccnpblhc06ZNCQ0NzeluWNzx48eJjo7OcnmTyURkZCSXL19+5DaTk5P58MMPqVGjBrVr12b9+vW4uLhw9uzZR67zbjdu3GDJkiXGz/7+/nh5eVms/sycPXsWFxcXduzYcc8yv//+O2+//TY1a9akcePGTJ48meTk5Gzt18N6Ws91EREREfl3nsgEtVWrVvzyyy853Q3JIh8fH37//fcsl9+1axf+/v4kJiY+cptbt27lu+++Y+rUqaxevZrnnnvukeu6l3nz5hEREWHxev+NK1eu0K9fPypUqMDq1asZN24ckZGRBAcH53TXzKxYsQJvb++c7oaIiIiIWJknMkHNmzcvxYoVy+luSDYxmf79cyYTEm4/D7Np06aUKVPmX9eXGUv009L27t3L1atX+fjjjylXrhyvvPIKbdu2tbovdBwdHXFwcMjpboiIiIiIlcnxBNXFxYWvvvqKbt26Ub16ddq0aUNUVJSxPzQ0lK5duzJ06FBq1arFmDFjMkzxdXFx4bvvvqNnz55Ur16dZs2asWnTJjZt2kSLFi2oUaMG/fr148qVK8YxmzZtomvXrtSsWRNXV1c6derEr7/+auz38vJi+PDhdO7cmdq1axMWFoaLiwu7du0y6/8HH3yAr69vlse7bds2unbtipubG6+88gpTp04lNTU107J79+6lR48eVK9enVdffZUxY8Zw/fp1Y/9ff/3FsGHDaNCgAVWrVqVx48YEBweTlpYG3J4K3bRpUyIjI2nWrBnVqlWjY8eO7Nu3z6gjOTmZoKAgXn75ZWrWrEmXLl3Mkpn0OsaPH0/t2rUZMGBAhn4ePHiQ7t27U7NmTerUqcPgwYM5d+4ccDtBjIuLIywszJj+evz4cQYOHEi9evWoVq0azZo1Y8GCBQDs2LGDnj17AuDp6cmqVauyFIs7hYaG4u/vD8BLL71kvL5Tamoq8+fPp0WLFri6utKiRQu++eYbszL3O0dCQ0MJCwsjLi7ObOrwrVu3+PTTT3F3d8fDw4Np06Zx69Yto849e/bQp08f3N3dqVatGm+88Qbfffedsd/f35+PPvqISZMmUb9+fdzc3Bg4cCAXL17MdKyxsbE0atSIDz/8kNTUVIoUKQLAV199RWpqKmfPnmXLli24ubllevy9PMx5mpmVK1fSvn17qlevTo0aNfDy8uLQoUPG/jun+Gb2GRcRERGRZ5NdTncAYPLkyQwbNozx48ezatUqfH19WbJkCbVq1QJg3759uLq68u2335KamsrevXsz1PHpp58yZswYPv30Uz777DM+/PBDKlasSFBQEDdu3OC9994jPDwcPz8//vvf/zJo0CA++ugjgoKC+OeffwgODmbYsGFER0djb28P3E7OgoKCeOmll3j++eeJiopi9erV1KlTB7h9lS4qKoqQkJAsjfPAgQP069ePXr16MX78eP73v/8xbNgwcuXKxQcffGBW9siRI/Tu3ZsBAwYwfvx4Ll26xOTJk/H29mbZsmXY2Njg4+PDc889R0REBAUKFCA6OppPP/0UV1dXXnvtNQAuXLjA119/TVBQELlz5yYwMBA/Pz9+/PFHbGxsCAgI4Pjx4wQFBVGyZEk2b97MgAEDCAsL49VXXwUgLi6O8+fPExkZSVJSklk/09LS8PHxoUuXLkyaNIn4+HhGjRrF8OHDmT9/PitWrKBDhw60atUKHx8fEhMT6dOnDx4eHixduhQ7OztWrlzJhAkTqFu3LjVr1iQ0NJTBgwezfPlynJ2dsxSLO3l7e1OoUCEmTJjAL7/8Qt68eTl8+LBZmYkTJ/Ltt98ycuRIXF1d2bZtG2PHjuXmzZt4eXk98Bzx9vbmxo0brF+/nhUrVuDo6AjcTqRLlCjB119/zdmzZxkxYgQ3btxgxIgRnD9/Hm9vb7p3705gYCC3bt3iiy++ICAgAA8PD55//nkAvv/+e9q0acPixYs5d+4cw4YNIzg4mAkTJpiN4c8//6RXr140bNiQzz77jFy5clG7dm3eeecdpk+fTnBwMKmpqdStW5eRI0dm6Rx92PM0Mxs2bGD06NF8+umn1KlTh0uXLvHpp5/yySefsHr16kyPufszbq1sbXP8O73HIn2cz8p4HwfF1PIU0+yhuFqeYmp5iqnlWVNMrSJB7dixIz169ABg2LBh7Nq1i8WLFxsJKsB7771HwYIFATJNUDt06ECLFi0A6Nq1K5s2beKDDz6gevXqADRs2JBjx44BYGtry4gRI4w2AXr27Im3tzeXL1+mVKlSAFSuXJk2bdqY9TMkJIRRo0aRJ08evv/+ewoWLMgrr7ySpXEuXLiQ6tWrG1f0nJycGDduHBcuXMhQNiIigvr16zNw4EAAypcvz9SpU3nttdfYuXMnbm5utGvXjhYtWlC6dGng9lXfuXPncvToUSNBTUlJITAwkMqVKwO37wcdNGgQFy9eJDExke+++44VK1bg6uoKQJ8+fThy5AgRERFGggowcOBAXnjhhQz9TEhI4O+//6Z48eKUKVMGGxsbQkJCjAWOHB0dsbW1JX/+/BQpUoQrV67Qs2dPunfvToECBQDw9fVlzpw5HD16lMqVK1O4cGHj2Lx58z4wFvXq1TPrk4ODg3GuZDYV/Pr163z11Vf4+/sb72/58uU5c+YMs2fP5u23387SOZI/f35sbW3N2ihWrBiTJk0iT548VKpUiffff5+xY8fy4YcfkpycjK+vL3379iVXrlzG+7Fq1SpOnTplJKgFChRg7Nix5M6dGycnJ9q1a8eWLVvMxnD27Fn8/Px4+eWXGTdunFFffHw8p06dokePHrRt25YzZ87w2WefERgYyGeffZYhFpl5mPM0M0WKFOHTTz+lffv2AJQuXZrOnTszevTo+x5352fcWhUqlC+nu/BYPWvjfRwUU8tTTLOH4mp5iqnlKaaWZw0xtYoEtW7dumY/u7m5mU23fe655x74h+uLL75ovM6bNy+AWUKVJ08eYyXT9CQoPDyc2NhYTp06xR9//AFgdvWmXLlyZm20adOGSZMmERUVRatWrYiMjKRt27bY2WUtjEePHqVBgwZm25o1a5Zp2cOHD3P69Glq1qyZYV9MTAz16tXj7bff5ocffmDBggWcPn2aI0eOcOHCBWOKbzonJyfjdXocU1JSjKuK6VNq06WkpFCoUCGzbeXLl8+0n4ULF6Zfv36MGzeOsLAwGjRowCuvvGJ8WXA3R0dHunfvzvr16zly5AinT582Yn93vx8mFg/j5MmTpKSk4O7ubra9du3afPnll1y+fDnL58jdqlWrRp48eYyfq1evTkpKCqdOnaJy5cp07NiRxYsXc+LEifued7lz5zZ+LliwICkpKWbtBAYGkpKSQqlSpYzkFGDKlCnEx8cb02erVq1K4cKF6d27N7169eKll156YHwe5jzNTJ06dXB0dGTWrFmcPn2a2NhY/vjjj3u+v5C1z7g1iI9PJDX13uN4Wtja5qJQoXzPzHgfB8XU8hTT7KG4Wp5ianmKqeVld0wLFcqX5auzVpGg3p3gpaWlmf3RnZ5wPkwdQIapn+l27dqFt7c3jRs3pnbt2rRu3ZrExEQGDRpkVu7udgsXLsxrr73GmjVrcHV1Zd++fYwdO/aBfbuzj/fq093S0tJo06ZNpvd8Ojo6kpiYSI8ePUhMTKRly5a0a9eOkSNHml3xS5c+ZflOJpPJWORnyZIlGRasuTP+cP/3YNiwYXTv3p0tW7awfft2AgMDmTNnDqtXr87Q9qVLl+jSpQtFixbF09OT+vXr4+rqSuPGjR85Fg8rfdx3vxfpCZSdnV2Wz5G72draZlqnvb09MTExdOvWjSpVqtCwYUM8PT0pWrQonTt3Njsms/frbh06dMDZ2ZmJEyfSrFkz457sPXv20KRJE7Oy6fefxsbGZilBfZjzNDPr1q3j448/5o033qB69ep06tSJY8eO3fezkpXPuDVITU3j1q1n5z/CZ228j4NianmKafZQXC1PMbU8xdTyrCGmVpGg/v777zRt2tT4ef/+/VStWjXb2ouIiKBevXqEhYUZ2xYtWgQ8eGXWjh078u677/Ltt9/i6upKpUqVstyuk5NThsetzJ8/n2+//ZbIyEiz7ZUqVeL48eNmV3FPnjzJ5MmTGTp0KKdOneLQoUNs27bNmBp69epVLl++nOXVZdP7fuHCBbPpvMHBwdjY2DBkyJAH1nHy5EkWLFjA8OHD6datG926dWPPnj10796dI0eOGFOs061du5arV6/y448/GlcJjx49Ctw7cXxQLB72yluFChWws7Nj9+7dZgnb7t27KVasGIULF87SOZJZEpd+pTA9wd+zZw958+blhRdeYPLkyTz33HPMnz/fKL9p0yazOrOqdevW1K1bl++++46AgAC++eYb7OzsKFmypBHPdOlT2+91FfxuD3OeZmb27Nl06tTJbLGj9IXPTCbTv0p+RUREROTplvN3wQILFixg7dq1xMbGMmnSJI4cOUKvXr2yrb1SpUpx9OhRdu/ezdmzZ1m5ciXTp08HMKYB30uDBg14/vnnCQ8P580333yodvv168f+/fsJCQkhNjaWLVu2MGfOHDw9PTOU9fb25o8//mDUqFGcOHGCAwcOMGzYMGJjYylfvjwlS5YEYM2aNcTFxbF7924GDhxISkrKA8eQrlKlSjRp0oTRo0cTFRXFmTNniIiIYM6cOZneb5ru6tWrXL16Fbh9v+F3333HqFGjiImJITY2lpUrV1K4cGEqVKgA3L4n9NSpU1y6dImSJUuSmJjI999/z7lz5/jll18YOnQo8H+xz58/P3B7oah//vnngbEA+Oeff+650u3dChYsSJcuXZgxYwZr167l9OnTLFmyhKVLl+Lt7Y2NjU2WzpH8+fNz7do1YmNjjSm4//vf/xg+fDjHjx/nxx9/JDQ0lH79+mFvb0/JkiX566+/2LJlC3Fxcfz0008EBgaa1fkwbGxsGD9+PMeOHWPu3LnA7XuIf/75Z0JCQvjzzz/Zvn07/v7+NG7c2LgP+UEe5jzNTKlSpdi7dy+HDh3izz//ZP78+SxevPiRxykiIiIizw6ruIL61ltv8eWXX3L8+HFeeuklIiIisjQV8VG99957XLp0yZgyWrFiRSZMmMBHH33EwYMHze7ZvFuuXLlo27YtX375Ja1bt36oditXrsysWbOYMWMGX3zxBcWKFcPLyyvTqas1atTgiy++YPr06bz55pvky5cPDw8P/Pz8sLe3p3r16gQEBDB//nxCQkIoUaIErVq1olSpUhw4cCDLfQoODiY4OJjRo0dz7do1XnjhBcaNG0fHjh3veczgwYOB21cUHR0d+eKLL5g6dSpdunQhNTWVGjVq8OWXXxqLIHl5eTFp0iSOHz/Ot99+y6FDh5g0aRLXr183FtCJiori4MGDdOvWDWdnZxo3bsyQIUMYOnQo3t7e940FwLx58wgLC8tw9fBePvnkE4oWLcrUqVO5dOkS5cqVY9SoUXTp0gXI2jnSvHlzvvnmG9q2bWskYJ6entja2tKlSxfy5ctHt27djMWdevbsycmTJ/n4449JTk6mfPnyDB06lBkzZnDw4MEsL7Z1JycnJwYMGMCsWbPw9PSkUaNGzJkzh5kzZ7JgwQKKFi1Ks2bNeP/997Nc58Ocp5kZOXIko0aN4u2338be3p6XXnqJyZMn88EHH3DgwIEM95yLiIiIiKSzMT3s3EILc3Fx4bPPPnvoq5E5KSAggJSUFKZMmZLTXRGR/2/ItGhi4q5lS91OpQsTMvRV/v77nxy/L+NxsLPLRdGiDs/MeB8HxdTyFNPsobhanmJqeYqp5WV3TB0dHZ6sRZKeFNu2bePEiRN89913LFmyJKe7IyIiIiIi8lRRgvoQVq5cSXR0NIMHDzZb/Gffvn14e3vf99jXXnuNoKCg7O6iyD392/P0/PnzvP766/c9vkqVKvryRkREREQeWY4nqFm9Z9AaTJs2LdPtVapUYfXq1fc9Nn3hH5Gc8m/P0+eff/6Bx9/5DFgRERERkYeV4wnq0yBPnjxmj0ARsUb/9jy1tbW16vO8TImHe9yQtdQtIiIiIv9HCaqIPPFMJhPDerhnaxupqWmkpeXomnIiIiIiTz0lqCLyxLOxsSE+PpHU1OxbyS8tzaQEVURERCSbKUEVkadCamqalpoXERERecJl7WE0IiIiIiIiItlMCaqIiIiIiIhYBSWoIiIiIiIiYhWUoIqIiIiIiIhVUIIqIiIiIiIiVkEJqoiIiIiIiFgFJagiIiIiIiJiFZSgioiIiIiIiFVQgioiIiIiIiJWQQmqiIiIiIiIWAUlqCIiIiIiImIVlKCKiIiIiIiIVVCCKiIiIiIiIlZBCaqIiIiIiIhYBSWoIiIiIiIiYhWUoIqIiIiIiIhVUIIqIiIiIiIiVkEJqoiIiIiIiFgFJagiIiIiIiJiFZSgioiIiIiIiFWwy+kOiIhYgq3t4/m+LS3NRFqa6bG0JSIiIvKsUYIqIk88k8lEoUL5HktbqalpXL16Q0mqiIiISDZQgioiTzwbGxumLNnD2fMJ2dpOmRIFGdbDnVy5bJSgioiIiGQDJagi8lQ4ez6BmLhrOd0NEREREfkXtEiSiIiIiIiIWAUlqCIiIiIiImIVlKCKiIiIiIiIVVCCmk127NiBi4sLZ8+ezdZ2vLy88Pf3z9Y27nbjxg2WLFly3zJz587Fw8MDNzc3Nm7ciIuLC6tWrbJYH0wmE5GRkVy+fBmAVatW4eLiYrH676Vp06aEhoY+sFxaWhp9+/a9Z9lvv/2Wpk2bWrp7FrFnzx52794NwNmzZ3FxcWHHjh3/qs7jx48THR1tgd6JiIiIyNNMCeoTLjQ0lE8++eSxtjlv3jwiIiLuuT8+Pp5p06bRpUsX1q9fT8OGDS3eh127duHv709iYqLF6/63kpKS+Oijj/jll18y3b9+/frH/p49jO7du/Pnn39atE4fHx9+//13i9YpIiIiIk8freL7hCtSpMhjb9Nkuv/jNRISEjCZTDRo0IDSpUvnSB9yyt69e/nkk09ISUmhUKFCZvuuX79OYGAg33//PRUrViQhIXsfiSIiIiIi8qR56q+guri4EBwcTJMmTWjYsCEnT54kKSmJkJAQPD09cXV1pX379mzcuNE4JrPpondP2U1NTSU4OJhGjRrh5ubG4MGDGT9+PF5eXmbHbdmyhTZt2lCtWjVat25tNs3RZDIRHh6Op6cnbm5utGvXjjVr1mRoMzw8nHr16tGhQwdSU1PN6r9ziu+qVato2rQpkZGRNGvWjGrVqtGxY0f27dtnlG/atCmzZ8/Gx8eH6tWr06xZM5YvX57lsYeGhhIWFkZcXFymU5h37NhhTF3t1avXPaexRkdH06VLF2rWrEmjRo2YOHEiN2/eNPYfP36cgQMHUq9ePapVq0azZs1YsGCB0UbPnj0B8PT0NJs6vHz5cl555RVq1KjBe++9x5UrV4x9f/31F8OGDaNBgwZUrVqVxo0bExwcTFpaWpbjd6cbN27Qo0cP2rRpY0w1/vnnn2nWrBmrV6+mYMGCZuXPnj3LtWvXWLFiBa+99lqmdd7PgQMHqFKlCl9++aWxLSQkBHd3d86cOQPApk2b6Nq1KzVr1sTV1ZVOnTrx66+/GuW9vLyYNGkSw4cPp3bt2tSqVQs/Pz/++ecfAOO9DwgIMJs6fuDAAbp06UK1atXw9PRk5cqVxr7k5GSmTp3Ka6+9RrVq1ahXrx5Dhw7l77//Bm6fc3FxcYSFhRmfj4SEBEaOHImHhwfu7u707NlTV1hFRERE5Nm4grps2TLCw8NJTU2lQoUKDBw4kMOHDzNq1ChefPFF1q1bh6+vLzNnzsTT0zNLdU6ZMoXIyEjGjh2Lk5MTS5cuZdGiRdSpU8es3MKFCxk7dizFixdnypQpDBkyhG3btuHg4EBwcDBr165l1KhRODk5sWvXLgIDA0lISKBHjx5GHdHR0SxbtozExERsbW3v268LFy7w9ddfExQURO7cuQkMDMTPz48ff/wRGxsbAGbOnImPjw9+fn5s3bqVUaNG4eDgQKtWrR44bm9vb27cuMH69etZsWIFjo6OZvtr1qzJ8uXL6dy5M6GhodSuXTtDHRs3bmTw4MH4+voyceJETp8+TWBgIHFxcYSGhpKYmEifPn3w8PBg6dKl2NnZsXLlSiZMmEDdunWpWbMmoaGhDB48mOXLl+Ps7Mz69euNeIeEhGBvb8+4cePw9vYmMjISGxsbfHx8eO6554iIiKBAgQJER0fz6aef4urqaiSMWYkf3J7GO2DAAP755x8WLFhgxOH999+/Z+xeeuklwsPDjRg8LDc3N3x8fJg+fTqenp5cuHCBuXPnMnnyZF544QX++9//MmjQID766COCgoL4559/CA4OZtiwYURHR2Nvbw/AokWL8Pb2Zvny5fzxxx/4+flRtmxZBg0axC+//EKjRo0YPnw4b775Jteu3X6u6Pz58/n000+pWLEi8+bNY8SIEdSuXZty5coxefJkoqKimDhxImXKlOH48eP4+fnx+eefM3z4cFasWEGHDh1o1aoVPj4+mEwm+vfvT+7cuZkzZw4FChTg22+/pVu3bnzzzTdUqVLloWPzuNnaPt3f7aWP72kf5+OkmFqeYpo9FFfLU0wtTzG1PGuK6TORoLZr1w5XV1cAYmJiiIqKYvbs2TRp0gQAX19fjh49yuzZs7OUoCYmJrJ06VICAgJo3rw5ACNHjsz0Stvw4cOpV68eAIMGDWLjxo3ExMRQsWJF5s+fz+TJk41+lC1blri4OCIiIswSVG9vb8qXL5+lsaakpBAYGEjlypWB2/f+DRo0iIsXL1K8eHEAGjZsiK+vLwAVKlTgwIEDLFiwIEsJqoODA/nz58fW1pZixYpl2G9vb28ka4ULF86QwALMmTOHZs2aMWjQIKMPJpOJd999l5iYGIoWLUrPnj3p3r07BQoUAG6/R3PmzOHo0aNUrlyZwoULA+Do6EjevHmNuoOCgnjppZcAmDRpEi1atGD79u3UqlWLdu3a0aJFC2PasZeXF3PnzuXo0aNGgpqV+N28eZN3332X69evM3/+/Mc6zXrQoEFs3bqVTz75hHPnztGuXTveeOMNAGxtbRkxYoTZudOzZ0+8vb25fPkypUqVAsDJyYmhQ4cCGF/Q7N27F8B4TwsWLEjBggWNBHXQoEHG1fAPPviAr776ikOHDlGuXDlcXV1p3rw5devWBaB06dI0atSIo0ePArffI1tbW/Lnz0+RIkXYvn07+/btY/v27cb5MXToUPbu3cvChQuZOHFitsbQEgoVypfTXXgsnpVxPk6KqeUpptlDcbU8xdTyFFPLs4aYPhMJarly5YzX6X80u7u7m5WpXbs2U6dOzVJ9MTExJCUlUaNGDbPt7u7uHDlyxGzbiy++aLxOvycxKSmJEydOcPPmTfz8/AgICDDK3Lp1i+TkZJKSkoxtWU1O0zk5ORmv06eZpqSkGNvSE+Z0NWrUeKwrrB47dozWrVubbUu/8nz06FFatWpF9+7dWb9+PUeOHOH06dP88ccfAMZ03Mw4ODgYySncjlvhwoU5duwYDRo04O233+aHH35gwYIFnD59miNHjnDhwoUMdT4ofgsWLCAlJYV69eoZifLjYmdnR1BQEG3btuW5555j5MiRxr70xD08PJzY2FhOnTplxO3OqeF3jg9ujzE+Pv6+7VaoUMF4nT7m9CnZ7dq1Y/v27UybNo1Tp04RExPDyZMnM716DnDo0CGADF8GJScnm03ztmbx8Ymkpt77XHzS2drmolChfE/9OB8nxdTyFNPsobhanmJqeYqp5WV3TAsVypflq7PPRIJ65xW2e0lLS8POzjwcJpPJmNZ569YtY3t6uaws1JMrV8Y3wmQyGceGhISY/fGfLn06JkCePHke2M69jr2zzXSZjfPuft5r7JZwZ93p0hMoOzs7Ll26RJcuXShatCienp7Ur18fV1dXGjdufN96M5v+nJaWhr29PYmJifTo0YPExERatmxJu3btGDlypNnVxnQPip+zszN+fn706dOHr7/+mm7dumVp3JZy7Ngx0tLSuHjxIkeOHKFWrVrA7ZWNvb29ady4MbVr16Z169YkJiYaV6rTZTa+B7nXeQwQGBjI+vXrad++Pa+++irvvvsuERERnD9/PtO60tLSKFCgQKaPHXqUvuWE1NQ0bt16+v9DfFbG+TgpppanmGYPxdXyFFPLU0wtzxpi+kwkqHdydnYGbj/rMX1qLcDu3bupWLEiALlz5wZuL+SSftXz9OnTRtly5cqRN29e9u/fb0wFBTh48GCW/8CuUKECdnZ2nDt3zqwfCxcu5MSJE4wdO/YRR/hgdy9Gs3fvXuO+vweNHciQXD4sZ2dn9uzZQ69evYxt6c/ddHJyYu3atVy9epUff/zR6E/6le/0pCizPsTHx/Pnn39StmxZ45iEhAScnZ35+eefOXToENu2beP5558H4OrVq1y+fPmhVwR+9dVXqVu3Lt7e3gQFBfHKK69k22rFd7tw4QKjR4+mf//+/PXXX/j5+bF69WocHByIiIigXr16hIWFGeUXLVoEZN+qx3///TdfffUVwcHBZlPET548Sf78+TM9xtnZmevXr5OcnEylSpWM7SNGjOCll17i7bffzpa+ioiIiIj1y/m7YB+zihUr0rhxY8aMGcPmzZuJjY0lLCyMqKgovL29gdtTXnPlykVISAhnzpwhOjqaefPmGXXky5cPLy8vZsyYwcaNG4mNjWXKlCns378/y/0oWLAgXbt2JSQkhNWrV3PmzBkiIyMJCgoyEqjMJCQkmK1M+yjWrVvH4sWLOXXqFF988QUbNmygX79+wIPHDpA/f36uXbtGbGwsKSkpJCcnc/HiRZKTk7PUft++ffnpp5+YOXMmsbGxbN68mXHjxtGkSROcnJwoWbIkiYmJfP/995w7d45ffvnFuGcyvY305OfIkSPGCrS5cuViyJAh7N+/n/379/Pxxx9Tt25dateuTcmSJQFYs2YNcXFx7N69m4EDBxr9fxS+vr4UK1aMESNGPNLxj2L48OEUL16cQYMGERAQwI0bN/jss88AKFWqFEePHmX37t2cPXuWlStXMn36dICHGmP+/PmJiYkxVuG9n/R7VaOiojh9+jRHjx5l5MiRHDp0yKxNBwcHTp06xaVLl3j55ZepXLkyQ4YMYfv27Zw+fZpJkyaxcuXKDNOPRUREROTZ8swlqADBwcF4enoyYsQI2rZty6ZNmwgNDeX1118H4IUXXmDs2LFs2bKFli1bGquR3un999+nbdu2jBgxgvbt23P27Fk8PT0faopiQEAAvXv3ZsaMGbRs2ZKZM2fi6+vL4MGD73nM+PHj6dSp06MN/P9r3749P/30E23atOHbb78lJCTEmD6blbE3b96cYsWK0bZtWw4fPsy+ffto1KjRPR/HcreWLVsyZcoUfvjhB9q0acPo0aNp3bo1ISEhALz++uv07duXSZMm0bJlSyZMmECnTp2oU6cOBw8eBG5fhWvcuDFDhgxh2bJlwO3FeNq1a8fAgQPp06cPTk5ORoJWvXp1AgICWLhwIS1btiQgIIA6derwxhtvcODAgUeKY548eRg3bhzbt283+pCdlixZwq+//sqECROwt7enSJEijBw5kuXLlxMVFcV7771HjRo1GDBgAO3bt2f58uVMmDCBvHnzGnHLCm9vbxYvXpzhfc+MnZ0d06dP59ixY7Rp04Z+/fqRmJjI0KFDOX78ODdu3ABuL0gVHR2Nt7c3tra2zJs3j+rVq/PBBx/Qtm1bduzYQWhoKPXr13/k+IiIiIjIk8/GlF1z/55yGzZswN3d3WyVWm9vb0qWLMmECRNysGf317RpUzp06HDfJFjkSTRkWjQxcdeytQ2n0oUJGfoqf//9T47fn5Gd7OxyUbSow1M/zsdJMbU8xTR7KK6Wp5hanmJqedkdU0dHhywvkvRMXkG1hIiICD788EP++OMPzpw5w/z58/ntt99o27ZtTndNRERERETkifTMLZJkKVOmTGHixIn07t2bpKQkKlasyPTp0/Hw8MjprskTaOzYsURGRt63zPTp03nllVceU49ERERERB4/JaiPqEyZMmarpT4pNm3alNNdkEz4+vqarWqcmeLFiz+m3oiIiIiI5AwlqCJWwNHR0ex+ZhERERGRZ5ESVBF5KpQpUfCpaENERETkWaYEVUSeeCaTiWE93B9LW6mpaaSlafFzERERkeygBFVEnng2NjbExyeSmpr9S82npZmUoIqIiIhkEyWoIvJUSE1N07PQRERERJ5weg6qiIiIiIiIWAUlqCIiIiIiImIVlKCKiIiIiIiIVVCCKiIiIiIiIlZBCaqIiIiIiIhYBSWoIiIiIiIiYhWUoIqIiIiIiIhVUIIqIiIiIiIiVkEJqoiIiIiIiFgFJagiIiIiIiJiFZSgioiIiIiIiFVQgioiIiIiIiJWQQmqiIiIiIiIWAUlqCIiIiIiImIVlKCKiIiIiIiIVVCCKiIiIiIiIlZBCaqIiIiIiIhYBSWoIiIiIiIiYhWUoIqIiIiIiIhVsMvpDoiIWIKt7eP9vi0tzURamumxtikiIiLytFOCKiJPPJPJRKFC+R5rm6mpaVy9ekNJqoiIiIgFKUEVkSeejY0NU5bs4ez5hMfSXpkSBRnWw51cuWyUoIqIiIhYkBJUEXkqnD2fQEzctZzuhoiIiIj8C1okSURERERERKyCElQRERERERGxCkpQRURERERExCooQRURERERERGroARVnlg7duzAxcWFs2fPZms7Xl5e+Pv7Z2sbd7tx4wZLliy5b5m5c+fi4eGBm5sbGzduzLa+uLi4sGrVKovVFxoaStOmTS1Wn4iIiIg8PbSKr8gDhIaGYmtr+1jbnDdvHqtWraJHjx6Z7o+Pj2fatGm88847vPXWWzg6Oj7W/omIiIiIZAclqCIPUKRIkcfepsl0/2drJiQkYDKZaNCgAaVLl35MvRIRERERyV5KUOVfc3FxYcCAAaxZs4bk5GQWLVrEf/7zH2bPns3atWu5cOECTk5O+Pr68tprrwGwatUqAgICOHr0qFHPjh076NmzJ1FRUZQpU4bU1FRmzJjBypUrSUhI4JVXXqFkyZIcOXKERYsWGcdt2bKFr7/+mtjYWMqVK8dHH33Eq6++CtxO9L744gu+/vprLl26RPny5enbty9t27Y1a3PYsGF88cUX/Oc//2HFihVmV0y9vLwoXbo0EydOZNWqVYSFhTF48GBmzZrF//73P1xcXBgxYgQ1a9YEoGnTpnTp0oV9+/axfft2SpQowTvvvEPnzp2zNPbIyEjCwsKM2KbH4+6yAL169aJ06dJs2rSJhIQEJk+ezIYNG0hJSaFq1ap89NFHuLq6Gsdu3ryZ0NBQTpw4QYkSJWjdujUDBw7E3t4egL/++osxY8bw22+/UbhwYT766KNHOh8CAwNZs2YNhw4doly5cgwZMgRPT0+zcrNmzWLhwoWkpKTQunVr/P39yZ8//0O3l5NsbZ/OuyTSx/W0ji8nKKaWp5hmD8XV8hRTy1NMLc+aYqoEVSxi2bJlhIeHk5qaSoUKFRg4cCCHDx9m1KhRvPjii6xbtw5fX19mzpyZIVG5lylTphAZGcnYsWNxcnJi6dKlLFq0iDp16piVW7hwIWPHjqV48eJMmTKFIUOGsG3bNhwcHAgODmbt2rWMGjUKJycndu3aRWBgIAkJCWbTZ6Ojo1m2bBmJiYkPnM574cIFvv76a4KCgsidOzeBgYH4+fnx448/YmNjA8DMmTPx8fHBz8+PrVu3MmrUKBwcHGjVqtUDx+3t7c2NGzdYv349K1asyDB9t2bNmixfvpzOnTsTGhpK7dq1MZlM9O/fn9y5czNnzhwKFCjAt99+S7du3fjmm2+oUqUKW7du5f333ycgIICGDRvy559/Mm7cOGJjY5k+fTq3bt2iX79+FChQgMWLF5OcnMyYMWOy9F7dbfLkyQwbNozx48ezatUqfH19WbJkCbVq1QIgLi6O7du3M2/ePK5fv87IkSP58MMP+fzzzx+pvZxSqFC+nO5Ctnrax5cTFFPLU0yzh+JqeYqp5SmmlmcNMVWCKhbRrl0740pdTEwMUVFRzJ49myZNmgDg6+vL0aNHmT17dpYS1MTERJYuXUpAQADNmzcHYOTIkezbty9D2eHDh1OvXj0ABg0axMaNG4mJiaFixYrMnz+fyZMnG/0oW7YscXFxREREmCWo3t7elC9fPktjTUlJITAwkMqVKwPg4+PDoEGDuHjxIsWLFwegYcOG+Pr6AlChQgUOHDjAggULspSgOjg4kD9/fmxtbSlWrFiG/fb29kbSWrhwYRwdHdm+fbtxxTZ939ChQ9m7dy8LFy5k4sSJzJ49m06dOtGtWzcjFmPGjKFXr16cPXuW2NhYjh8/zoYNGyhbtiwAn332Ge3bt89SXO7UsWNHI77Dhg1j165dLF682EhQ7e3tCQ4O5vnnnwdg1KhReHt7c/r0acqVK/fQ7eWU+PhEUlPTcrobFmdrm4tChfI9tePLCYqp5Smm2UNxtTzF1PIUU8vL7pgWKpQvy1dnlaCKRdyZVKRPXXV3dzcrU7t2baZOnZql+mJiYkhKSqJGjRpm293d3Tly5IjZthdffNF4XahQIQCSkpI4ceIEN2/exM/Pj4CAAKPMrVu3SE5OJikpydiW1eQ0nZOTk/G6YMGCwO3ENV16wpyuRo0aREdHP1QbD+PQoUMAGZL/5ORkbt68CcDhw4c5ePAgkZGRxv70e11jYmI4ceIEhQsXNpJTgMqVK5Mv38N/k1a3bl2zn93c3Pj111+Nn8uXL28kp+n7AY4fP/5EJaipqWncuvX0/sf4tI8vJyimlqeYZg/F1fIUU8tTTC3PGmKqBFUsIm/evA8sk5aWhp2d+SlnMpmMabG3bt0ytqeXe9BiQQC5cmX8NsZkMhnHhoSEUKFChQxl0u+7BMiTJ88D27nXsXe2mS6zcd7dz3uN/VGkpaVRoECBTB8Hk97XtLQ0+vXrR4cOHTKUKVasGCdOnMg03nePJSvuPiYtLc1s/HdPo05NTQUgd+7cD92WiIiIiDw9cv4uWHnqODs7A7Bnzx6z7bt376ZixYrA/yUiCQkJxv7Tp08br8uVK0fevHnZv3+/WR0HDx7Mcj8qVKiAnZ0d586do1y5csa/LVu2EBERkWliaym///672c979+6lSpUqwIPHDhiJa1Y5Oztz/fp1kpOTzcYaHh5OVFQUAJUqVeLkyZNm+8+fP8/kyZP5559/qFKlCvHx8Rw/ftyoNzY21qyfWXX3+Pfv30/VqlWNn0+dOsX169eNn/fs2YONjY1xfoiIiIjIs0kJqlhcxYoVady4MWPGjGHz5s3ExsYSFhZGVFQU3t7ewO0pr7ly5SIkJIQzZ84QHR3NvHnzjDry5cuHl5cXM2bMYOPGjcTGxjJlypQMCev9FCxYkK5duxISEsLq1as5c+YMkZGRBAUFmU0vvVtCQgJXrlx55PEDrFu3jsWLF3Pq1Cm++OILNmzYQL9+/YAHjx0gf/78XLt2jdjYWFJSUkhOTubixYskJydn2t7LL79M5cqVGTJkCNu3b+f06dNMmjSJlStXGtOR+/fvz08//URoaCixsbFs376dgIAA4uPjKVasGPXq1cPNzY2PP/6Y/fv38/vvv+Pv7/9IifyCBQtYu3YtsbGxTJo0iSNHjtCrVy9j/82bNxkyZAiHDx9m27ZtjBs3jvbt2+uROSIiIiLPOE3xlWwRHBzMtGnTGDFiBPHx8VSqVInQ0FCaNWsGwAsvvMDYsWOZPXs233zzDVWrVmX48OG8++67Rh3vv/8+KSkpjBgxgsTERJo0aYKnp6dxT2VWBAQE4OjoyIwZM7hw4QIlS5bE19eXd955557HjB8/np07d7Jp06ZHHn/79u356aefmDRpEuXLlyckJITGjRtneezNmzfnm2++oW3btixevJikpCR69uzJwoULM9zfCrenzM6bN4+goCA++OADEhMTcXJyIjQ0lPr16wPw+uuvExwczJw5c5gzZw6FCxemSZMmxqNkcuXKxZw5c/j000/x9vYmb968+Pj4cPbs2Yce/1tvvcWXX37J8ePHeemll4iIiOCll14y9lerVo3KlSvTs2dPbGxsaNWqFf7+/g/djoiIiIg8XWxMWbnJTyQHbNiwAXd3d7PHrHh7e1OyZEkmTJiQgz27v6ZNm9KhQwcGDx6c013JES4uLnz22We8+eabj7XdIdOiiYm79ljacipdmJChr/L33//k+EIC2cHOLhdFizo8tePLCYqp5Smm2UNxtTzF1PIUU8vL7pg6OjpkeRVfTfEVqxUREcGHH37IH3/8wZkzZ5g/fz6//fYbbdu2zemuiYiIiIhINtAUX7FaU6ZMYeLEifTu3ZukpCQqVqzI9OnT8fDwyOmuPZMGDBjAjh077ltmxYoVj6k3IiIiIvI0UoIqVqtMmTKEhYXldDce2r+5d9WajRkzxuzZsZkpVaqU8RxcEREREZGHpQRVRLKkRIkSOd2F+ypTouBT2ZaIiIjIs0QJqog88UwmE8N6uD/WNlNT00hL0xpzIiIiIpakBFVEnng2NjbExyeSmvr4VvJLSzMpQRURERGxMCWoIvJUSE1N01LzIiIiIk84PWZGRERERERErIISVBEREREREbEKSlBFRERERETEKihBFREREREREaugBFVERERERESsghJUERERERERsQpKUEVERERERMQqKEEVERERERERq6AEVURERERERKyCElQRERERERGxCkpQRURERERExCooQRURERERERGroARVRERERERErIISVBEREREREbEKSlBFRERERETEKihBFREREREREaugBFVERERERESsghJUERERERERsQpKUEVERERERMQq2OV0B0RELMHWNue/b0tLM5GWZsrpboiIiIg8sZSgisgTz2QyUahQvpzuBqmpaVy9ekNJqoiIiMgjUoIqIk88GxsbpizZw9nzCTnWhzIlCjKshzu5ctkoQRURERF5REpQReSpcPZ8AjFx13K6GyIiIiLyL+T8TVsiIiIiIiIiKEEVERERERERK6EEVURERERERKyCElQRERERERGxCkpQ5Ym1Y8cOXFxcOHv2bLa24+Xlhb+/f7a2cbcbN26wZMmS+5aZO3cuHh4euLm5sXHjxmzri4uLC6tWrcq2+kVERERE0mkVX5EHCA0NxdbW9rG2OW/ePFatWkWPHj0y3R8fH8+0adN45513eOutt3B0dHys/RMRERERyQ5KUEUeoEiRIo+9TZPp/s/RTEhIwGQy0aBBA0qXLv2YeiUiIiIikr00xVf+NRcXF4KDg2nSpAkNGzbk5MmTJCUlERISgqenJ66urrRv395sGuqqVatwcXExq+fuKbupqakEBwfTqFEj3NzcGDx4MOPHj8fLy8vsuC1bttCmTRuqVatG69atiY6ONvaZTCbCw8Px9PTEzc2Ndu3asWbNmgxthoeHU69ePTp06EBqaqpZ/XdO8V21ahVNmzYlMjKSZs2aUa1aNTp27Mi+ffuM8k2bNmX27Nn4+PhQvXp1mjVrxvLly7M89tDQUMLCwoiLi8t0CvOOHTto2rQpAL169TJeJyQkMHLkSDw8PHB3d6dnz578/vvvZsdu3ryZN9980+hXSEgIycnJxv6//vqLd999l5o1a/Lqq6+ybt06Htbx48cZOHAg9erVo1q1ajRr1owFCxYY+0NDQ40+3y8mIiIiIvLs0RVUsYhly5YRHh5OamoqFSpUYODAgRw+fJhRo0bx4osvsm7dOnx9fZk5cyaenp5ZqnPKlClERkYyduxYnJycWLp0KYsWLaJOnTpm5RYuXMjYsWMpXrw4U6ZMYciQIWzbtg0HBweCg4NZu3Yto0aNwsnJiV27dhEYGEhCQoLZ9Nno6GiWLVtGYmLiA6fzXrhwga+//pqgoCBy585NYGAgfn5+/Pjjj9jY2AAwc+ZMfHx88PPzY+vWrYwaNQoHBwdatWr1wHF7e3tz48YN1q9fz4oVKzJM361ZsybLly+nc+fOhIaGUrt2bUwmE/379yd37tzMmTOHAgUK8O2339KtWze++eYbqlSpwtatW3n//fcJCAigYcOG/Pnnn4wbN47Y2FimT5/OrVu36NevHwUKFGDx4sUkJyczZsyYLL1X6RITE+nTpw8eHh4sXboUOzs7Vq5cyYQJE6hbty6VK1d+qPqeRLa2T/73fuljeBrGYi0UU8tTTLOH4mp5iqnlKaaWZ00xVYIqFtGuXTtcXV0BiImJISoqitmzZ9OkSRMAfH19OXr0KLNnz85SgpqYmMjSpUsJCAigefPmAIwcOdLsSmW64cOHU69ePQAGDRrExo0biYmJoWLFisyfP5/Jkycb/ShbtixxcXFERESYJaje3t6UL18+S2NNSUkhMDDQSLZ8fHwYNGgQFy9epHjx4gA0bNgQX19fACpUqMCBAwdYsGBBlhJUBwcH8ufPj62tLcWKFcuw397e3khaCxcujKOjI9u3b2ffvn1s377d2Dd06FD27t3LwoULmThxIrNnz6ZTp05069bNiMWYMWPo1asXZ8+eJTY2luPHj7NhwwbKli0LwGeffUb79u2zFBe4/b717NmT7t27U6BAAeD2ez9nzhyOHj36TCSohQrly+kuWMzTNBZroZhanmKaPRRXy1NMLU8xtTxriKkSVLGIcuXKGa+PHj0KgLu7u1mZ2rVrM3Xq1CzVFxMTQ1JSEjVq1DDb7u7uzpEjR8y2vfjii8brQoUKAZCUlMSJEye4efMmfn5+BAQEGGVu3bpFcnIySUlJxrasJqfpnJycjNcFCxYEbieu6dIT5nQ1atQwm3psaYcOHQLIkPwnJydz8+ZNAA4fPszBgweJjIw09qff6xoTE8OJEycoXLiwkZwCVK5cmXz5sv6LytHRke7du7N+/XqOHDnC6dOn+eOPPwBIS0t7tME9YeLjE0lNfbLHamubi0KF8j0VY7EWiqnlKabZQ3G1PMXU8hRTy8vumBYqlC/LV2eVoIpF5M2b94Fl0tLSsLMzP+VMJpMxLfbWrVvG9vRyD1osCCBXrownu8lkMo4NCQmhQoUKGcrY29sbr/PkyfPAdu517J1tpstsnHf3815jfxRpaWkUKFAg08fBpPc1LS2Nfv360aFDhwxlihUrxokTJzKN991juZ9Lly7RpUsXihYtiqenJ/Xr18fV1ZXGjRublbu7nX87fmuSmprGrVtPx3+WT9NYrIVianmKafZQXC1PMbU8xdTyrCGmOT/JWJ46zs7OAOzZs8ds++7du6lYsSIAuXPnBm4v7JPu9OnTxuty5cqRN29e9u/fb1bHwYMHs9yPChUqYGdnx7lz5yhXrpzxb8uWLURERGSa2FrK3YsT7d27lypVqgAPHjtgJK5Z5ezszPXr10lOTjYba3h4OFFRUQBUqlSJkydPmu0/f/48kydP5p9//qFKlSrEx8dz/Phxo97Y2Fizfj7I2rVruXr1Kl9//TUDBw6kWbNmXLt2Dfi/pDR37txcv37dLEm9e/wiIiIi8mxSgioWV7FiRRo3bsyYMWPYvHkzsbGxhIWFERUVhbe3N3B7ymuuXLkICQnhzJkzREdHM2/ePKOOfPny4eXlxYwZM9i4cSOxsbFMmTIlQ8J6PwULFqRr166EhISwevVqzpw5Q2RkJEFBQTz//PP3PC4hIYErV6488vgB1q1bx+LFizl16hRffPEFGzZsoF+/fsCDxw6QP39+rl27RmxsLCkpKSQnJ3Px4kWzFXfv9PLLL1O5cmWGDBnC9u3bOX36NJMmTWLlypXGdOT+/fvz008/ERoaSmxsLNu3bycgIID4+HiKFStGvXr1cHNz4+OPP2b//v38/vvv+Pv7P1QiX7JkSRITE/n+++85d+4cv/zyC0OHDgUw+l6rVi3i4+OZO3cuZ8+eZe3atZle+RURERGRZ48SVMkWwcHBeHp6MmLECNq2bcumTZsIDQ3l9ddfB+CFF15g7NixbNmyhZYtW/L5558zfPhwszref/992rZty4gRI2jfvj1nz57F09Mz0+m19xIQEEDv3r2ZMWMGLVu2ZObMmfj6+jJ48OB7HjN+/Hg6der0aAP//9q3b89PP/1EmzZt+PbbbwkJCTGmuWZl7M2bN6dYsWK0bduWw4cPs2/fPho1apTpIlEAtra2zJs3j+rVq/PBBx/Qtm1bduzYQWhoKPXr1wfg9ddfJzg4mKioKNq0acOwYcOoX78+YWFhwO2p0nPmzKFChQp4e3vj4+NDq1atMqwifD+vv/46ffv2ZdKkSbRs2ZIJEybQqVMn6tSpY1z9rlu3Lh988AGLFy+mVatWrF69Gj8/v4eOsYiIiIg8fWxMWbnJTyQHbNiwAXd3d7MEydvbm5IlSzJhwoQc7Nn9NW3alA4dOtw3CRbLGzItmpi4aznWvlPpwoQMfZW///4nx+/d+Lfs7HJRtKjDUzEWa6GYWp5imj0UV8tTTC1PMbW87I6po6NDlhdJ0hVUsVoRERF8+OGH/PHHH5w5c4b58+fz22+/0bZt25zumoiIiIiIZAOt4itWa8qUKUycOJHevXuTlJRExYoVmT59Oh4eHjndtWfSgAED2LFjx33LrFixwuwRPCIiIiIiD0MJqlitMmXKGPdHPkk2bdqU013IFmPGjDF7dmxmSpUq9Zh6IyIiIiJPIyWoIpIlJUqUyOku3FeZEgWf6fZFREREngZKUEXkiWcymRjWwz2nu0FqahppaVp3TkRERORRKUEVkSeejY0N8fGJpKbm7Ep+aWkmJagiIiIi/4ISVBF5KqSmpmmpeREREZEnnB4zIyIiIiIiIlZBCaqIiIiIiIhYBSWoIiIiIiIiYhWUoIqIiIiIiIhVUIIqIiIiIiIiVkEJqoiIiIiIiFgFJagiIiIiIiJiFZSgioiIiIiIiFVQgioiIiIiIiJWQQmqiIiIiIiIWAUlqCIiIiIiImIVlKCKiIiIiIiIVVCCKiIiIiIiIlZBCaqIiIiIiIhYBSWoIiIiIiIiYhWUoIqIiIiIiIhVUIIqIiIiIiIiVkEJqoiIiIiIiFgFJagiIiIiIiJiFexyugMiIpZga6vv29KlpZlISzPldDdEREREHpoSVBF54plMJgoVypfT3bAaqalpXL16Q0mqiIiIPHGUoIrIE8/GxoYpS/Zw9nxCTnclx5UpUZBhPdzJlctGCaqIiIg8cZSgishT4ez5BGLiruV0N0RERETkX9BNWyIiIiIiImIVlKCKiIiIiIiIVVCCKiIiIiIiIlZBCaqIiIiIiIhYBSWoFrZjxw5cXFw4e/Zstrbj5eWFv79/trZxtxs3brBkyZL7lpk7dy4eHh64ubmxceNGXFxcWLVqlcX6YDKZiIyM5PLlywCsWrUKFxcXi9V/L02bNiU0NPSB5dLS0ujbt2+mZb///ntatWqFq6srbdq0YevWrdnR1X/l+PHjREdHGz9b4v07d+4c69at+5c9ExEREZFngRLUJ1RoaCiffPLJY21z3rx5RERE3HN/fHw806ZNo0uXLqxfv56GDRtavA+7du3C39+fxMREi9f9byUlJfHRRx/xyy+/ZNj322+/8dFHH9G9e3dWr15No0aNGDRoEDExMTnQ03vz8fHh999/t2idfn5+/PzzzxatU0RERESeTkpQn1BFihShYMGCj7VNk+n+z1RMSEjAZDLRoEEDSpcuTb58+R57H3LK3r176dChAwcOHKBQoUIZ9oeHh9OsWTPefvttnJyc8PPzo2rVqixYsCAHeisiIiIiYp2e2gTVxcWF4OBgmjRpQsOGDTl58iRJSUmEhITg6emJq6sr7du3Z+PGjcYxmU0XvXvKbmpqKsHBwTRq1Ag3NzcGDx7M+PHj8fLyMjtuy5YttGnThmrVqtG6dWuzaZMmk4nw8HA8PT1xc3OjXbt2rFmzJkOb4eHh1KtXjw4dOpCammpW/51TfFetWkXTpk2JjIykWbNmVKtWjY4dO7Jv3z6jfNOmTZk9ezY+Pj5Ur16dZs2asXz58iyPPTQ0lLCwMOLi4jKdwrxjxw6aNm0KQK9evYzXd4uOjqZLly7UrFmTRo0aMXHiRG7evGnsP378OAMHDqRevXpUq1aNZs2aGUncjh076NmzJwCenp5mU0+XL1/OK6+8Qo0aNXjvvfe4cuWKse+vv/5i2LBhNGjQgKpVq9K4cWOCg4NJS0vLcvzudOPGDXr06EGbNm2MqcY///wzzZo1Y/Xq1Rm+OEhLS2Pv3r14eHiYba9Xrx67d+/OtI27nTlzhlq1avHpp5+ajblq1aocOHAAgD179tCnTx/c3d2pVq0ab7zxBt99951R3t/fn48++ohJkyZRv3593NzcGDhwIBcvXgRunyNxcXGEhYWZnc+xsbH06dOH6tWr06hRI+bMmWPsM5lMfPHFF7Rs2ZJq1arh7u6Oj48PZ86cAW6fpzt37iQyMtI4J5KTkwkKCuLll1+mZs2adOnSJdOrziIiIiLy7LHL6Q5kp2XLlhEeHk5qaioVKlRg4MCBHD58mFGjRvHiiy+ybt06fH19mTlzJp6enlmqc8qUKURGRjJ27FicnJxYunQpixYtok6dOmblFi5cyNixYylevDhTpkxhyJAhbNu2DQcHB4KDg1m7di2jRo3CycmJXbt2ERgYSEJCAj169DDqiI6OZtmyZSQmJmJra3vffl24cIGvv/6aoKAgcufOTWBgIH5+fvz444/Y2NgAMHPmTHx8fPDz82Pr1q2MGjUKBwcHWrVq9cBxe3t7c+PGDdavX8+KFStwdHQ021+zZk2WL19O586dCQ0NpXbt2hnq2LhxI4MHD8bX15eJEydy+vRpAgMDiYuLIzQ0lMTERPr06YOHhwdLly7Fzs6OlStXMmHCBOrWrUvNmjUJDQ1l8ODBLF++HGdnZ9avX2/EOyQkBHt7e8aNG4e3tzeRkZHY2Njg4+PDc889R0REBAUKFCA6OppPP/0UV1dXXnvttSzHD25P4x0wYAD//PMPCxYsMOLw/vvv3zN28fHx3Lhxg5IlS5ptL168OP/73/8eGHuAF154geHDhzNy5EhatmzJ888/z4QJE3jvvfdwc3Pj/PnzeHt70717dwIDA7l16xZffPEFAQEBeHh48PzzzwO374Nt06YNixcv5ty5cwwbNozg4GAmTJjAihUr6NChA61atcLHx8doe/HixYwePZqxY8eydu1apk2bRvXq1alfvz4LFixgzpw5TJo0yfjiYuTIkUycOJGZM2cSGhrKgAEDKFmyJKNGjQIgICCA48ePExQURMmSJdm8eTMDBgwgLCyMV199NUvxkAeztX307x/Tj/03dYg5xdTyFNPsobhanmJqeYqp5VlTTJ/qBLVdu3a4uroCEBMTQ1RUFLNnz6ZJkyYA+Pr6cvToUWbPnp2lBDUxMZGlS5cSEBBA8+bNARg5cmSmV9qGDx9OvXr1ABg0aBAbN24kJiaGihUrMn/+fCZPnmz0o2zZssTFxREREWGWoHp7e1O+fPksjTUlJYXAwEAqV64M3L6XcNCgQVy8eJHixYsD0LBhQ3x9fQGoUKECBw4cYMGCBVlKUB0cHMifPz+2trYUK1Ysw357e3sjWStcuHCGBBZgzpw5NGvWjEGDBhl9MJlMvPvuu8TExFC0aFF69uxJ9+7dKVCgAHD7PZozZw5Hjx6lcuXKFC5cGABHR0fy5s1r1B0UFMRLL70EwKRJk2jRogXbt2+nVq1atGvXjhYtWlC6dGng9lW9uXPncvToUSNBzUr8bt68ybvvvsv169eZP38+RYoUeWDc4HZSmx6jO+XJk8fs6vGDdOrUic2bNzNy5EgKFixI9erV6d+/P3D7qqSvry99+/YlV65cxhhWrVrFqVOnjAS1QIECjB07lty5c+Pk5ES7du3YsmWLEVNbW1vy589vNrZu3brRvn17AAYOHMi8efP473//S/369SlbtiwTJ040ro6WLl2ali1bGosiFSlShNy5c5M3b14cHR05ffo03333HStWrDA+m3369OHIkSNEREQoQbWgQoX+/RR7S9Qh5hRTy1NMs4fianmKqeUpppZnDTF9qhPUcuXKGa+PHj0KgLu7u1mZ2rVrM3Xq1CzVFxMTQ1JSEjVq1DDb7u7uzpEjR8y2vfjii8br9HsSk5KSOHHiBDdv3sTPz4+AgACjzK1bt0hOTjaSGSDLyWk6Jycn43X6NNOUlBRjW3rCnK5GjRpmU4+z27Fjx2jdurXZtvQrz0ePHqVVq1Z0796d9evXc+TIEU6fPs0ff/wBYEzHzYyDg4ORnMLtuBUuXJhjx47RoEED3n77bX744QcWLFjA6dOnOXLkCBcuXMhQ54Pit2DBAlJSUqhXr56RKGdFnjx5gNtJ5J1u3rz50Pfpjhs3jpYtW3Lu3Dl+/PFHIxl94YUX6NixI4sXL+bEiROcOnXKiN2d08PLlStH7ty5zcZ55xgzc+e5DLfP5/TEumnTphw4cIAZM2Zw+vRpYmJiOH78OCVKlMi0rsOHDwMYU7XTpaSkZHrvrjy6+PhEUlPv/bm5H1vbXBQqlO9f1SHmFFPLU0yzh+JqeYqp5SmmlpfdMS1UKF+Wr84+1QnqnVfY7iUtLQ07O/MwmEwmY1rnrVu3jO3p5bKyUE964nB3venHhoSEUKFChQxl7rzKlp7YZNXdV+jS20yX2Tjv7ue9xm4Jd9adLj15srOz49KlS3Tp0oWiRYvi6elJ/fr1cXV1pXHjxvetN7Ppz2lpadjb25OYmEiPHj1ITEykZcuWtGvXjpEjR5pdqU73oPg5Ozvj5+dHnz59+Prrr+nWrVuWxl2kSBHy58/PhQsXzLZfuHAhw7TfB/nzzz9JSEgAbt9zmn71OyYmhm7dulGlShUaNmyIp6cnRYsWpXPnzg8c44NkFt/0uISHhxMaGsqbb75J3bp18fLyIioq6p6PlUk/bsmSJTg4OJjty+wzI48uNTWNW7f+3X8wlqhDzCmmlqeYZg/F1fIUU8tTTC3PGmL6VCeod3J2dgZu/1GfPrUWYPfu3VSsWBHAuLKUkJBgXM05ffq0UbZcuXLkzZuX/fv3G1NBAQ4ePJjlP/wrVKiAnZ0d586dM+vHwoULOXHiBGPHjn3EET7Y3Y8P2bt3L1WqVAEePHYgQ3L5sJydndmzZw+9evUytqUvEuTk5MTatWu5evUqP/74o9Gf9Cvf6YlNZn2Ij4/nzz//pGzZssYxCQkJODs78/PPP3Po0CG2bdtmTHO9evUqly9ffugVgV999VXq1q2Lt7c3QUFBvPLKK8a04fuxsbGhVq1a7Ny50yxh3LFjR4Yr+vdz48YNPv74Y1q1akXZsmUJDAzE3d2dEiVK8NVXX/Hcc88xf/58o/ymTZuA7F35+PPPP8fX15d33nnH2BYREXHPNitVqgTcTs7vnM4bHByMjY0NQ4YMyba+ioiIiIj1e2YuWVSsWJHGjRszZswYNm/eTGxsLGFhYURFReHt7Q3cnvKaK1cuQkJCOHPmDNHR0cybN8+oI1++fHh5eTFjxgw2btxIbGwsU6ZMYf/+/VnuR8GCBenatSshISGsXr2aM2fOEBkZSVBQkJFAZSYhIcFsZdpHsW7dOhYvXsypU6f44osv2LBhA/369QMePHaA/Pnzc+3aNWJjY0lJSSE5OZmLFy9mmLp6L3379uWnn35i5syZxMbGsnnzZsaNG0eTJk1wcnKiZMmSJCYm8v3333Pu3Dl++eUXhg4dCvzf9Nj8+fMDcOTIEf755x/g9pW3IUOGsH//fvbv38/HH39M3bp1qV27tnGFcs2aNcTFxbF7924GDhxo9P9R+Pr6UqxYMUaMGJHlY/r06cO6dev48ssviYmJYfLkyfzxxx9myfqDTJw4kX/++YcRI0YwYMAAihcvTkBAACaTiZIlS/LXX3+xZcsW4uLi+OmnnwgMDAQyTi2+HwcHB06dOsWlS5eyVL5UqVJs27aNEydOcPLkSYKDg/npp5/M2nRwcCAuLo6//vqLSpUq0aRJE0aPHk1UVBRnzpwhIiKCOXPm8MILL2S5nyIiIiLydHpmElS4fZXG09OTESNG0LZtWzZt2kRoaCivv/46cPs+vrFjx7JlyxZatmzJ559/zvDhw83qeP/992nbti0jRoygffv2nD17Fk9Pz4eaOhkQEEDv3r2ZMWMGLVu2ZObMmfj6+jJ48OB7HjN+/Hg6der0aAP//9q3b89PP/1EmzZt+PbbbwkJCTGmz2Zl7M2bN6dYsWK0bduWw4cPs2/fPho1anTPx7HcrWXLlkyZMoUffviBNm3aMHr0aFq3bk1ISAgAr7/+On379mXSpEm0bNmSCRMm0KlTJ+rUqcPBgweB21dhGzduzJAhQ1i2bBlwe3Gfdu3aMXDgQPr06YOTkxPTp08HoHr16gQEBLBw4UJatmxJQEAAderU4Y033jAez/Kw8uTJw7hx49i+fbvRhwdp1KgREyZM4KuvvqJDhw789ttvzJ492+y+1/tJX9F5zJgxFClSBHt7e8aPH8/27dtZtGgRPXv2pGXLlnz88ce88cYbfP755wwdOpTSpUsbscsKLy8voqOjjS9tHmTy5MkkJSXRsWNH3n77bY4dO8aYMWO4fPmy8Siirl27cuzYMdq2bWs8pqlFixaMHj2aVq1asXLlSsaNG0fHjh2z3E8REREReTrZmLJz/t9TaMOGDbi7u5utUuvt7U3JkiWZMGFCDvbs/po2bUqHDh3umwSLPMmGTIsmJu5aTncjxzmVLkzI0Ff5++9/HvkeEju7XBQt6vCv6hBziqnlKabZQ3G1PMXU8hRTy8vumDo6OmR5kaRn6gqqJURERPDhhx/yxx9/cObMGebPn89vv/1G27Ztc7prIiIiIiIiT7RnZpEkS5kyZQoTJ06kd+/eJCUlUbFiRaZPn46Hh0dOd02eUOHh4cyaNeu+Zfz9/XnrrbceU49ERERERHKGEtSHVKZMGcLCwnK6Gw8tfUVXsT5dunShefPm9y1z55RyEREREZGnlRJUkRxWuHBhChcunNPdeOKVKVEwp7tgFRQHEREReZIpQRWRJ57JZGJYj6w/U/Zpl5qaRlqa1r8TERGRJ48SVBF54tnY2BAfn0hqqlbyA0hLMylBFRERkSeSElQReSqkpqZpqXkRERGRJ5weMyMiIiIiIiJWQQmqiIiIiIiIWAUlqCIiIiIiImIVlKCKiIiIiIiIVVCCKiIiIiIiIlZBCaqIiIiIiIhYBSWoIiIiIiIiYhWUoIqIiIiIiIhVUIIqIiIiIiIiVkEJqoiIiIiIiFgFJagiIiIiIiJiFZSgioiIiIiIiFVQgioiIiIiIiJWQQmqiIiIiIiIWAUlqCIiIiIiImIVlKCKiIiIiIiIVVCCKiIiIiIiIlZBCaqIiIiIiIhYBSWoIiIiIiIiYhXscroDIiKWYGur79ssJT2W94tpWpqJtDTT4+qSiIiIPCOUoIrIE89kMlGoUL6c7sZT534xTU1N4+rVG0pSRURExKKUoIrIE8/GxoYpS/Zw9nxCTnflmVCmREGG9XAnVy4bJagiIiJiUUpQReSpcPZ8AjFx13K6GyIiIiLyL+imLREREREREbEKSlBFRERERETEKihBFREREREREaugBFVERERERESsghLUp8COHTtwcXHh7NmzOdqPc+fOsW7duoc6ZvPmzZw4ceJftTt+/Hjc3d2pWbMm//3vf3FxcWHHjh3/qs47paSkMH/+fOPn0NBQmjZtarH678XFxYVVq1Y9sFxSUhJt2rTJtOySJUvw9PSkevXqvPXWW/z+++8P1YezZ8/y1ltvUa1aNTp37vxQx97L44qfiIiIiDx5lKCKxfj5+fHzzz9nuXxcXBwDBgzg8uXLj9zmkSNHWLhwIUOHDuW7776jQIECj1zXvXz33Xd89tlnFq/XEq5evYqPjw/Hjh3LsC8yMpKgoCCGDBnCqlWrKFeuHP369ePKlStZrn/hwoWcOXOGyMhIwsLCLNJnb29vVqxYYZG6REREROTpogRVcozJ9O+fnxgfHw/AK6+8QunSpbGzs/yTkyzRz+wQFRVFu3btSEjI/Nmfs2fP5u2336ZNmzZUrFiRCRMmkC9fvodKDhMSEqhQoQKVKlWiRIkSFum3g4MDjo6OFqlLRERERJ4uSlAtyMXFheDgYJo0aULDhg05efIkSUlJhISE4OnpiaurK+3bt2fjxo3GMatWrcLFxcWsnrun7KamphIcHEyjRo1wc3Nj8ODBjB8/Hi8vL7PjtmzZQps2bahWrRqtW7cmOjra2GcymQgPD8fT0xM3NzfatWvHmjVrMrQZHh5OvXr16NChA6mpqWb1nzp1ir59+xrTafv27cvRo0cB8PLyYufOnURGRhrTN//66y+GDRtGgwYNqFq1Ko0bNyY4OJi0tDTOnj2Lp6cnAD179iQ0NBSAmJgY+vfvT82aNWnUqBEffvghFy9ezDTeq1atMmLw2muvZYhHutWrV9O2bVuqV69O06ZNmT17Nmlpacb+PXv20KdPH9zd3alWrRpvvPEG3333ndFGQECA8f7eOXV41qxZeHh44O7uzqhRo7hx44ax7/jx4wwcOJB69epRrVo1mjVrxoIFC4z9oaGheHl5ER4eziuvvIKrqys9e/bk5MmTmY7h8uXLtGrVip49e5KYmAhAdHQ0Xl5efP3115mWP3XqFB4eHsY2Ozs7ateuza5duzJt425eXl6sWrWKXbt2ZXm6cVbHnn6OnD17FhcXF2bNmkXDhg1p2rSp8aWDiIiIiDx7LH+56Rm3bNkywsPDSU1NpUKFCgwcOJDDhw8zatQoXnzxRdatW4evry8zZ840ErQHmTJlCpGRkYwdOxYnJyeWLl3KokWLqFOnjlm5hQsXMnbsWIoXL86UKVMYMmQI27Ztw8HBgeDgYNauXcuoUaNwcnJi165dBAYGkpCQQI8ePYw6oqOjWbZsGYmJidja2prVP3ToUFxcXFi5ciW3bt1i0qRJ+Pr6smHDBkJDQxkwYAAlS5Zk1KhRAPj4+PDcc88RERFBgQIFiI6O5tNPP8XV1ZUmTZqwfPlyOnfuTGhoKA0bNuT8+fN0796d1q1b4+/vT2JiIqGhoXTt2pW1a9eSP39+s/60atWKAgUKMHjwYJYvX07ZsmW5fv26WZn58+czdepU/P39adiwIb///jtjx47l6tWr+Pv7c/78eby9venevTuBgYHcunWLL774goCAADw8PGjVqhUJCQlMmDCBX375hcKFC7Nz507i4uLYvn078+bN4/r164wcOZIPP/yQzz//nMTERPr06YOHhwdLly7Fzs6OlStXMmHCBOrWrUvlypUB2LdvH/ny5WPu3Ln8888/+Pn5MWbMGLNkDuDKlSv06tWLkiVLMmvWLPLmzQvAuHHj7nnO/PXXXwCUKlXKbHvx4sU5cuTIPY+7U2hoKIGBgfz111+EhoZSsGDBBx6T1bHfbc2aNSxYsIDExEQKFSqUpf5JzrO11XecWZUeK8XMchTT7KG4Wp5ianmKqeVZU0yVoFpYu3btcHV1BW5fDYyKimL27Nk0adIEAF9fX44ePcrs2bOzlKAmJiaydOlSAgICaN68OQAjR45k3759GcoOHz6cevXqATBo0CA2btxITEwMFStWZP78+UyePNnoR9myZYmLiyMiIsIsQfX29qZ8+fKZ9uXPP/+kYcOGlClTBjs7OyZMmMDJkydJS0ujSJEi5M6dm7x58+Lo6EhSUhLt2rWjRYsWlC5dGrh9RW7u3LkcPXqU1157zZjmWbhwYRwcHAgPD6d48eJGggsQEhKCh4cHP/zwA2+++aZZf/LmzUvhwoUBcHR0pEiRImYJavpV47ffftsYY/ny5bl69SqTJk1i0KBBJCcn4+vrS9++fcmV6/YH0sfHh1WrVnHq1Clq165tJGbFihUz6ra3tyc4OJjnn38egFGjRuHt7c3p06cpWLAgPXv2pHv37sY9sb6+vsyZM4ejR48aSdqtW7eYPHkyRYoUMeITFBRkNsZr167Ru3dvSpUqRVhYGHny5Mn0vblb+lVWe3t7s+158uTh5s2bWaqjSJEi5M2bl9y5c5uN/UHtZmXsd+vevTsVK1bMUhtiPQoVypfTXXjiKGaWp5hmD8XV8hRTy1NMLc8aYqoE1cLKlStnvE6f/uru7m5Wpnbt2kydOjVL9cXExJCUlESNGjXMtru7u2e4Evbiiy8ar9OvQiUlJXHixAlu3ryJn5+fMV0VbidIycnJJCUlGdvulZwCfPDBB0yYMIGvvvoKDw8PXn75ZVq2bGkkdnfKmzcvb7/9Nj/88AMLFizg9OnTHDlyhAsXLphNr73T4cOHiYmJoWbNmmbbb968SUxMzD37dS9Xrlzh0qVLGeJfp04dUlJSOHnyJG5ubnTs2JHFixdz4sQJTp06xR9//AGQYYrzncqXL28kpwBubm7A7emtr732Gt27d2f9+vUcOXKE06dPG3XeOfbnn3/eSE4BChYsSEpKilk706ZNIyUlhapVq2Y5OQWMq6zJyclm22/evEm+fNn3i8fR0TFLY7/bnZ8beXLExyeSmnrv91X+j61tLgoVyqeYWZBimj0UV8tTTC1PMbW87I5poUL5snx1VgmqhaUnBveTlpaWYTEfk8mEjY0NcDtxTJdeLisL9WSWKJpMJuPYkJAQKlSokKHMnVfZ7pcE9ejRg9dff50tW7awfft2pk2bRmhoKKtXrzZL1uD2lbQePXqQmJhIy5YtadeuHSNHjjS7Wnu3tLQ0PDw8GD16dIZ9WZleerd7xSw98bSzsyMmJoZu3bpRpUoVGjZsiKenJ0WLFn3gI1Xunv6cXmfu3Lm5dOkSXbp0oWjRonh6elK/fn1cXV1p3Lix2TF3X93MTIMGDejUqRO+vr60bNmSV1555YHHAPznP/8B4MKFCzg5ORnbL1y4QMmSJbNUx6PI6tjvlpXPjVif1NQ0bt3SHwYPQzGzPMU0eyiulqeYWp5iannWENOcn2T8FHN2dgZuL8Jzp927dxvTGXPnzg1gthLr6dOnjdflypUjb9687N+/36yOgwcPZrkfFSpUwM7OjnPnzlGuXDnj35YtW4iIiMg0sb3bpUuXGDt2LCkpKbz55psEBQWxZs0aLl68yM6dOzOU//nnnzl06BCLFi3ivffeM+4XvXz5spE4pifk6SpVqkRMTAylSpUy+li4cGEmTJiQ6WNUHuS5557jueeeyzT+uXPnpmzZsnz11Vc899xzzJ8/n/79+9O4cWMuXboEcM9+wu0Fo+6cTrxnzx5sbGyoWLEia9eu5erVq3z99dcMHDiQZs2ace3aNbM6s6pFixY0a9aM1q1bM3LkyAz32N6Lo6MjL774otmiTrdu3WL37t3Url37ofrwMCw5dhERERF59ihBzUYVK1akcePGjBkzhs2bNxMbG0tYWBhRUVF4e3sDUKNGDXLlykVISAhnzpwhOjqaefPmGXXky5cPLy8vZsyYwcaNG4mNjWXKlCkZEtb7KViwIF27diUkJITVq1cbz7UMCgrKcOXzTgkJCcYzM4sUKUJ0dDQjRozgjz/+4MyZMyxdupTcuXNTrVo14PbjQ+Li4vjrr7+Mq3Rr1qwhLi6O3bt3M3DgQFJSUoxpp+mLHh07doyEhAS6d+9OQkICQ4cO5Y8//uDIkSN8+OGHHDx4kEqVKgG3pyxfvHjxvtNv09nY2ODt7c3ixYtZsmQJp0+fZu3atYSFhfHWW29RsGBBSpYsyV9//cWWLVuIi4vjp59+IjAwECBDP//73/8a06Fv3rzJkCFDOHz4MNu2bWPcuHG0b9+e0qVLU7JkSRITE/n+++85d+4cv/zyC0OHDjWr82GNGDGCpKSkh3oeq7e3N19++SWRkZGcOHGC4cOHk5SURKdOnR6pD1mRHWMXERERkWeHpvhms+DgYKZNm8aIESOIj4+nUqVKhIaG0qxZMwBeeOEFxo4dy+zZs/nmm2+oWrUqw4cP59133zXqeP/990lJSWHEiBEkJibSpEkTPD09s7zYDUBAQACOjo7MmDHDmObp6+vLO++8c89jxo8fz86dO9m0aRN2dnaEh4czadIkevfuTWJiIpUrV2bu3LmULVsWgK5du+Ln50fbtm3Zvn07AQEBzJ8/n5CQEEqUKEGrVq0oVaoUBw4cAKBo0aJ07NiRyZMnc/r0aUaMGMHixYuZOnUq3bt3x9bWlho1arBgwQKee+45ANavX09AQABRUVGUKVPmgePu168f9vb2LFiwgM8++4ySJUvSv39/+vbtC2A82uXjjz8mOTmZ8uXLM3ToUGbMmMHBgwd55ZVX8PDwwM3Nja5duxqLGFWrVo3KlSvTs2dPbGxsaNWqFf7+/gC8/vrrHDp0iEmTJnH9+nVKly5N586diYqK4uDBg3Tr1i3L71s6R0dHAgIC8PPz4/XXX+fll19+4DFdunQhISGBkJAQrl69SrVq1fjyyy+z9Rmk2TF2EREREXl22Jg0787qbdiwAXd3d7PEwtvbm5IlSzJhwoQc7JmI9RgyLZqYuGs53Y1nglPpwoQMfZW///4nx+9TeVLY2eWiaFEHxcyCFNPsobhanmJqeYqp5WV3TB0dHbK8SJKm+D4BIiIi+PDDD42ptfPnz+e3336jbdu2Od01ERERERERi9EU3yfAlClTmDhxIr179yYpKYmKFSsyffp0PDw8crpr8oQaO3YskZGR9y0zffp0s1WDBwwYYLboUmZWrFhhtmqwiIiIiMjDUIL6BChTpgxhYWE53Q15ivj6+tKrV6/7lilevLjZz2PGjDF7Zm5mSpUq9a/7JiIiIiLPLiWoIs8gR0fHh14sqUSJEtnUG8soU+Lhn5Urj0axFhERkeyiBFVEnngmk4lhPdxzuhvPlNTUNNLStMaeiIiIWJYSVBF54tnY2BAfn0hqqlbyswRb21wUKpTvvjFNSzMpQRURERGLU4IqIk+F1NQ0LTVvYYqpiIiIPG56zIyIiIiIiIhYBSWoIiIiIiIiYhWUoIqIiIiIiIhVUIIqIiIiIiIiVkEJqoiIiIiIiFgFJagiIiIiIiJiFZSgioiIiIiIiFVQgioiIiIiIiJWQQmqiIiIiIiIWAUlqCIiIiIiImIVlKCKiIiIiIiIVVCCKiIiIiIiIlZBCaqIiIiIiIhYBSWoIiIiIiIiYhWUoIqIiIiIiIhVUIIqIiIiIiIiVkEJqoiIiIiIiFgFJagiIiIiIiJiFZSgioiIiIiIiFVQgioiIiIiIiJWwS6nOyAiYgm2tvq+zVLSY/ksxDQtzURamimnuyEiIiL/nxJUEXnimUwmChXKl9PdeOo8CzFNTU3j6tUbSlJFRESshBJUEXni2djYMGXJHs6eT8jprsgTpEyJggzr4U6uXDZKUEVERKyEElQReSqcPZ9ATNy1nO6GiIiIiPwLT/8NRiIiIiIiIvJEUIIqIiIiIiIiVkEJqoiIiIiIiFgFJahidXbs2IGLiwtnz57N1na8vLzw9/fP1jbuduPGDZYsWXLfMnPnzsXDwwM3Nzc2btyYbX1xcXFh1apVFqnrcb1nIiIiIvJ00yJJ8swKDQ3F1tb2sbY5b948Vq1aRY8ePTLdHx8fz7Rp03jnnXd46623cHR0fKz9e1Q1a9bkl19+eWL6KyIiIiLWSQmqPLOKFCny2Ns0me7/KIuEhARMJhMNGjSgdOnSj6lX/569vT3FihXL6W6IiIiIyBNOU3zlnlxcXAgODqZJkyY0bNiQkydPkpSUREhICJ6enri6utK+fXuzaairVq3CxcXFrJ67p3+mpqYSHBxMo0aNcHNzY/DgwYwfPx4vLy+z47Zs2UKbNm2oVq0arVu3Jjo62thnMpkIDw/H09MTNzc32rVrx5o1azK0GR4eTr169ejQoQOpqalm9d85xXfVqlU0bdqUyMhImjVrRrVq1ejYsSP79u0zyjdt2pTZs2fj4+ND9erVadasGcuXL8/y2ENDQwkLCyMuLi7T6bA7duygadOmAPTq1ct4nZCQwMiRI/Hw8MDd3Z2ePXvy+++/mx27efNm3nzzTaNfISEhJCcnG/v/+usv3n33XWrWrMmrr77KunXreFhbtmzhzTffxM3Njfr16+Pv78+1a9cyjDM9Dpn9i4uLAyAmJob+/ftTs2ZNGjVqxIcffsjFixcfuk8iIiIi8nTRFVS5r2XLlhEeHk5qaioVKlRg4MCBHD58mFGjRvHiiy+ybt06fH19mTlzJp6enlmqc8qUKURGRjJ27FicnJxYunQpixYtok6dOmblFi5cyNixYylevDhTpkxhyJAhbNu2DQcHB4KDg1m7di2jRo3CycmJXbt2ERgYSEJCgtn02ejoaJYtW0ZiYuIDp/NeuHCBr7/+mqCgIHLnzk1gYCB+fn78+OOP2NjYADBz5kx8fHzw8/Nj69atjBo1CgcHB1q1avXAcXt7e3Pjxg3Wr1/PihUrMkyHrVmzJsuXL6dz586EhoZSu3ZtTCYT/fv3J3fu3MyZM4cCBQrw7bff0q1bN7755huqVKnC1q1bef/99wkICKBhw4b8+eefjBs3jtjYWKZPn86tW7fo168fBQoUYPHixSQnJzNmzJgsvVfprly5gq+vL/7+/rz66qv89ddffPzxx0yePJnx48eblW3VqhUvv/yy8fPly5fp27cvL7/8MqVLl+b8+fN0796d1q1b4+/vT2JiIqGhoXTt2pW1a9eSP3/+h+qbyL9la5v939Wmt/E42npWKKbZQ3G1PMXU8hRTy7OmmCpBlftq164drq6uwO2rXlFRUcyePZsmTZoA4Ovry9GjR5k9e3aWEtTExESWLl1KQEAAzZs3B2DkyJFmVyrTDR8+nHr16gEwaNAgNm7cSExMDBUrVmT+/PlMnjzZ6EfZsmWJi4sjIiLCLEH19vamfPnyWRprSkoKgYGBVK5cGQAfHx8GDRrExYsXKV68OAANGzbE19cXgAoVKnDgwAEWLFiQpQTVwcGB/PnzY2trm+l0WHt7eyNpLVy4MI6Ojmzfvp19+/axfft2Y9/QoUPZu3cvCxcuZOLEicyePZtOnTrRrVs3IxZjxoyhV69enD17ltjYWI4fP86GDRsoW7YsAJ999hnt27fPUlwAzp8/T3JyMv/5z38oXbo0pUuXZvbs2RmuSgPkzZuXvHnzAnDz5k18fX0pV64cY8eOBeCrr76iePHijBo1yjgmJCQEDw8PfvjhB958880s90vEEgoVyvdUtvWsUEyzh+JqeYqp5SmmlmcNMVWCKvdVrlw54/XRo0cBcHd3NytTu3Ztpk6dmqX6YmJiSEpKokaNGmbb3d3dOXLkiNm2F1980XhdqFAhAJKSkjhx4gQ3b97Ez8+PgIAAo8ytW7dITk4mKSnJ2JbV5DSdk5OT8bpgwYLA7cQ1XXrCnK5GjRpmU48t7dChQwAZkv/k5GRu3rwJwOHDhzl48CCRkZHG/vR7XWNiYjhx4gSFCxc2klOAypUrky9f1n8BVa5cmTfeeIMBAwZQqlQpGjRowKuvvmpMQ86MyWTC39+fS5cusXz5cuzt7Y3+xsTEULNmTbPyN2/eJCYmJst9ErGU+PhEUlPTsrUNW9tcFCqU77G09axQTLOH4mp5iqnlKaaWl90xLVQoX5avzipBlftKvxJ2P2lpadjZmZ9KJpPJmBZ769YtY3t6uQctFgSQK1fGk9hkMhnHhoSEUKFChQxl0hMhgDx58jywnXsde2eb6TIb5939vNfYH0VaWhoFChTI9HEw6X1NS0ujX79+dOjQIUOZYsWKceLEiUzjffdYHmTq1KkMGjSIrVu38uuvvzJ06FBq1arFwoULMy0fEhLC1q1bWbZsmdl05rS0NDw8PBg9enSGY9K/FBB5nFJT07h16/H8gfM423pWKKbZQ3G1PMXU8hRTy7OGmOb8JGN5Yjg7OwOwZ88es+27d++mYsWKAOTOnRu4vbBPutOnTxuvy5UrR968edm/f79ZHQcPHsxyPypUqICdnR3nzp2jXLlyxr8tW7YQERGRaWJrKXcvTrR3716qVKkCPHjsgJG4ZpWzszPXr18nOTnZbKzh4eFERUUBUKlSJU6ePGm2//z580yePJl//vmHKlWqEB8fz/Hjx416Y2Njzfr5IPv372fChAlUqFCB3r17M3fuXCZMmMCOHTu4fPlyhvKRkZGEh4czbdo049xIV6lSJWJiYihVqpTR38KFCzNhwgSOHTv2UPERERERkaeLElTJsooVK9K4cWPGjBnD5s2biY2NJSwsjKioKLy9vYHbU15z5cpFSEgIZ86cITo6mnnz5hl15MuXDy8vL2bMmMHGjRuJjY1lypQpGRLW+ylYsCBdu3YlJCSE1atXc+bMGSIjIwkKCuL555+/53EJCQlcuXLlkcfP/2vvzsNrOP//jz8jdhIVYilKbaGV2LeiNKFtkIilrb2aavnYd1JLRVFrE2LXtGJr1RK0tKWxtFU7Re1LhChCkVgS2eb3h2/OzxHkpD2Rg9fjunJdycw9M/e85x3yPnPPPcC6detYvHgxZ8+e5csvv2Tjxo107doVSPvcAXLnzk10dDTh4eEkJCQQHx/PlStXzGbcvV+DBg2oWLEi/fr1Y/v27URERDBx4kRWrlxpGo780UcfsWHDBoKCgggPD2f79u34+fkRExODs7MztWvXpnLlygwZMoQ///yTQ4cOMWzYsHQV8nnz5mXp0qVMnjyZiIgIjh8/zrp16yhVqhT58+c3a7tr1y5GjhzJwIEDcXV15cqVK6avuLg42rdvz82bNxkwYABHjx7l2LFjDBw4kIMHD1KuXLn0XA4RERERecaoQJV0CQgIwMPDgxEjRuDt7c2mTZsICgri7bffBqBEiRKMGTOGrVu34unpyezZs/nkk0/M9tG3b1+8vb0ZMWIEPj4+REZG4uHh8dDhtY/i5+dHly5dmD59Op6ensycOZNevXrRu3fvR24zbtw42rRp8+9O/P/4+PiwYcMGvLy8WLNmDYGBgTRs2BCw7NzffPNNnJ2d8fb25siRI+zfv5/69es/dJIoAHt7e7766ivc3Nzo378/3t7e7Ny5k6CgIOrWrQvA22+/TUBAAGFhYXh5eTFo0CDq1q3LjBkzgHtDpefOnUvp0qXx9fWlW7duNG3aNNUswo9TtmxZgoKC2LFjBz4+PrRv356sWbMyf/78VIXuqlWrSEhIYNKkSdStW5f69eubvtavX0+JEiVYvHgxsbGxtG/fno4dO2JnZ0dISAgFChSwuE8iIiIi8uyxMyx5GFDEijZu3Ej16tXNCiRfX1+KFCnC+PHjM7Fnj+fu7k7Lli0fWwRL5un3xRZOX4jO7G7IU6RMsXwEDmjE9eu3M/x5m6xZs5A/f54ncqznhWKaMRRX61NMrU8xtb6MjqmTUx6LJ0nSHVR54oKDgxk4cCBHjx7l/PnzLFiwgB07duDt7Z3ZXRMRERERkUykWXzliZsyZQoTJkygS5cuxMXFUbZsWaZNm0adOnUyu2vPpe7du7Nz587HtlmxYoXZK3hERERERDKCClR54ooXL256PvJpsmnTpszuQobw9/c3e3fswxQtWvQJ9UZEREREnmcqUEWec4ULF87sLoiIiIiIACpQReQZUbywQ2Z3QZ4yyhkRERHbowJVRJ56hmEwqEP1zO6GPIWSkpJJTtZk9iIiIrZCBaqIPPXs7OyIiYklKUlTzVuDvX0WHB1zPRcxTU42VKCKiIjYEBWoIvJMSEpK1rvQrEwxFRERkSdN70EVERERERERm6ACVURERERERGyCClQRERERERGxCSpQRURERERExCaoQBURERERERGboAJVREREREREbIIKVBEREREREbEJKlBFRERERETEJqhAFREREREREZugAlVERERERERsggpUERERERERsQkqUEVERERERMQmqEAVERERERERm6ACVURERERERGyCClQRERERERGxCSpQRURERERExCaoQBURERERERGboAJVREREREREbIIKVBEREREREbEJWTO7AyIi1mBvr8/brCUlloqp9dhKTJOTDZKTjUztg4iIyOOoQBWRp55hGDg65srsbjxzFFPry+yYJiUlc+PGHRWpIiJis1SgishTz87OjilL9hJ5+WZmd0XEZhUv7MCgDtXJksVOBaqIiNgsFagi8kyIvHyT0xeiM7sbIiIiIvIf6AEjERERERERsQkqUEVERERERMQmqEAVERERERERm6ACVURERERERGzCM12gRkZG4uLiws6dOzO7K4+0c+dOXFxciIyMzOyuWJVhGISGhvLPP/9YvM3169dZvnz5fzru4cOHad68OZUqVaJv374MGzaMTp06/ad9PujkyZNs2bLF9LOLiwurVq2y6jEeFBQUhLu7+yPXx8XFMXXqVNzd3alatSqtWrUiLCwsQ/v0b6xatQoXF5fM7oaIiIiI2KhnukCVzLN7926GDRtGbGysxdtMmjSJtWvX/qfjzpo1Czs7O3744QdGjhz5n/b1KN26dePQoUMZsu9/a+zYsfzwww/4+/uzevVq3nrrLXr16mVzH840bdqU33//PbO7ISIiIiI2SgWqZAjDSP879v7NNg+KiYnhlVdeoVSpUhQsWPA/7+9pEBsby+rVqxkwYAANGjSgZMmSdOvWjdq1a7Ny5crM7p6ZnDlz4uzsnNndEBEREREbla73oG7dupVp06Zx+vRpcufOTcOGDfHz8yNfvnycPn2aCRMmsGfPHvLkyUPt2rUZNmyY2R+jixYtYvHixVy8eJHixYvTrVs3WrRoAcDFixeZOnUq27dv5/bt21SvXp0hQ4aYhgMOGzaMpKQkChYsyOrVq7lz5w716tXD39/fdIwTJ04wduxYDh48SOHChfnoo4/M+h8fH09QUBA//vgjly5dIk+ePNSrV4+RI0eSP39+IiMj8fDwoG/fvixZsoQcOXLg4eHB5s2b+eWXX0z7uXnzJvXq1SMgIAAPD48045aYmMjs2bNNQ17LlClDv379eP3111O1NQyDL7/8km+//ZarV69SqlQpPvzwQ7y9vU1tNm3axLx58zh+/DiJiYm4uLgwYMAAXnvtNQA6depEpUqViI6OZsOGDSQnJ9OkSRNGjRpFnjx5ANK8Xp06daJEiRKcPHmS8PBwRowYgY+Pj1lfg4OD+eabb7h06RKFChWidevW9OjRg127dtG5c2cAPDw8+Pzzz2nVqhUrV65k0aJFnDlzhixZsuDq6sqwYcN49dVXGTZsGKGhocC9IbPHjx+3KBb3c3d358KFCwCsXr2ahQsXpmqTVp6llSMpx5gxYwa7du1i0aJFAJw5c4Z27drx119/UbJkSfz8/KhXr57pmgYHB7Ny5UrOnz9Pjhw5qFGjBiNGjKBEiRKmc/7ss8/48ccf2bt3L/ny5aNjx45069btoee6cOFCJk2aRGBgIPXr12fOnDlUqlQpVbvoaMvfC3rp0iWmTJnCH3/8QXR0NAULFsTHx4e+ffuSJYtln2WltY9Vq1bh5+fH8ePHTefdvXt31q5dS3x8PIsWLaJ06dIW91lE0s/e/tn4bDrlPJ6V87EViqv1KabWp5hany3F1OIC9dq1a/Tq1Ythw4bRqFEjLl26xJAhQ5g0aRJ9+vShffv2NGvWzDSsMygoiLZt2/L999+TO3dugoODmT59OsOHD6dOnTr89ttv+Pn5UbBgQSpXrky7du0oUaIEs2fPJnv27MycOZOOHTuyZs0aXnzxRQB+/PFHvLy8WLx4MX///TeDBg0iICCA8ePHc/PmTbp06UKVKlVYvnw5UVFRqYZ4Tpo0ibCwMCZMmEDx4sU5efIkQ4cOZfbs2XzyySemdmvXriUkJITY2FiyZ8/OwoUL2bNnDzVq1ABg/fr15M2bl4YNG1oUu/Hjx7N+/XpGjRpFpUqVCA0NpUePHqxevTpV24CAAL7//ntGjRpFmTJl2L17N6NHj+bmzZt06NCBv/76i549ezJ48GAmT57M7du3CQgIYNCgQWzZsoXs2bMD9z4M8PX1Zfny5Rw9epShQ4fy0ksv0bNnTy5fvpzm9YJ7zwtOnjyZChUqpLobuWnTJubMmUNgYCAvv/wyf/75J0OGDKF48eJ4enoSFBRE7969Wb58OeXLl2fjxo18+umnjB07lpo1a3L16lXGjh3L8OHDWb16NcOHDycuLo5Lly4RFBRkUSwetGLFCnr06EGRIkUYPnw4+fLlMxW9ALdu3Uozz9LKkRUrVtCyZUuaNm1qVjyGhIQwfPhwxo8fz5o1a/jwww9ZsWIFlSpVIiQkhLlz5zJx4kTT88YjR45kwoQJzJw50yw/R44cyahRo1izZg1ffPEF1atXN+VdiqVLlzJlyhRmzJhBo0aNAKhfv75ZmwMHDrBjxw6GDx+eVnqadOvWjQIFChAcHEzevHnZsmULY8eOxdXVlcaNG2fYPpYtW8b8+fNJSkpScSryBDg65srsLljVs3Y+tkJxtT7F1PoUU+uzhZhaXKBevnyZ+Ph4XnzxRYoVK0axYsWYM2cOSUlJfPPNNxQqVIhRo0aZ2gcGBlKnTh1++uknWrVqxYIFC+jcuTPvvvsuAB06dCAuLo6kpCTWrl3L9evXWbVqFU5OTgBMmTKFxo0bs2TJEgYPHgxA3rx5GTNmDNmyZaNMmTK0aNGCrVu3ArBu3TpiY2OZOHEiDg4OlCtXjk8++YSePXua+uTq6sqbb75JrVq1AChWrBj169c33c1J0b59e8qWLWv6+dVXX2Xt2rWmQiE0NJQWLVqQNWva4bt16xbfffcdI0aMoGnTpgD07duX5ORkbt++bdb2zp07LFiwgEmTJvHGG28A8NJLL3HhwgWCg4Pp0KED9vb2jBgxwqxA69y5M76+vvzzzz8ULVoUgDJlyjBgwAAAXn75ZdatW8e+ffsALLpeABUrVsTLy+uh53Xu3Dly5MhB8eLFefHFF3nxxRcpVKgQL774ItmzZydfvnwAODk5kTNnTl544QXGjh1rugtbrFgx3nnnHT799FMAHBwcyJkzJ9myZcPZ2dmiWDzIycmJbNmyPXIYqSV5llaOODk5YW9vT+7cuXnhhRdM+27Xrh1t27YFoF+/fuzYsYMFCxYwZcoUXnrpJSZMmGCa5KhYsWJ4enqybt06s/61bNnSNKKgX79+LF26lL1795oVqN999x0TJ05k5syZNGjQ4KHX5syZM/Ts2ZNKlSrx3nvvPbTNg+Li4mjRogVvvfUWxYoVA+7dRU+5U29Jgfpv99GiRQtcXV0t6qeI/HcxMbEkJSVndjf+M3v7LDg65npmzsdWKK7Wp5han2JqfRkdU0fHXBbfnbW4QK1YsSLNmzene/fuFC1alNdee41GjRrh7u7OkSNHOH36NFWrVjXb5u7du5w+fZpr164RFRVF5cqVzdZ/+OGHAIwePZpSpUqZigaAHDly4ObmZlY8lixZkmzZspl+dnBwICEhAbg3vLdUqVI4ODiY1j/YnxYtWrB9+3a++OILzp49y+nTpzlz5kyqO1QlS5Y0+7l169YEBgYyYsQILl68yP79+xkzZoxFcQsPDychIYEqVaqYLe/fvz+A2SQ2p06d4u7duwwdOhQ/Pz/T8sTEROLj44mLi6NixYrky5eP+fPnEx4eztmzZzl69CgASUlJpm3KlCljdjwHBwdiYmIA0rxej4rD/by9vVm5ciVvvvkmLi4u1KtXjyZNmpjudj+oZs2aODk5MWvWLCIiIggPD+fo0aMkJz/8F8CSWOTMmfOR/XuYlBx5XJ5ZmiMPenB95cqV2bFjB3Bv6PGBAweYPn06ERERnD59mpMnT1K4cGGzbR68Znnz5jXlN0BUVBSjR48ma9asFC9e/KH92LdvHz169MDZ2Zl58+aZ7qinJWfOnHTs2JGffvqJkJAQIiIiOHbsGFFRUY+8Rtbax+PyTESsLykpmcTEZ+cPumftfGyF4mp9iqn1KabWZwsxTdczqFOnTqVnz578+uuv/PHHHwwYMIBq1aqRPXt26tSpY7obdj8HBwfTH8l2dnYP3a9hGA9dl5SUZHaXMq0/th+cZOfBO5yjR49m/fr1+Pj40KhRI/73v/8RHBzM5cuXzdo9WPh4eXkxceJENm/ezIkTJ3B1daV8+fKP7UuK+wvqtKT0PzAw8KFDHbNnz87u3bvx9fWlYcOG1KhRg2bNmhEbG2t2pzil7aMkJyc/9nqleFwB6OTkxJo1a9i/fz/btm3j999/56uvvqJ379706tUrVft169YxZMgQmjdvjpubG23atOHEiROPLPQtiUV6WZJnlubIgx58RjMpKcnUx/nz5xMUFESrVq2oVasWnTp1IiwsLNUd1Ied0/05bWdnx/z585k2bRp+fn4sXbrU7LgbN25k4MCBuLq6Mnv2bBwdHdOIyP8XGxtLhw4diI2NxdPTkxYtWjBy5MiH3qm29j7S+0GDiIiIiDy7LC5Q//zzT9avX88nn3xC6dKl6dKlC2vXrmXw4MG0atWKP/74g6JFi5r+yL5x4wZDhw7lgw8+oE6dOhQqVIhDhw6ZTSrUp08fChUqRPny5Vm9ejX//PMPBQoUAO7dzfvrr79STczzKBUrVmTlypVcu3bNdIfs/leBXL9+nW+++YaAgADTUFu4Nxwy5ZnLR3F0dKRJkyZs2LCBEydO0K5dO4v6BP//ru+hQ4eoUKGCaXmbNm14++23zYY2li5dmqxZs/L333+bhrXCvQlxTp06xZgxYwgODqZ27drMmDHDtD5loh5LZ8EtV64c69evf+z1SsuaNWu4desWHTp0oHr16vTp04cRI0awfv16evXqlaoQnDNnDm3atMHf39+0LOU9nSmF4/3bWBKL9Eorz/5Ljhw+fNhsCOu+fftM13v27Nn06tWLjz/+2LQ+ODg43bMWOzs7U69ePZydnWnVqhUhISF88MEHwL1ngvv164eHhwdTpkxJdwH/22+/cfjwYbZt22Z63vjGjRv8888/FvfTGvsQERERkeebxdM05c2bl6VLlzJ58mQiIiI4fvw469ato1SpUvzvf//j5s2bDBgwgKNHj3Ls2DEGDhzIwYMHKVeuHAAff/wxISEhrF69mnPnzrFkyRLCwsJo3LgxXl5eODo60q9fPw4ePMixY8cYPHgwd+7csfgZumbNmlGgQAEGDhzIsWPH2LVrF+PHjzetd3BwwMHBgbCwMFP/R44cyeHDh4mPj09z/61bt+aXX34hIiKC5s2bWxo2cuXKRceOHZk2bRphYWGcO3eOgIAATp06ZVZ4pfSxbdu2BAYGsnr1as6fP09oaCiTJ082/cFftGhRjh8/zp49e4iMjGTlypVMmzYNwKLzgHvP2KZ1vR6UlJTElStXiIuLA+4VdhMnTmT16tVERkayZ88edu3aZRo2nFLQHTt2jNu3b1O0aFH27dvH4cOHOXfuHAsWLGDx4sVm/c6dOzdRUVGcP3/eoljAvcm7bt68adF5p5VnluZInjx5OHv2LFevXjUtW7BgAaGhoZw5c4bx48dz4sQJ0yzSRYsWZdu2bZw6dYozZ84QEBDAhg0bLL5eDypfvjxdu3YlMDCQs2fPEh0dzdChQ3n11VcZPnw40dHRXLlyhStXrnDjxg2L9lmkSBHg3nO6Fy5cYM+ePfTo0YOEhASL+2mNfYiIiIjI883iArVs2bIEBQWxY8cOfHx8aN++PVmzZmX+/Pm89NJLLF68mNjYWNq3b0/Hjh2xs7MjJCTEdKeqY8eO9OzZk+nTp9OsWTO+/fZbAgICqFOnDo6OjixevBgHBwe6dOlC+/btiY2N5ZtvvjG9hiMtuXPnZuHChWTLlo127doxZMgQs9fMZM2alWnTpnHixAm8vLzo2rUrsbGxDBgwgJMnT3Lnzp3H7r9u3brkz5+fxo0bp2voJMCAAQPw8fFh9OjReHl5sX37dubNm5fqmUMAPz8/unTpwvTp0/H09GTmzJn06tWL3r17A/fuOlepUoXu3bvj4+PD8uXLGT9+PDlz5uTgwYMW9adEiRJpXq8HXbx4kfr167N+/XoA3n33XXr37s2sWbPw9PSkX79+NGjQgBEjRgD3iqiGDRvSr18/li1bxsiRIylYsCAdO3bknXfeYfPmzUyaNAm4N+MsgI+PD7GxsTRv3pyoqKg0YwH37kSPGzfOovNOK88szZFOnTqxZcsWfH19Tfvu0aMHixYtwtvbm127djFv3jxefvll4N7svHFxcbRu3ZqOHTty4sQJ/P39+eeff4iMjLSo7w/q0aMHL774In5+fvz666/ExMRw4MABXn/9derXr2/6uj9Wj+Pm5oafnx8LFy7E09MTPz8/atasSfPmzU3X50nsQ0RERESeb3aGxt5Z5M6dO9SvX58ZM2aY3jcqIraj3xdbOH3B8ve+ijxvyhTLR+CARly/fjvTJ8CwhqxZs5A/f55n5nxsheJqfYqp9Smm1pfRMXVyymP9WXyfV9HR0ezYsYMff/yRF198kbp162Z2l0RERERERJ5JKlDTkJiYyPDhw3FyciIwMNBsIp8xY8YQGhr62O2nTZvG66+/ntHdFHkka+Spcl1EREREngQN8f0PLJmgp1ChQuTKlesJ9UgkNWvk6dOQ6xriK/J4GuIrllBcrU8xtT7F1Po0xPcZ4eTkZHqljYitskaePg25XrywQ9qNRJ5j+h0REZGngQpUEXnqGYbBoA7VM7sbIjYvKSmZ5GQNnBIREdulAlVEnnp2dnbExMSSlKRhPtZgb58FR8dciqkV2UpMk5MNFagiImLTVKCKyDMhKSlZz6FYmWJqfYqpiIjI41n2pKqIiIiIiIhIBlOBKiIiIiIiIjZBBaqIiIiIiIjYBBWoIiIiIiIiYhNUoIqIiIiIiIhNUIEqIiIiIiIiNkEFqoiIiIiIiNgEFagiIiIiIiJiE1SgioiIiIiIiE1QgSoiIiIiIiI2QQWqiIiIiIiI2AQVqCIiIiIiImITVKCKiIiIiIiITVCBKiIiIiIiIjZBBaqIiIiIiIjYBBWoIiIiIiIiYhNUoIqIiIiIiIhNUIEqIiIiIiIiNkEFqoiIiIiIiNiErJndARERa7C31+dt1pISS8XUehRT68uomCYnGyQnG1bdp4iIWE4Fqog89QzDwNExV2Z345mjmFqfYmp91o5pUlIyN27cUZEqIpJJVKCKyFPPzs6OKUv2Enn5ZmZ3RUSeYsULOzCoQ3WyZLFTgSoikklUoIrIMyHy8k1OX4jO7G6IiIiIyH+gh2FERERERETEJqhAFREREREREZugAlVERERERERsggpUERERERERsQkqUJ9iq1atwsXFJbO7YXXu7u4EBQVldjfSLTP6ff36dZYvX55h+4+MjMTFxYWdO3cCEB8fz8CBA6lSpQo1atTg6tWrae7jzp07LFmyJMP6KCIiIiLPDs3i+xRr2rQpDRo0yOxuyP9ZsWIFOXLkeKLHnDRpEpGRkbzzzjtP5Hi//vorP/zwA7NmzcLFxYWCBQumuc1XX33FqlWr6NChwxPooYiIiIg8zVSgPsVy5sxJzpw5M7sb8n+cnJye+DEN48m+p+/mzXvvGXV3d8fOzs6ibZ50H0VERETk6aUhvjbCxcWFb775hnbt2uHm5oaXlxdhYWGm9UFBQbRt25YBAwZQrVo1/P39Uw3xdXFx4YcffqBz5864ubnRpEkTNm3axKZNm3jrrbeoUqUKXbt25dq1a6ZtNm3aRNu2balatSqurq60adOGP/74w7S+U6dOfPLJJ7zzzjvUqFGDGTNm4OLiwu7du836379/f3r16mXx+W7bto22bdtSuXJlXn/9daZOnUpSUtJD2+7bt48OHTrg5uZGo0aN8Pf359atW6b1ly5dYtCgQbz22mu8+uqrNGzYkICAAJKTk4F7Q6Hd3d0JDQ2lSZMmVKpUidatW7N//37TPuLj45k8eTINGjSgatWqvPvuu/z++++m9Sn7GDduHDVq1KB79+6p+nn/EN+goCA6derE/Pnzef3113F1daVz586cOXPG1N6Sa+7u7m52jPuv+bBhwwgNDWXXrl2PHOodGxvL8OHDqVevHq6urvj4+LBhwwbT+k6dOjFs2DCzbYYNG0anTp1S7SsoKMjUtkKFCqbvH5dDQUFBzJgxgwsXLuDi4kJkZCQAK1euxNPTEzc3Nzw9PQkJCTFdLxERERF5fukOqg2ZNGkSgwYNYty4caxatYpevXqxZMkSqlWrBsD+/ftxdXVlzZo1JCUlsW/fvlT7GDt2LP7+/owdO5bPP/+cgQMHUrZsWSZPnsydO3fo06cP8+fPZ+jQofz111/07NmTwYMHM3nyZG7fvk1AQACDBg1iy5YtZM+eHbhXFE2ePJkKFSpQsGBBwsLCWL16NTVr1gTu3VULCwsjMDDQovM8cOAAXbt25f3332fcuHFcvHiRQYMGkSVLFvr372/W9tixY3Tp0oXu3bszbtw4rl69yqRJk/D19WXZsmXY2dnRrVs3ChQoQHBwMHnz5mXLli2MHTsWV1dXGjduDEBUVBTffvstkydPJlu2bIwePZqhQ4fy888/Y2dnh5+fHydPnmTy5MkUKVKEzZs30717d2bMmEGjRo0AuHDhApcvXyY0NJS4uLg0z3P//v3kypWLefPmcfv2bYYOHYq/vz8hISEWX/PHGT58OHFxcVy6dOmRz75OmzaN48ePM2/ePBwdHVm+fDn9+/fn559/pnjx4mke436+vr44Ojoyfvx4fv/9d3LmzJlmDvn6+nLnzh3Wr1/PihUrcHJyYtmyZUydOpVRo0ZRuXJljhw5wmeffcbly5cZMmRIuvokIpIR7O2f38/vU879eY6BtSmm1qeYWp8txVQFqg1p3bq16Tm9QYMGsXv3bhYvXmxWrPTp0wcHBweAhxaoLVu25K233gKgbdu2bNq0if79++Pm5gZAvXr1OHHiBAD29vaMGDHC7NnAzp074+vryz///EPRokUBqFixIl5eXmb9DAwMZNSoUeTIkYMff/wRBwcHXn/9dYvOc+HChbi5uZnuwJUpU4bPPvuMqKioVG2Dg4OpW7cuPXr0AKBUqVJMnTqVxo0bs2vXLipXrkyLFi146623KFasGHDvruC8efM4fvy4qUBNSEhg9OjRVKxYEYBu3brRs2dPrly5QmxsLD/88AMrVqzA1dUVgA8++IBjx44RHBxsKlABevToQYkSJSw6z8TERCZNmsQLL7xg6tfkyZPN2lhyzR/FwcGBnDlzki1bNpydnR/a5ty5c+TNm5eXXnoJBwcH+vbtS40aNciXL59F53C/PHnymHIv5XiW5FDu3Lmxt7c3bTNr1iy6detG8+bNAShRogS3bt3C39+fvn37PvHneEVEHuTomCuzu5DpFAPrU0ytTzG1PluIqQpUG1KrVi2znytXrmw23LZAgQKmAuFRXn75ZdP3Kc+n3l9Q5ciRg/j4eOBe4ZkvXz7mz59PeHg4Z8+e5ejRowBmw21LlixpdgwvLy8mTpxIWFgYTZs2JTQ0FG9vb7JmtSydjh8/zmuvvWa2rEmTJg9te+TIESIiIqhatWqqdadPn6Z27dp07NiRn376iZCQECIiIjh27BhRUVGphoyWKVPG9H1KHBMSEjhy5Ahwr7C6X0JCAo6OjmbLSpUqZdE5AhQsWNBUnKYcMyEhwaxNWtf8v/roo4/o3r07devWpWrVqtSrV49mzZqlmUeWsjSHUly7do1Lly4xbdo0ZsyYYVqenJzM3bt3iYyMNLtOIiKZISYmlqSk5/OxA3v7LDg65nquY2Btiqn1KabWl9ExdXTMZfHdWRWoNuTBAi85OZksWf7/hbRkQqSHFYmPmsxm9+7d+Pr60rBhQ2rUqEGzZs2IjY2lZ8+eZu0ePG6+fPlo3Lgxa9euxdXVlf379zNmzJg0+3Z/Hy2dYCc5ORkvL6+HPvPp5OREbGwsHTp0IDY2Fk9PT1q0aMHIkSMfOmNsypDl+xmGYZrEZ8mSJeTJk8ds/f3xB8uuweOO96C0rvmDEwwlJiZafHyAqlWrsnXrVrZt28b27dtZsWIFQUFBfPnll9StW/ehx3iwiH4cS3MoRcqHBn5+fqk+pABMd+1FRDJTUlIyiYnP9x+9ioH1KabWp5hany3ENPMHGYvJoUOHzH7+888/efXVVzPseMHBwdSuXZsZM2bQpUsX6tWrx8WLF4G0Z15t3bo127ZtY82aNbi6ulKuXDmLj1umTJlU57pgwQJatmyZqm25cuU4efIkJUuWNH0lJSXx+eefc/HiRX777TcOHz7MokWL6NOnD02bNiVv3rz8888/Fs8em9L3qKgos+OsWrWKlStXWnxe/8bjrnm2bNm4deuW2XlERESYtU+r0J8+fTp79+7Fw8ODESNG8PPPP1OiRAl+/vln0zFSZuZNce7cOYv7b0kO3d/HAgUKUKBAAc6dO2cW68OHD1v8DLOIiIiIPLtUoNqQkJAQvv/+e8LDw5k4cSLHjh3j/fffz7DjFS1alOPHj7Nnzx4iIyNZuXIl06ZNAzANA36U1157jYIFCzJ//nxatWqVruN27dqVP//8k8DAQMLDw9m6dStz587Fw8MjVVtfX1+OHj3KqFGjOHXqFAcOHGDQoEGEh4dTqlQpihQpAsDatWu5cOECe/bsoUePHiQkJKR5DinKlSvHG2+8waeffkpYWBjnz58nODiYuXPnPvZ50xs3bnDjxo10nfuDHnfNq1WrRkxMDPPmzSMyMpLvv/+eVatWmW2fO3duoqKiOH/+PABxcXFcuXLFNLw2IiKCTz/9lO3bt3PhwgV++ukn/v77b9OQ6WrVqvHHH3+wadMmzp8/z/Tp003PKFvCkhzKnTs30dHRhIeHk5iYSNeuXVm0aBGLFi3i3Llz/PLLL/j7+5M9e3aL7jqLiIiIyLNLQ3xtyHvvvcfXX3/NyZMnqVChAsHBwVSoUCHDjtenTx+uXr1qGj5btmxZxo8fz+DBgzl48OBjnwXMkiUL3t7efP311zRr1ixdx61YsSKzZs1i+vTpfPnllzg7O9OpU6eHDuOtUqUKX375JdOmTaNVq1bkypWLOnXqMHToULJnz46bmxt+fn4sWLCAwMBAChcuTNOmTSlatCgHDhywuE8BAQEEBATw6aefEh0dTYkSJfjss89o3br1I7fp3bs3AIsWLUrX+d/vcde8Vq1a9O/fn8WLFzNz5kxq1qzJ0KFDGTp0qGl7Hx8fNm7cSPPmzdm4cSO///47fn5+hIWFUbx4cfz9/Zk4cSKDBw/mxo0bFCtWjEGDBtGiRQsAunTpwvnz5xk8eDB2dnY0bdqULl26PHQCroexJIfefPNNvvvuO7y9vVm8eDG+vr7kyJGDRYsWMXHiRAoUKECrVq1SzeAsIiIiIs8fO8PScZCSoVxcXPj888/TfTcyM/n5+ZGQkMCUKVMyuytPpafxmtuyfl9s4fSF6Mzuhog8xcoUy0fggEZcv34705/ByixZs2Yhf/48z3UMrE0xtT7F1PoyOqZOTnk0SZJknG3btnHq1Cl++OEHlixZktndERERERGRZ4QKVEm3lStXsmXLFnr37m16vyrA/v378fX1fey2jRs3TvUuUBEREREREVCBajOOHz+e2V2w2BdffPHQ5a+88gqrV69+7La5c+fOgB49nZ6may4iIiIi8iSoQBWryZEjByVLlszsbshzqnhhh8zugog85fTviIhI5lOBKiJPPcMwGNShemZ3Q0SeAUlJySQna/5IEZHMogJVRJ56dnZ2xMTEkpSkmfyswd4+C46OuRRTK1JMrS+jYpqcbKhAFRHJRCpQReSZkJSUrKnmrUwxtT7F1PoUUxGRZ4tlL6MRERERERERyWAqUEVERERERMQmqEAVERERERERm6ACVURERERERGyCClQRERERERGxCSpQRURERERExCaoQBURERERERGboAJVREREREREbIIKVBEREREREbEJKlBFRERERETEJqhAFREREREREZugAlVERERERERsggpUERERERERsQkqUEVERERERMQmqEAVERERERERm6ACVURERERERGyCClQRERERERGxCSpQRURERERExCaoQBURERERERGbkDWzOyAiYg329vq8zVpSYqmYWo9ian2KacZQXK1PMbU+xfS/S042SE42MrsbD2VnGIZt9kxExEKGYWBnZ5fZ3RARERF5KiQlJXPjxh1TkZo1axby58/D9eu3SUxMtvrxnJzyWPyBgu6gishTz87OjilL9hJ5+WZmd0VERETEphUv7MCgDtXJksXOJu+iqkAVkWdC5OWbnL4QndndEBEREZH/QAO3RURERERExCaoQBURERERERGboAJVREREREREbIIKVBEREREREbEJKlDlqbVz505cXFyIjIzM0ON06tSJYcOGZegxHnTnzh2WLFny2Dbz5s2jTp06VK5cmV9++SXD+uLi4sKqVasybP8iIiIiIik0i69IGoKCgrC3t3+ix/zqq69YtWoVHTp0eOj6mJgYvvjiCz7++GPee+89nJycnmj/REREREQyggpUkTS88MILT/yYhvH4d1LdvHkTwzB47bXXKFas2BPqlYiIiIhIxtIQX/nPXFxcCAgI4I033qBevXqcOXOGuLg4AgMD8fDwwNXVFR8fH7NhqKtWrcLFxcVsPw8O2U1KSiIgIID69etTuXJlevfuzbhx4+jUqZPZdlu3bsXLy4tKlSrRrFkztmzZYlpnGAbz58/Hw8ODypUr06JFC9auXZvqmPPnz6d27dq0bNmSpKQks/3fP8R31apVuLu7ExoaSpMmTahUqRKtW7dm//79pvbu7u7MmTOHbt264ebmRpMmTVi+fLnF5x4UFMSMGTO4cOHCQ4cw79y5E3d3dwDef/990/c3b95k5MiR1KlTh+rVq9O5c2cOHTpktu3mzZtp1aqVqV+BgYHEx8eb1l+6dIn//e9/VK1alUaNGrFu3TrS6+TJk/To0YPatWtTqVIlmjRpQkhIiFmb77//Hk9PT1xdXWnTpg0hISGpYiIiIiIizx/dQRWrWLZsGfPnzycpKYnSpUvTo0cPjhw5wqhRo3j55ZdZt24dvXr1YubMmXh4eFi0zylTphAaGsqYMWMoU6YMS5cuZdGiRdSsWdOs3cKFCxkzZgyFChViypQp9OvXj23btpEnTx4CAgL4/vvvGTVqFGXKlGH37t2MHj2amzdvmg2f3bJlC8uWLSM2NjbN4bxRUVF8++23TJ48mWzZsjF69GiGDh3Kzz//jJ2dHQAzZ86kW7duDB06lF9//ZVRo0aRJ08emjZtmuZ5+/r6cufOHdavX8+KFStSDd+tWrUqy5cv55133iEoKIgaNWpgGAYfffQR2bJlY+7cueTNm5c1a9bQrl07vvvuO1555RV+/fVX+vbti5+fH/Xq1ePcuXN89tlnhIeHM23aNBITE+natSt58+Zl8eLFxMfH4+/vb9G1ShEbG8sHH3xAnTp1WLp0KVmzZmXlypWMHz+eWrVqUbFiRTZv3szQoUMZOHAg7u7u7Nixg88//zxdxxERERGR/8bePkuq7+9flllUoIpVtGjRAldXVwBOnz5NWFgYc+bM4Y033gCgV69eHD9+nDlz5lhUoMbGxrJ06VL8/Px48803ARg5cqTZncoUn3zyCbVr1wagZ8+e/PLLL5w+fZqyZcuyYMECJk2aZOrHSy+9xIULFwgODjYrUH19fSlVqpRF55qQkMDo0aOpWLEiAN26daNnz55cuXKFQoUKAVCvXj169eoFQOnSpTlw4AAhISEWFah58uQhd+7c2Nvb4+zsnGp99uzZTUVrvnz5cHJyYvv27ezfv5/t27eb1g0YMIB9+/axcOFCJkyYwJw5c2jTpg3t2rUzxcLf35/333+fyMhIwsPDOXnyJBs3buSll14C4PPPP8fHx8eiuMC969a5c2fat29P3rx5gXvXfu7cuRw/fpyKFSsSHBzM22+/zYcffgjAyy+/TEREBF9//bXFxxERERGR/8bRMZdFy540FahiFSVLljR9f/z4cQCqV69u1qZGjRpMnTrVov2dPn2auLg4qlSpYra8evXqHDt2zGzZyy+/bPre0dERgLi4OE6dOsXdu3cZOnQofn5+pjaJiYnEx8cTFxdnWmZpcZqiTJkypu8dHByAe4VripSCOUWVKlXMhh5b2+HDhwFSFf/x8fHcvXsXgCNHjnDw4EFCQ0NN61OedT19+jSnTp0iX758puIUoGLFiuTKZfk/VE5OTrRv357169dz7NgxIiIiOHr0KADJycmmvqZ86JCiRo0aKlBFREREnqCYmFiSku79fWZvnwVHx1xmy6zJ0TGXxXdnVaCKVeTMmTPNNsnJyWTNap5yhmGYhsUmJiaalqe0S2uyIIAsWVInu2EYpm0DAwMpXbp0qjbZs2c3fZ8jR440j/Oobe8/ZoqHneeD/XzUuf8bycnJ5M2b96Gvg0npa3JyMl27dqVly5ap2jg7O3Pq1KmHxvvBc3mcq1ev8u6775I/f348PDyoW7curq6uNGzY0Gx/KcWqiIiIiGSOpKRkEhOT01z2pGX+IGN55pQvXx6AvXv3mi3fs2cPZcuWBSBbtmzAvYl9UkRERJi+L1myJDlz5uTPP/8028fBgwct7kfp0qXJmjUrf//9NyVLljR9bd26leDg4IcWttby4ORE+/bt45VXXgHSPnfAVLhaqnz58ty6dYv4+Hizc50/fz5hYWEAlCtXjjNnzpitv3z5MpMmTeL27du88sorxMTEcPLkSdN+w8PDzfqZlu+//54bN27w7bff0qNHD5o0aUJ0dDTw/wv4ChUqcODAAbPtHvxZRERERJ5PKlDF6sqWLUvDhg3x9/dn8+bNhIeHM2PGDMLCwvD19QXuDXnNkiULgYGBnD9/ni1btvDVV1+Z9pErVy46derE9OnT+eWXXwgPD2fKlCmpCtbHcXBwoG3btgQGBrJ69WrOnz9PaGgokydPpmDBgo/c7ubNm1y7du1fnz/AunXrWLx4MWfPnuXLL79k48aNdO3aFUj73AFy585NdHQ04eHhJCQkEB8fz5UrV8xm3L1fgwYNqFixIv369WP79u1EREQwceJEVq5caRqO/NFHH7FhwwaCgoIIDw9n+/bt+Pn5ERMTg7OzM7Vr16Zy5coMGTKEP//8k0OHDjFs2LB0FfJFihQhNjaWH3/8kb///pvff/+dAQMGAJj6/tFHH/Hzzz/z9ddfExERQWhoKIsWLUp3jEVERETk2aMCVTJEQEAAHh4ejBgxAm9vbzZt2kRQUBBvv/02ACVKlGDMmDFs3boVT09PZs+ezSeffGK2j759++Lt7c2IESPw8fEhMjISDw+Phw6vfRQ/Pz+6dOnC9OnT8fT0ZObMmfTq1YvevXs/cptx48bRpk2bf3fi/8fHx4cNGzbg5eXFmjVrCAwMNA1zteTc33zzTZydnfH29ubIkSPs37+f+vXrP3SSKAB7e3u++uor3Nzc6N+/P97e3uzcuZOgoCDq1q0LwNtvv01AQABhYWF4eXkxaNAg6taty4wZM4B7Q6Xnzp1L6dKl8fX1pVu3bjRt2jTVLMKPkzL50cSJE/H09GT8+PG0adOGmjVrmu5+v/766/j7+7NkyRKaNWvGsmXLaNu2renOsoiIiIg8v+wMSx7yE8kEGzdupHr16mYFkq+vL0WKFGH8+PGZ2LPHc3d3p2XLlo8tgp9nu3btomDBgmbPBc+ZM4cVK1aYvSs3vfp9sYXTF6Kt0UURERGRZ1aZYvkIHNCI69dvm543zZo1C/nz5zFbZk1OTnksniRJd1DFZgUHBzNw4ECOHj3K+fPnWbBgATt27MDb2zuzuyb/wbZt2/jwww/ZsWMHf//9N2FhYYSEhNCiRYvM7pqIiIiIZDLN4is2a8qUKUyYMIEuXboQFxdH2bJlmTZtGnXq1Mnsrj2Xunfvzs6dOx/bZsWKFWav4HmYnj17cvv2bYYMGcK1a9coWrQoXbp0MT2jKyIiIiLPLw3xFRGLXL582ezdsQ9TtGjRdD0jbE0a4isiIiKSNlsf4qs7qCJikcKFC2d2Fx6reGGHzO6CiIiIiM2z9b+ZdAdVRJ56hmGk+92xIiIiIs+rpKRkbty4Q3LyvVJQd1BFRKzIzs6OmJhYkpKs/w/q88jePguOjrkUUytSTK1PMc0Yiqv1KabWp5j+d8nJhqk4tTUqUEXkmZCUlJwhn/g9zxRT61NMrU8xzRiKq/UpptanmD6b9JoZERERERERsQkqUEVERERERMQmqEAVERERERERm6ACVURERERERGyCClQRERERERGxCSpQRURERERExCaoQBURERERERGboAJVREREREREbIKdYRhGZndCROS/SkrSi7qtyd4+i2JqZYqp9SmmGUNxtT7F1PoUU+vLyJhmyWKHnZ2dRW1VoIqIiIiIiIhN0BBfERERERERsQkqUEVERERERMQmqEAVERERERERm6ACVURERERERGyCClQRERERERGxCSpQRURERERExCaoQBURERERERGboAJVREREREREbIIKVBEREREREbEJKlBFRERERETEJqhAFREREREREZugAlVERERERERsggpUERERERERsQkqUEXEpiQnJzN9+nQaNGhA5cqV8fX1JSIi4pHtr1+/zsCBA6lZsyY1a9Zk5MiR3Llzx6zNjz/+SNOmTXF1dcXLy4tff/01o0/DpmRETN3d3XFxcTH7GjRoUEafis1Ib0zv3+7DDz8kKCgo1TrlqfVjqjxNX0xPnjzJxx9/TO3atalbty59+vTh77//NmujPLV+TJWn6YvpX3/9xfvvv0/VqlWpU6cOo0aNIiYmxqyN8tT6MX2ieWqIiNiQoKAgo27dusaWLVuMo0ePGr6+vkaTJk2Mu3fvPrR9x44djXfeecf466+/jD/++MN44403jCFDhpjWb9++3Xj11VeNRYsWGadOnTImTJhgVKpUyTh16tSTOqVMZ+2Y3rx503BxcTE2b95sREVFmb5iYmKe1ClluvTG1DAMIzY21hgwYIBRvnx5Y/r06WbrlKfWj6nyNH0xvXbtmlGvXj2jX79+xokTJ4xDhw4ZHTt2NDw9PY24uDjDMJSnhmH9mCpP0xfTy5cvGzVq1DBGjBhhhIeHG3v37jWaNWtmdO/e3dRGeWr9mD7pPFWBKiI24+7du0bVqlWNpUuXmpZFR0cbbm5uxg8//JCq/b59+4zy5cub/afz22+/GS4uLsalS5cMwzAMX19fo1+/fmbbvffee8bIkSMz6CxsS0bEdO/evUb58uWN6OjojD8BG5TemBrGvZi9/fbbhoeHh1GjRo1UxZTy1PoxVZ6mL6bfffedUa1aNVPhZBiGcfHiRaN8+fLGH3/8YRiG8jQjYqo8Tf//Uf379zcSEhJMyxYsWGBUrlzZ9LPy1PoxfdJ5qiG+ImIzjh07xu3bt6lTp45pmaOjI6+88gq7d+9O1X7Pnj04OztTpkwZ07JatWphZ2fH3r17SU5OZt++fWb7A6hduzZ79uzJuBOxIdaOKcDx48dxdnbG0dEx40/ABqU3pgC//fYbTZo0YfXq1Tg4OJitU55aP6agPE1vTOvWrcvMmTPJkSNHqnXR0dHKU6wfU1CepjemVatW5YsvviBr1qwAnDp1itDQUOrVqwfo31Owfkzhyedp1idyFBERC1y6dAmAokWLmi0vVKgQFy9eTNX+8uXLqdpmz56dF154gYsXLxITE8OdO3coUqSIRft7Flk7pgAnTpwgd+7c9O7dm/379+Pk5ESrVq3o3LkzWbI8+597pjemAH379n3k/pSn1o8pKE/TG9PixYtTvHhxs2Vz584lR44c1KxZU3mK9WMKytN/87uf4q233uLs2bMUK1aMWbNmAfr3FKwfU3jyefrsZ76IPDViY2OBewXR/XLkyMHdu3cf2v7Btve3j4uLS9f+nkXWjincm/Tj5s2bNG3alODgYN577z2mTZv20ElqnkXpjWlalKfWjykoT/9rTBcuXMjSpUsZMGAABQoUUJ5i/ZiC8vS/xHTKlCksXrwYZ2dnOnfuzO3bt5WnWD+m8OTzVHdQRcRm5MyZE4D4+HjT9wB3794lV65cD20fHx+favndu3fJnTu3aVjVg20etb9nkbVjCvD1119z9+5d8ubNC4CLiwu3b99m9uzZ9O7d+5n/1D+9MU2L8tT6MQXl6b+NqWEYTJs2jdmzZ9OtWze6dOkCKE/B+jEF5el/+d13dXUFICgoiIYNG7Jx40YaNmxo2t/9lKf/PqY+Pj5PPE+f7awXkadKynCUqKgos+VRUVGphusAFClSJFXb+Ph4bty4QeHChXnhhRfInTu3xft7Flk7pgDZsmUz/SeVonz58ty5c8f0XNWzLL0xTYvy1PoxBeXpv4lpQkICgwcPZs6cOQwZMoQBAwaY1ilPrR9TUJ6mN6anT59m69atZssKFSpEvnz5uHz5svIU68cUnnyeqkAVEZtRoUIF8ubNy86dO03LYmJiOHLkCDVq1EjVvmbNmly6dMns3V4p21arVg07OzuqVavGrl27zLbbuXMn1atXz6CzsC3WjmlycjLu7u7Mnj3bbLtDhw5RsGBB8ufPn0FnYjvSG9O0KE+tH1Pl6b+L6ZAhQ/jpp5+YOnUqH374odk65an1Y6o8TX9Mf/vtN/r27cutW7dMy86dO8f169cpU6aM8hTrxzRT8vSJzBUsImKhL774wqhVq5bxyy+/mN7d9eabbxp37941EhMTjaioKCM2NtYwDMNITk422rZta7Rs2dI4cOCAsX37duONN94whg0bZtrfb7/9ZlSsWNH46quvjFOnThkTJ0403Nzcnqv3oVk7phMmTDCqVatmrF+/3oiIiDC+/fZbw83NzVi2bFlmneITl56YPuiNN95I9UoU5an1Y6o8TV9MV65caZQvX9748ssvzd5zeH8b5an1Y6o8TV9Mr127ZtSvX9/o3r27ceLECWP37t1GixYtjDZt2hiJiYmGYShPDcP6MX3SeaoCVURsSmJiojFp0iSjTp06RpUqVYyPPvrIOH/+vGEYhnH+/HmjfPnyxsqVK03tr169avTu3duoUqWKUbt2bePTTz81e+ecYRhGaGio0aRJE8PV1dVo2bKl6f1zzwtrxzQhIcGYNWuW4eHhYbz66qvGW2+99Vz9MWUY6Y/p/R5WTBmG8tTaMVWepi+mH3zwgVG+fPmHft0fd+WpdWOqPE3/7/6ZM2eMjz/+2KhevbpRq1Ytw8/PL9X7OZWn1o3pk85TO8MwDOvflxURERERERFJHz2DKiIiIiIiIjZBBaqIiIiIiIjYBBWoIiIiIiIiYhNUoIqIiIiIiIhNUIEqIiIiIiIiNkEFqoiIiIiIiNgEFagiIiIiIiJiE1SgioiIiIiIiE1QgSoiIiIiIiI2QQWqiIiIiIiI2AQVqCIiIiIiImIT/h8XKX5hxwoX7AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot for final recovery predictions\n",
    "plt.figure(figsize=(8,6))\n",
    "\n",
    "feat_importances_final.nlargest(10).plot(kind='barh')\n",
    "plt.title('Feature importance for final recovery prediction')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Most top features are either the parameters of air flow (**..air** features) or rougher input parameters. Some features like, for example, `rougher.input.feed_ag` or `rougher.input.floatbank_10_xanthate` are important for predictions of both targets.  \n",
    "Top-5 for important features for predictions of both targets were already added to **important_features** list in section 2.\n",
    "  \n",
    "Let's try to use this information to improve model performance. Using only top features for predictions can help improve the metrics and learning rate. However, this probably is not a good idea for predictions on real test datasets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [],
   "source": [
    "selected_rougher = feat_importances_rougher.nlargest(10).index.tolist()\n",
    "selected_final = feat_importances_final.nlargest(10).index.tolist()\n",
    "\n",
    "X_rougher_train_selected = train_dataset['X_rougher'][selected_rougher]\n",
    "X_final_train_selected = train_dataset['X_final'][selected_final]\n",
    "\n",
    "train_dataset_topten = {\n",
    "    'X_rougher': X_rougher_train_selected, \n",
    "    'y_rougher': train_dataset['y_rougher'],\n",
    "    'X_final': X_final_train_selected, \n",
    "    'y_final': train_dataset['y_final'] \n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: total: 10.2 s\n",
      "Wall time: 21.9 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "grid_rougher_RFR_topten, grid_final_RFR_topten, sMAPE_RFR_topten = model_fit_gridsearch(\n",
    "    RFR, parameters_RFR, train_dataset_topten, 'RFR (top-10)'\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Resulting sMAPE score for RFR (top-10) = 7.01%\n"
     ]
    }
   ],
   "source": [
    "sMAPE_RFR_topten.print_score()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Learning rate is 6 times faster than when we use full training dataset. \n",
    "  \n",
    "* The resulting score is also slightly better (7.01% using top-10 features vs 7.49% for cross-validation on dataset with outliers).\n",
    "  \n",
    "Overall, RFR provided better results than LR on cross-validation. The result can be improved by tuning hyperparameters, but it's very time-consuming and a better solution is to use a gradient boosting model."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### CatBoostRegressor (gradient boosting)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Catboost is used for gradient boosting in this notebook. Hyperparameters for gridsearch and function for fitting models and returning final metrics are provided in the following block of code. A small subset of hyperparameters is used to save time, as this is a research task, not an industrial or competition one. Mean absolute error is used as loss function as Catboost doesn't allow for sMAPE usage to fit models. However, it is possible to use sMAPE for predictions by means of trained Catboost model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [],
   "source": [
    "parameters_CB = {\n",
    "    'iterations': [800, 1000],\n",
    "    'learning_rate': [0.1],\n",
    "    'depth': [5],\n",
    "    'l2_leaf_reg': [1]\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "def CB_fit_gridsearch(train_dataset_rougher, train_dataset_final, label=\"*not specified*\"):\n",
    "    \n",
    "    \"\"\"\n",
    "    This function returns grid_search objects for rougher and final recovery predictions by CBR.\n",
    "    It also returns the sMAPE metric object and adds sMAPE for current model to metrics dict as a side effect.\n",
    "    \"\"\"\n",
    "    \n",
    "    cb_rougher = CatBoostRegressor(\n",
    "        loss_function=\"MAE\", thread_count=-1, logging_level='Silent'  # suppress output\n",
    "    )\n",
    "    cb_final = CatBoostRegressor(\n",
    "        loss_function=\"MAE\", thread_count=-1, logging_level='Silent'\n",
    "    )\n",
    "    \n",
    "    print('Evaluation on rougher')\n",
    "    grid_rougher_CB = cb_rougher.grid_search(\n",
    "        parameters_CB, train_dataset_rougher, plot=True, verbose=False\n",
    "    )\n",
    "    print('\\n', 'Evaluation on final')\n",
    "    grid_final_CB = cb_final.grid_search(\n",
    "        parameters_CB, train_dataset_final, plot=True, verbose=False\n",
    "    )\n",
    "     \n",
    "    # getting best sMAPE scores via Catboost built-in metrics\n",
    "    sMAPE_rougher = min(\n",
    "        cb_rougher.eval_metrics(data=train_dataset_rougher, metrics=[\"SMAPE\"])[\"SMAPE\"]\n",
    "    )\n",
    "    sMAPE_final = min(\n",
    "        cb_final.eval_metrics(data=train_dataset_final, metrics=[\"SMAPE\"])[\"SMAPE\"]\n",
    "    )\n",
    "    sMAPE_CB = Resulting_sMAPE(sMAPE_rougher, sMAPE_final, label)\n",
    "    \n",
    "    # adding score to metrics dict\n",
    "    if label != \"*not specified*\":\n",
    "        metrics[label] = sMAPE_CB.score()\n",
    "\n",
    "    return cb_rougher, cb_final, sMAPE_CB"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, train model on dataset without outliers:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [],
   "source": [
    "# constructing objects for Catboost (dataset without outliers)\n",
    "train_dataset_rougher_full = Pool(train_dataset_full['X_rougher'], train_dataset_full['y_rougher']) \n",
    "train_dataset_final_full = Pool(train_dataset_full['X_final'], train_dataset_full['y_final']) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Evaluation on rougher\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "cd423530e61c4b40a119bbccda39900f",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "MetricVisualizer(layout=Layout(align_self='stretch', height='500px'))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " Evaluation on final\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "0f4ce85d52e8456cb1212b24aea75cd3",
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       "version_minor": 0
      },
      "text/plain": [
       "MetricVisualizer(layout=Layout(align_self='stretch', height='500px'))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: total: 7min 30s\n",
      "Wall time: 1min 14s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "# training Catboost on dataset without outliers\n",
    "cb_rougher_full, cb_final_full, sMAPE_CB_full = CB_fit_gridsearch(\n",
    "    train_dataset_rougher_full, \n",
    "    train_dataset_final_full,\n",
    "    'CB (full)'\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Resulting sMAPE score for this dataset:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Resulting sMAPE score for CB (full) = 5.32%\n"
     ]
    }
   ],
   "source": [
    "sMAPE_CB_full.print_score()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This result is significantly better than best results of LR (7.94%) or RFR (7.49%)!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Getting the results on dataset with outliers:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [],
   "source": [
    "# constructing objects for Catboost (dataset without outliers)\n",
    "train_dataset_rougher = Pool(train_dataset['X_rougher'], train_dataset['y_rougher']) \n",
    "train_dataset_final = Pool(train_dataset['X_final'], train_dataset['y_final']) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Evaluation on rougher\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "6525655d05bd4d818874befbb2243d94",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "MetricVisualizer(layout=Layout(align_self='stretch', height='500px'))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " Evaluation on final\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "505aef7ae5e04f488e00fcf03404d0bd",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "MetricVisualizer(layout=Layout(align_self='stretch', height='500px'))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: total: 7min 25s\n",
      "Wall time: 1min 11s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "# training Catboost on dataset without outliers\n",
    "cb_rougher, cb_final, sMAPE_CB = CB_fit_gridsearch(\n",
    "    train_dataset_rougher, \n",
    "    train_dataset_final,\n",
    "    'CB'\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Resulting sMAPE score for this dataset:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Resulting sMAPE score for CB = 3.13%\n"
     ]
    }
   ],
   "source": [
    "sMAPE_CB.print_score()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Removing outliers from dataset helps to significantly improve the key metric (from  5.32% to 3.13%!) \n",
    "  \n",
    "We can get the best MAE and hyperparameters using the following code: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [],
   "source": [
    "# display(cbr_rougher.get_best_score())  # get best score on training data\n",
    "# display(cbr_rougher.get_params())  # get hyperparameters of best model on training data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The last step is to compare the obtained results with the predictions on train dataset with feature selection."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [],
   "source": [
    "# constructing objects for Catboost (dataset with feature selection)\n",
    "train_dataset_rougher_fs = Pool(train_dataset_fs['X_rougher'], train_dataset_fs['y_rougher']) \n",
    "train_dataset_final_fs = Pool(train_dataset_fs['X_final'], train_dataset_fs['y_final']) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Evaluation on rougher\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "357b63f32aeb4dd3b82d542476f3f993",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "MetricVisualizer(layout=Layout(align_self='stretch', height='500px'))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " Evaluation on final\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "4761107054f0435c9e7e117de0f07e10",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "MetricVisualizer(layout=Layout(align_self='stretch', height='500px'))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: total: 6min 48s\n",
      "Wall time: 1min 6s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "# training Catboost on dataset with feature selection\n",
    "cb_rougher_fs, cb_final_fs, sMAPE_CB_fs = CB_fit_gridsearch(\n",
    "    train_dataset_rougher_fs, \n",
    "    train_dataset_final_fs,\n",
    "    'CB (FS)'\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Resulting sMAPE score for CB (FS) = 3.12%\n"
     ]
    }
   ],
   "source": [
    "sMAPE_CB_fs.print_score()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The resulting sMAPE score on training dataset with feature selection is almost the same as for dataset without outliers. There is surely no purpose in using this dataset for Catboost model as this approach only impairs model's predictive power without any metric gain even for training data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Conclusions:**\n",
    "1. Gradient boosting provides overall best sMAPE on cross-validation (3.13%) from all the suggested models.\n",
    "2. The proposed strategy of outliers removal proved to be successful for gradient boosting (as well as for other models), improving the key metric by more than 2% on the training dataset.\n",
    "3. Feature selection technique doesn't cause any metric improvement in case of Catboost regression.\n",
    "\n",
    "We have chosen the best model (Catboost trained on dataset without outliers). The next step is to obtain the results of this model on test data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Checking the best model on test data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The best model (CatBoostRegressor trained on dataset without outliers) will be used for predictions on test data. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_dataset_rougher = Pool(test_dataset['X_rougher'], test_dataset['y_rougher'])\n",
    "test_dataset_final = Pool(test_dataset['X_final'], test_dataset['y_final'])\n",
    "\n",
    "# calculating sMAPE for rougher and final\n",
    "sMAPE_rougher_test = min(cb_rougher.eval_metrics(data=test_dataset_rougher, metrics=['SMAPE'])['SMAPE'])\n",
    "sMAPE_final_test = min(cb_final.eval_metrics(data=test_dataset_final, metrics=['SMAPE'])['SMAPE'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Resulting sMAPE score for CB (test) = 8.37%\n"
     ]
    }
   ],
   "source": [
    "# calculating resulting sMAPE\n",
    "sMAPE_CB = Resulting_sMAPE(\n",
    "    sMAPE_rougher_test, \n",
    "    sMAPE_final_test, \n",
    "    'CB (test)'\n",
    ")\n",
    "metrics['CB (test)'] = sMAPE_CB.score()\n",
    "sMAPE_CB.print_score()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "sMAPE on test data (~8.37%) is significantly worse than on training sample (3.13%). This may be caused by overfitting due to outliers removal or by presence of many outliers in test data. Let's compare this result with the predictions of Catboost model that was trained on the full dataset with outliers:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [],
   "source": [
    "# calculating sMAPE for rougher and final (model fit on full training dataset)\n",
    "sMAPE_rougher_test_f = min(\n",
    "    cb_rougher_full.eval_metrics(data=test_dataset_rougher, metrics=['SMAPE'])['SMAPE']\n",
    ")\n",
    "sMAPE_final_test_f = min(\n",
    "    cb_final_full.eval_metrics(data=test_dataset_final, metrics=['SMAPE'])['SMAPE']\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Resulting sMAPE score for CB (full, test) = 8.38%\n"
     ]
    }
   ],
   "source": [
    "# calculating resulting sMAPE (model fit on full training dataset)\n",
    "sMAPE_CB_full = Resulting_sMAPE(\n",
    "    sMAPE_rougher_test_f, \n",
    "    sMAPE_final_test_f, \n",
    "    'CB (full, test)'\n",
    ")\n",
    "metrics['CB (full, test)'] = sMAPE_CB_full.score()\n",
    "sMAPE_CB_full.print_score()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The result for this model on test data is only slightly worse. Both trained models can be used for predictions of gold recovery on test datasets. However, model trained on a dataset without outliers produces slightly better results. \n",
    "  \n",
    "The score may further be improved by removing outliers from test dataset, but such practice requires some grounding.\n",
    "  \n",
    "The last part of the work is to determine whether the best obtained model can solve regression task better than the constant model."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Best model adequacy checking"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To understand if the obtained model is any better than constant model, we will compare the results of both on test datasets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [],
   "source": [
    "dummy = DummyRegressor(strategy='median')\n",
    "parameters_dummy = {\n",
    "    'strategy': ['median', 'mean']\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [],
   "source": [
    "# get best dummy models on CV\n",
    "dummy_rougher, dummy_final, sMAPE_dummy_train = model_fit_gridsearch(\n",
    "    dummy, \n",
    "    parameters_dummy, \n",
    "    train_dataset,\n",
    "    CV=2  # increasing CV-fold drops sMAPE dramatically\n",
    ")\n",
    "\n",
    "# get best scores for train and rougher\n",
    "sMAPE_rougher_d = calculate_sMAPE(\n",
    "    test_dataset['y_rougher'],  # true values\n",
    "    dummy_rougher.predict(test_dataset['X_rougher'])  # predicted values (DummyRegressor)\n",
    ")\n",
    "sMAPE_final_d = calculate_sMAPE(\n",
    "    test_dataset['y_final'], \n",
    "    dummy_final.predict(test_dataset['X_final'])\n",
    ")\n",
    "\n",
    "# calculate resulting sMAPE\n",
    "dummy_sMAPE_test = Resulting_sMAPE(sMAPE_rougher_d, sMAPE_final_d, 'Dummy (test)')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Resulting sMAPE score for Dummy (test) = 9.42%\n"
     ]
    }
   ],
   "source": [
    "metrics['Dummy (test)'] = dummy_sMAPE_test.score()\n",
    "dummy_sMAPE_test.print_score()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "sMAPE of the best Catboost model is more than 1% better, which means that the target of finding effective model for gold recovery predictions was fulfilled."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conclusion"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Preparation and analysis of a dataset containing information on the technological process of recovering gold from ore was conducted. Best model was selected by cross-validation and used for predictions on test data.\n",
    "* Preprocessing of data was performed. Illogical values were removed from test and training datasets. \n",
    "* Outliers were removed from training dataset (in accordance with 0.2% lower quantile and 0.1% upper quantile). The efficiency of this approach for model performance improvement was proven on all LR and RFR models considered in this work.\n",
    "* Different models (LR, RFR, Catboost gradient boosting) were compared in the task of gold recovery prediction from ore using sMAPE metric. Catboost showed the best result with the score of 3.13% on the training dataset.\n",
    "* All models were trained on 3 datasets (with outliers (full), without outliers, without outliers and with feature selection (FS)) in order to determine the most efficient approach for data preprocessing. Removing outliers improves prediction quality for all the models, while feature selection doesn't provide a significant metric enhancement.\n",
    "* The obtained metrics for all models are presented in the following table:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>model</th>\n",
       "      <th>LR (full)</th>\n",
       "      <th>LR</th>\n",
       "      <th>LR (FS)</th>\n",
       "      <th>RFR (full)</th>\n",
       "      <th>RFR</th>\n",
       "      <th>RFR (FS)</th>\n",
       "      <th>RFR (top-10)</th>\n",
       "      <th>CB (full)</th>\n",
       "      <th>CB</th>\n",
       "      <th>CB (FS)</th>\n",
       "      <th>CB (test)</th>\n",
       "      <th>CB (full, test)</th>\n",
       "      <th>Dummy (test)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>sMAPE, %</th>\n",
       "      <td>10.8</td>\n",
       "      <td>7.94</td>\n",
       "      <td>7.81</td>\n",
       "      <td>10.24</td>\n",
       "      <td>7.49</td>\n",
       "      <td>7.46</td>\n",
       "      <td>7.01</td>\n",
       "      <td>5.32</td>\n",
       "      <td>3.13</td>\n",
       "      <td>3.12</td>\n",
       "      <td>8.37</td>\n",
       "      <td>8.38</td>\n",
       "      <td>9.42</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "model     LR (full)    LR  LR (FS)  RFR (full)   RFR  RFR (FS)  RFR (top-10)  \\\n",
       "sMAPE, %       10.8  7.94     7.81       10.24  7.49      7.46          7.01   \n",
       "\n",
       "model     CB (full)    CB  CB (FS)  CB (test)  CB (full, test)  Dummy (test)  \n",
       "sMAPE, %       5.32  3.13     3.12       8.37             8.38          9.42  "
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame.from_dict(metrics, orient='index', columns=['sMAPE, %']).rename_axis('model').T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* The best model (Catboost trained on the test set without outliers) provided sMAPE = 8.37% on test dataset, which is more than 1% better than the predictions of constant model. The obtained model is effective for gold recovery predictions from ore based on process parameters.\n",
    "* It is shown that, in case of Catboost, removing outliers from training dataset doesn't help to improve metrics on test dataset. Training a model on a sample with removed outliers has almost no effect on the resulting sMAPE (8.37%) compared to a model trained on a full sample (sMAPE = 8.38%)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The resulting scores for the best model can be improved by:\n",
    "* Hyperarameters selection \n",
    "* More careful outliers removal\n",
    "* Feature engineering\n",
    "* Very selective data centering and scaling (general methods were tested and doesn't seem to work well)\n",
    "* Improving quality of test data\n",
    "* ??? (something I haven't yet learned)"
   ]
  }
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