predicting_va_evictions.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Inspired/based on https://github.com/rayidghani/magicloops and https://github.com/dssg/MLforPublicPolicy/blob/master/labs/2019/lab6_feature_generation_sol.ipynb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pipeline_evictions as pipeline\n",
    "import ml_loop_evictions as loop\n",
    "\n",
    "import importlib\n",
    "import datetime\n",
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Read data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "importlib.reload(pipeline)\n",
    "\n",
    "datafile = \"data/tracts.csv\"\n",
    "\n",
    "#Read data, parsing year column to date type\n",
    "data = pd.read_csv(datafile, parse_dates=['year'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Basic Cleaning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Delete rows that do not have eviction-rate. We do not want to impute this value\n",
    "\n",
    "data.dropna(subset=['eviction-rate'], inplace=True)\n",
    "data['eviction-rate'].isnull().values.any()\n",
    "data.reset_index(inplace=True, drop=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Create outcome label"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_eviction_rate(df, year,geoid):\n",
    "    \n",
    "  data_to_return = df.loc[(df['year'] == year) & (df['GEOID'] == geoid)]\n",
    "  \n",
    "  if(data_to_return.empty):\n",
    "    return 0\n",
    "  \n",
    "  return data_to_return['eviction-rate'].iloc[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "importlib.reload(pipeline)\n",
    "\n",
    "from dateutil.relativedelta import relativedelta\n",
    "\n",
    "#Obtain eviction-rate cutoff for the top 10%, for each year\n",
    "cutoff_10_percent={}\n",
    "for year in range(2000,2017):\n",
    "    year = pd.Timestamp(year,1,1)\n",
    "    cutoff_10_percent[year]=data.loc[data['year'] == year]['eviction-rate'].quantile(.9)\n",
    "    \n",
    "top_10_eviction_rate_in_any_next_3_years_column = np.zeros(len(data))\n",
    "\n",
    "#We will take the chance of generating feature 'top 10% last year', which we will mainly used for baseline model\n",
    "top_10_eviction_rate_last_year_column = np.zeros(len(data))\n",
    "\n",
    "for index, row in data.iterrows():\n",
    "  \n",
    "  #Because the outcome will come from eviction-rate in next 3 years and we have data till 2016,\n",
    "  #features data bust be from 2013 or before\n",
    "  \n",
    "  #We will also limit rows so that they all have one year before them (to calculate to_10_previous_year_feature)\n",
    " \n",
    "  if(row['year']<=pd.Timestamp(2013,1,1) and row['year']>=pd.Timestamp(2001,1,1)):    \n",
    "    \n",
    "    found_year_where_eviction_was_in_top_10_percent=0\n",
    "    \n",
    "    #Get eviction for the next 3 years\n",
    "    for i in range(1,4):\n",
    "      date_in_i_years = row['year'] + relativedelta(years=i)\n",
    "      eviction_rate_in_i_years = get_eviction_rate(data, date_in_i_years,row['GEOID'])\n",
    "    \n",
    "      top_10_eviction_rate_in_i_years = 1 if eviction_rate_in_i_years>= cutoff_10_percent[date_in_i_years] else 0\n",
    "      \n",
    "\n",
    "      \n",
    "      #If we found one year that meets requirement, we are done with looping\n",
    "      if(top_10_eviction_rate_in_i_years==1):\n",
    "        found_year_where_eviction_was_in_top_10_percent=1\n",
    "        break\n",
    "\n",
    "    top_10_eviction_rate_in_any_next_3_years_column[index]=found_year_where_eviction_was_in_top_10_percent\n",
    "\n",
    "    \n",
    "    #Generating feature top 10% last year.\n",
    "    date_last_years = row['year'] - relativedelta(years=1)\n",
    "    eviction_rate_last_year = get_eviction_rate(data, date_last_years,row['GEOID'])\n",
    "    top_10_eviction_rate_last_year = 1 if eviction_rate_last_year>= cutoff_10_percent[date_last_years] else 0\n",
    "\n",
    "    top_10_eviction_rate_last_year_column[index] = top_10_eviction_rate_last_year\n",
    "    \n",
    "    \n",
    "data['top_10_percent_in_any_next_3_years'] = top_10_eviction_rate_in_any_next_3_years_column\n",
    "\n",
    "label ='top_10_percent_in_any_next_3_years'\n",
    "\n",
    "#Append feature top 10% last year\n",
    "data['top_10_percent_last_year']=top_10_eviction_rate_last_year_column"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Create temporal train and test sets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "importlib.reload(pipeline)\n",
    "\n",
    "#Create sets of train and test data, based on different split thresholds\n",
    "#The split thresholds corresponds to the starting date of the testing data\n",
    "\n",
    "#Splits according to https://docs.google.com/spreadsheets/d/1ipqsgThz7hdXXyyNpTuqa4J1inc088lop7lhFsAQ_r0/edit#gid=0\n",
    "split_thresholds = [pd.Timestamp(i,1,1) for i in range (2005, 2014)]\n",
    "\n",
    "#Indicating which is the column to be used for splitting training and test data\n",
    "date_column='year'\n",
    "\n",
    "#Amount of data used for test set\n",
    "test_window = relativedelta(years=4)\n",
    "\n",
    "#Gap needed between training and test set\n",
    "prediction_horizon = relativedelta(years=3)\n",
    "\n",
    "#Gap needed for feature generation\n",
    "feature_generation_gap = relativedelta(years=1)\n",
    "\n",
    "#Generate train and test sets\n",
    "train_test_sets= pipeline.create_temp_validation_train_and_testing_sets(\n",
    "  data,\n",
    "  date_column,\n",
    "  label,\n",
    "  split_thresholds,\n",
    "  test_window,\n",
    "  prediction_horizon,\n",
    "  feature_generation_gap)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Imputation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "importlib.reload(pipeline)\n",
    "\n",
    "#Impute data on continuous columns for each training and test set\n",
    "\n",
    "float_columns = [column for column in data.columns if data[column].dtype=='float']\n",
    "\n",
    "#Do not consider GEOID column nor top_10_percent_in_any_next_3_years\n",
    "float_columns.remove('name')\n",
    "float_columns.remove('top_10_percent_in_any_next_3_years')\n",
    "\n",
    "\n",
    "for train_test_set in train_test_sets:\n",
    "  train_data = train_test_set['x_train']\n",
    "  test_data = train_test_set['x_test']\n",
    "\n",
    "  #fill na values with mean\n",
    "  pipeline.impute_data(train_data, float_columns)\n",
    "  pipeline.impute_data(test_data, float_columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "# Create features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "import feature_generation as fg\n",
    "\n",
    "importlib.reload(pipeline)\n",
    "importlib.reload(fg)\n",
    "\n",
    "#We will have to generate features independently for each different train/test set\n",
    "for train_test_set in train_test_sets:\n",
    "\n",
    "  train_features = fg.create_features(train_test_set['x_train'])\n",
    "  test_features = fg.create_features(train_test_set['x_test']) \n",
    "  \n",
    "  #Alternative for just working with default features\n",
    "  #train_features, test_features = pipeline.create_features(train_test_set)\n",
    "  \n",
    "  #Replace raw data in train_test_set with features generated\n",
    "  #Keep geoid\n",
    "  train_test_set['x_train'] = train_test_set['x_train']['GEOID'].to_frame().reset_index(drop=True).join(train_features)\n",
    "  train_test_set['x_test'] = train_test_set['x_test']['GEOID'].to_frame().reset_index(drop=True).join(test_features)\n",
    "  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Build Clasifiers and parameters generation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "importlib.reload(pipeline)\n",
    "\n",
    "#We define the specific models we want to run\n",
    "models_to_run=['Baseline','DT','LR','RF','NB','BA','AB','GB']#,ET,KNN,SVM'\n",
    "\n",
    "#Get all posible models and their different sets of parameters\n",
    "models, parameters_grid = pipeline.get_models_and_parameters('small')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Loop over models and different training/test sets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2019-06-12 16:06:08.357978: Running Baseline with params: {} on train/test set 2005-01-01 00:00:00\n",
      "2019-06-12 16:06:08.593987: Running DT with params: {'min_samples_split': 2, 'criterion': 'gini', 'max_depth': 2} on train/test set 2005-01-01 00:00:00\n",
      "2019-06-12 16:06:08.764038: Running DT with params: {'min_samples_split': 2, 'criterion': 'gini', 'max_depth': 50} on train/test set 2005-01-01 00:00:00\n",
      "2019-06-12 16:06:08.948663: Running DT with params: {'min_samples_split': 2, 'criterion': 'gini', 'max_depth': 100} on train/test set 2005-01-01 00:00:00\n",
      "2019-06-12 16:06:09.129843: Running LR with params: {'penalty': 'l1', 'C': 0.1} on train/test set 2005-01-01 00:00:00\n",
      "2019-06-12 16:06:14.647509: Running LR with params: {'penalty': 'l2', 'C': 0.1} on train/test set 2005-01-01 00:00:00\n",
      "2019-06-12 16:06:15.098431: Running LR with params: {'penalty': 'l1', 'C': 10} on train/test set 2005-01-01 00:00:00\n",
      "2019-06-12 16:06:25.840690: Running LR with params: {'penalty': 'l2', 'C': 10} on train/test set 2005-01-01 00:00:00\n",
      "2019-06-12 16:06:26.265160: Running RF with params: {'n_estimators': 100, 'n_jobs': -1, 'max_features': 'sqrt', 'min_samples_split': 2, 'max_depth': 5} on train/test set 2005-01-01 00:00:00\n",
      "2019-06-12 16:06:26.673283: Running RF with params: {'n_estimators': 1000, 'n_jobs': -1, 'max_features': 'sqrt', 'min_samples_split': 2, 'max_depth': 5} on train/test set 2005-01-01 00:00:00\n",
      "2019-06-12 16:06:28.231971: Running RF with params: {'n_estimators': 100, 'n_jobs': -1, 'max_features': 'sqrt', 'min_samples_split': 2, 'max_depth': 50} on train/test set 2005-01-01 00:00:00\n",
      "2019-06-12 16:06:28.769187: Running RF with params: {'n_estimators': 1000, 'n_jobs': -1, 'max_features': 'sqrt', 'min_samples_split': 2, 'max_depth': 50} on train/test set 2005-01-01 00:00:00\n",
      "2019-06-12 16:06:30.010424: Running RF with params: {'n_estimators': 100, 'n_jobs': -1, 'max_features': 'sqrt', 'min_samples_split': 2, 'max_depth': 100} on train/test set 2005-01-01 00:00:00\n",
      "2019-06-12 16:06:30.425205: Running RF with params: {'n_estimators': 1000, 'n_jobs': -1, 'max_features': 'sqrt', 'min_samples_split': 2, 'max_depth': 100} on train/test set 2005-01-01 00:00:00\n",
      "2019-06-12 16:06:32.470343: Running NB with params: {} on train/test set 2005-01-01 00:00:00\n",
      "2019-06-12 16:06:32.645719: Running BA with params: {'n_estimators': 10} on train/test set 2005-01-01 00:00:00\n",
      "2019-06-12 16:06:33.285174: Running BA with params: {'n_estimators': 100} on train/test set 2005-01-01 00:00:00\n",
      "2019-06-12 16:06:38.142220: Running AB with params: {'n_estimators': 10, 'algorithm': 'SAMME'} on train/test set 2005-01-01 00:00:00\n",
      "2019-06-12 16:06:38.337967: Running AB with params: {'n_estimators': 100, 'algorithm': 'SAMME'} on train/test set 2005-01-01 00:00:00\n",
      "2019-06-12 16:06:38.833906: Running GB with params: {'learning_rate': 0.001, 'n_estimators': 10} on train/test set 2005-01-01 00:00:00\n",
      "2019-06-12 16:06:39.033569: Running GB with params: {'learning_rate': 0.001, 'n_estimators': 100} on train/test set 2005-01-01 00:00:00\n",
      "2019-06-12 16:06:39.563432: Running GB with params: {'learning_rate': 0.1, 'n_estimators': 10} on train/test set 2005-01-01 00:00:00\n",
      "2019-06-12 16:06:39.763250: Running GB with params: {'learning_rate': 0.1, 'n_estimators': 100} on train/test set 2005-01-01 00:00:00\n",
      "2019-06-12 16:06:40.285147: Running Baseline with params: {} on train/test set 2006-01-01 00:00:00\n",
      "2019-06-12 16:06:40.437530: Running DT with params: {'min_samples_split': 2, 'criterion': 'gini', 'max_depth': 2} on train/test set 2006-01-01 00:00:00\n",
      "2019-06-12 16:06:40.611922: Running DT with params: {'min_samples_split': 2, 'criterion': 'gini', 'max_depth': 50} on train/test set 2006-01-01 00:00:00\n",
      "2019-06-12 16:06:40.820194: Running DT with params: {'min_samples_split': 2, 'criterion': 'gini', 'max_depth': 100} on train/test set 2006-01-01 00:00:00\n",
      "2019-06-12 16:06:41.084267: Running LR with params: {'penalty': 'l1', 'C': 0.1} on train/test set 2006-01-01 00:00:00\n",
      "2019-06-12 16:06:43.396389: Running LR with params: {'penalty': 'l2', 'C': 0.1} on train/test set 2006-01-01 00:00:00\n",
      "2019-06-12 16:06:43.905095: Running LR with params: {'penalty': 'l1', 'C': 10} on train/test set 2006-01-01 00:00:00\n",
      "2019-06-12 16:07:05.439111: Running LR with params: {'penalty': 'l2', 'C': 10} on train/test set 2006-01-01 00:00:00\n",
      "2019-06-12 16:07:05.939500: Running RF with params: {'n_estimators': 100, 'n_jobs': -1, 'max_features': 'sqrt', 'min_samples_split': 2, 'max_depth': 5} on train/test set 2006-01-01 00:00:00\n",
      "2019-06-12 16:07:06.371544: Running RF with params: {'n_estimators': 1000, 'n_jobs': -1, 'max_features': 'sqrt', 'min_samples_split': 2, 'max_depth': 5} on train/test set 2006-01-01 00:00:00\n",
      "2019-06-12 16:07:07.612285: Running RF with params: {'n_estimators': 100, 'n_jobs': -1, 'max_features': 'sqrt', 'min_samples_split': 2, 'max_depth': 50} on train/test set 2006-01-01 00:00:00\n",
      "2019-06-12 16:07:08.032019: Running RF with params: {'n_estimators': 1000, 'n_jobs': -1, 'max_features': 'sqrt', 'min_samples_split': 2, 'max_depth': 50} on train/test set 2006-01-01 00:00:00\n",
      "2019-06-12 16:07:09.951939: Running RF with params: {'n_estimators': 100, 'n_jobs': -1, 'max_features': 'sqrt', 'min_samples_split': 2, 'max_depth': 100} on train/test set 2006-01-01 00:00:00\n",
      "2019-06-12 16:07:10.374690: Running RF with params: {'n_estimators': 1000, 'n_jobs': -1, 'max_features': 'sqrt', 'min_samples_split': 2, 'max_depth': 100} on train/test set 2006-01-01 00:00:00\n",
      "2019-06-12 16:07:11.711617: Running NB with params: {} on train/test set 2006-01-01 00:00:00\n",
      "2019-06-12 16:07:11.877725: Running BA with params: {'n_estimators': 10} on train/test set 2006-01-01 00:00:00\n",
      "2019-06-12 16:07:12.652672: Running BA with params: {'n_estimators': 100} on train/test set 2006-01-01 00:00:00\n",
      "2019-06-12 16:07:19.667922: Running AB with params: {'n_estimators': 10, 'algorithm': 'SAMME'} on train/test set 2006-01-01 00:00:00\n",
      "2019-06-12 16:07:19.894424: Running AB with params: {'n_estimators': 100, 'algorithm': 'SAMME'} on train/test set 2006-01-01 00:00:00\n",
      "2019-06-12 16:07:20.620395: Running GB with params: {'learning_rate': 0.001, 'n_estimators': 10} on train/test set 2006-01-01 00:00:00\n",
      "2019-06-12 16:07:20.855270: Running GB with params: {'learning_rate': 0.001, 'n_estimators': 100} on train/test set 2006-01-01 00:00:00\n",
      "2019-06-12 16:07:21.778642: Running GB with params: {'learning_rate': 0.1, 'n_estimators': 10} on train/test set 2006-01-01 00:00:00\n",
      "2019-06-12 16:07:22.036459: Running GB with params: {'learning_rate': 0.1, 'n_estimators': 100} on train/test set 2006-01-01 00:00:00\n",
      "2019-06-12 16:07:22.894139: Running Baseline with params: {} on train/test set 2007-01-01 00:00:00\n",
      "2019-06-12 16:07:23.036021: Running DT with params: {'min_samples_split': 2, 'criterion': 'gini', 'max_depth': 2} on train/test set 2007-01-01 00:00:00\n",
      "2019-06-12 16:07:23.209045: Running DT with params: {'min_samples_split': 2, 'criterion': 'gini', 'max_depth': 50} on train/test set 2007-01-01 00:00:00\n",
      "2019-06-12 16:07:23.435133: Running DT with params: {'min_samples_split': 2, 'criterion': 'gini', 'max_depth': 100} on train/test set 2007-01-01 00:00:00\n",
      "2019-06-12 16:07:23.678514: Running LR with params: {'penalty': 'l1', 'C': 0.1} on train/test set 2007-01-01 00:00:00\n",
      "2019-06-12 16:07:38.923281: Running LR with params: {'penalty': 'l2', 'C': 0.1} on train/test set 2007-01-01 00:00:00\n",
      "2019-06-12 16:07:39.485834: Running LR with params: {'penalty': 'l1', 'C': 10} on train/test set 2007-01-01 00:00:00\n",
      "2019-06-12 16:08:13.595028: Running LR with params: {'penalty': 'l2', 'C': 10} on train/test set 2007-01-01 00:00:00\n",
      "2019-06-12 16:08:14.212288: Running RF with params: {'n_estimators': 100, 'n_jobs': -1, 'max_features': 'sqrt', 'min_samples_split': 2, 'max_depth': 5} on train/test set 2007-01-01 00:00:00\n",
      "2019-06-12 16:08:14.658474: Running RF with params: {'n_estimators': 1000, 'n_jobs': -1, 'max_features': 'sqrt', 'min_samples_split': 2, 'max_depth': 5} on train/test set 2007-01-01 00:00:00\n",
      "2019-06-12 16:08:16.015781: Running RF with params: {'n_estimators': 100, 'n_jobs': -1, 'max_features': 'sqrt', 'min_samples_split': 2, 'max_depth': 50} on train/test set 2007-01-01 00:00:00\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2019-06-12 16:08:16.418440: Running RF with params: {'n_estimators': 1000, 'n_jobs': -1, 'max_features': 'sqrt', 'min_samples_split': 2, 'max_depth': 50} on train/test set 2007-01-01 00:00:00\n",
      "2019-06-12 16:08:17.863315: Running RF with params: {'n_estimators': 100, 'n_jobs': -1, 'max_features': 'sqrt', 'min_samples_split': 2, 'max_depth': 100} on train/test set 2007-01-01 00:00:00\n",
      "2019-06-12 16:08:18.286570: Running RF with params: {'n_estimators': 1000, 'n_jobs': -1, 'max_features': 'sqrt', 'min_samples_split': 2, 'max_depth': 100} on train/test set 2007-01-01 00:00:00\n",
      "2019-06-12 16:08:20.438562: Running NB with params: {} on train/test set 2007-01-01 00:00:00\n",
      "2019-06-12 16:08:20.604642: Running BA with params: {'n_estimators': 10} on train/test set 2007-01-01 00:00:00\n",
      "2019-06-12 16:08:21.557640: Running BA with params: {'n_estimators': 100} on train/test set 2007-01-01 00:00:00\n",
      "2019-06-12 16:08:29.550762: Running AB with params: {'n_estimators': 10, 'algorithm': 'SAMME'} on train/test set 2007-01-01 00:00:00\n",
      "2019-06-12 16:08:29.786884: Running AB with params: {'n_estimators': 100, 'algorithm': 'SAMME'} on train/test set 2007-01-01 00:00:00\n",
      "2019-06-12 16:08:30.869301: Running GB with params: {'learning_rate': 0.001, 'n_estimators': 10} on train/test set 2007-01-01 00:00:00\n",
      "2019-06-12 16:08:31.177372: Running GB with params: {'learning_rate': 0.001, 'n_estimators': 100} on train/test set 2007-01-01 00:00:00\n",
      "2019-06-12 16:08:32.464149: Running GB with params: {'learning_rate': 0.1, 'n_estimators': 10} on train/test set 2007-01-01 00:00:00\n",
      "2019-06-12 16:08:32.755490: Running GB with params: {'learning_rate': 0.1, 'n_estimators': 100} on train/test set 2007-01-01 00:00:00\n",
      "2019-06-12 16:08:33.994961: Running Baseline with params: {} on train/test set 2008-01-01 00:00:00\n",
      "2019-06-12 16:08:34.155967: Running DT with params: {'min_samples_split': 2, 'criterion': 'gini', 'max_depth': 2} on train/test set 2008-01-01 00:00:00\n",
      "2019-06-12 16:08:34.338753: Running DT with params: {'min_samples_split': 2, 'criterion': 'gini', 'max_depth': 50} on train/test set 2008-01-01 00:00:00\n",
      "2019-06-12 16:08:34.605435: Running DT with params: {'min_samples_split': 2, 'criterion': 'gini', 'max_depth': 100} on train/test set 2008-01-01 00:00:00\n",
      "2019-06-12 16:08:34.868356: Running LR with params: {'penalty': 'l1', 'C': 0.1} on train/test set 2008-01-01 00:00:00\n",
      "2019-06-12 16:08:37.010971: Running LR with params: {'penalty': 'l2', 'C': 0.1} on train/test set 2008-01-01 00:00:00\n",
      "2019-06-12 16:08:37.600850: Running LR with params: {'penalty': 'l1', 'C': 10} on train/test set 2008-01-01 00:00:00\n",
      "2019-06-12 16:09:23.387323: Running LR with params: {'penalty': 'l2', 'C': 10} on train/test set 2008-01-01 00:00:00\n",
      "2019-06-12 16:09:23.984005: Running RF with params: {'n_estimators': 100, 'n_jobs': -1, 'max_features': 'sqrt', 'min_samples_split': 2, 'max_depth': 5} on train/test set 2008-01-01 00:00:00\n",
      "2019-06-12 16:09:24.421682: Running RF with params: {'n_estimators': 1000, 'n_jobs': -1, 'max_features': 'sqrt', 'min_samples_split': 2, 'max_depth': 5} on train/test set 2008-01-01 00:00:00\n",
      "2019-06-12 16:09:25.765555: Running RF with params: {'n_estimators': 100, 'n_jobs': -1, 'max_features': 'sqrt', 'min_samples_split': 2, 'max_depth': 50} on train/test set 2008-01-01 00:00:00\n",
      "2019-06-12 16:09:26.292921: Running RF with params: {'n_estimators': 1000, 'n_jobs': -1, 'max_features': 'sqrt', 'min_samples_split': 2, 'max_depth': 50} on train/test set 2008-01-01 00:00:00\n",
      "2019-06-12 16:09:28.641785: Running RF with params: {'n_estimators': 100, 'n_jobs': -1, 'max_features': 'sqrt', 'min_samples_split': 2, 'max_depth': 100} on train/test set 2008-01-01 00:00:00\n",
      "2019-06-12 16:09:29.165271: Running RF with params: {'n_estimators': 1000, 'n_jobs': -1, 'max_features': 'sqrt', 'min_samples_split': 2, 'max_depth': 100} on train/test set 2008-01-01 00:00:00\n",
      "2019-06-12 16:09:31.514074: Running NB with params: {} on train/test set 2008-01-01 00:00:00\n",
      "2019-06-12 16:09:31.680257: Running BA with params: {'n_estimators': 10} on train/test set 2008-01-01 00:00:00\n",
      "2019-06-12 16:09:32.739132: Running BA with params: {'n_estimators': 100} on train/test set 2008-01-01 00:00:00\n",
      "2019-06-12 16:09:42.499576: Running AB with params: {'n_estimators': 10, 'algorithm': 'SAMME'} on train/test set 2008-01-01 00:00:00\n",
      "2019-06-12 16:09:42.765753: Running AB with params: {'n_estimators': 100, 'algorithm': 'SAMME'} on train/test set 2008-01-01 00:00:00\n",
      "2019-06-12 16:09:43.980989: Running GB with params: {'learning_rate': 0.001, 'n_estimators': 10} on train/test set 2008-01-01 00:00:00\n",
      "2019-06-12 16:09:44.295554: Running GB with params: {'learning_rate': 0.001, 'n_estimators': 100} on train/test set 2008-01-01 00:00:00\n",
      "2019-06-12 16:09:45.864036: Running GB with params: {'learning_rate': 0.1, 'n_estimators': 10} on train/test set 2008-01-01 00:00:00\n",
      "2019-06-12 16:09:46.278413: Running GB with params: {'learning_rate': 0.1, 'n_estimators': 100} on train/test set 2008-01-01 00:00:00\n",
      "2019-06-12 16:09:47.832836: Running Baseline with params: {} on train/test set 2009-01-01 00:00:00\n",
      "2019-06-12 16:09:47.977820: Running DT with params: {'min_samples_split': 2, 'criterion': 'gini', 'max_depth': 2} on train/test set 2009-01-01 00:00:00\n",
      "2019-06-12 16:09:48.162006: Running DT with params: {'min_samples_split': 2, 'criterion': 'gini', 'max_depth': 50} on train/test set 2009-01-01 00:00:00\n",
      "2019-06-12 16:09:48.475365: Running DT with params: {'min_samples_split': 2, 'criterion': 'gini', 'max_depth': 100} on train/test set 2009-01-01 00:00:00\n",
      "2019-06-12 16:09:48.793951: Running LR with params: {'penalty': 'l1', 'C': 0.1} on train/test set 2009-01-01 00:00:00\n",
      "2019-06-12 16:09:52.416017: Running LR with params: {'penalty': 'l2', 'C': 0.1} on train/test set 2009-01-01 00:00:00\n",
      "2019-06-12 16:09:53.162933: Running LR with params: {'penalty': 'l1', 'C': 10} on train/test set 2009-01-01 00:00:00\n",
      "2019-06-12 16:10:57.910981: Running LR with params: {'penalty': 'l2', 'C': 10} on train/test set 2009-01-01 00:00:00\n",
      "2019-06-12 16:10:58.700995: Running RF with params: {'n_estimators': 100, 'n_jobs': -1, 'max_features': 'sqrt', 'min_samples_split': 2, 'max_depth': 5} on train/test set 2009-01-01 00:00:00\n",
      "2019-06-12 16:10:59.114008: Running RF with params: {'n_estimators': 1000, 'n_jobs': -1, 'max_features': 'sqrt', 'min_samples_split': 2, 'max_depth': 5} on train/test set 2009-01-01 00:00:00\n",
      "2019-06-12 16:11:00.967955: Running RF with params: {'n_estimators': 100, 'n_jobs': -1, 'max_features': 'sqrt', 'min_samples_split': 2, 'max_depth': 50} on train/test set 2009-01-01 00:00:00\n",
      "2019-06-12 16:11:01.509207: Running RF with params: {'n_estimators': 1000, 'n_jobs': -1, 'max_features': 'sqrt', 'min_samples_split': 2, 'max_depth': 50} on train/test set 2009-01-01 00:00:00\n",
      "2019-06-12 16:11:03.422598: Running RF with params: {'n_estimators': 100, 'n_jobs': -1, 'max_features': 'sqrt', 'min_samples_split': 2, 'max_depth': 100} on train/test set 2009-01-01 00:00:00\n",
      "2019-06-12 16:11:03.972592: Running RF with params: {'n_estimators': 1000, 'n_jobs': -1, 'max_features': 'sqrt', 'min_samples_split': 2, 'max_depth': 100} on train/test set 2009-01-01 00:00:00\n",
      "2019-06-12 16:11:06.122546: Running NB with params: {} on train/test set 2009-01-01 00:00:00\n",
      "2019-06-12 16:11:06.295562: Running BA with params: {'n_estimators': 10} on train/test set 2009-01-01 00:00:00\n",
      "2019-06-12 16:11:07.481013: Running BA with params: {'n_estimators': 100} on train/test set 2009-01-01 00:00:00\n",
      "2019-06-12 16:11:18.195336: Running AB with params: {'n_estimators': 10, 'algorithm': 'SAMME'} on train/test set 2009-01-01 00:00:00\n",
      "2019-06-12 16:11:18.529144: Running AB with params: {'n_estimators': 100, 'algorithm': 'SAMME'} on train/test set 2009-01-01 00:00:00\n",
      "2019-06-12 16:11:20.208720: Running GB with params: {'learning_rate': 0.001, 'n_estimators': 10} on train/test set 2009-01-01 00:00:00\n",
      "2019-06-12 16:11:20.582534: Running GB with params: {'learning_rate': 0.001, 'n_estimators': 100} on train/test set 2009-01-01 00:00:00\n",
      "2019-06-12 16:11:22.637875: Running GB with params: {'learning_rate': 0.1, 'n_estimators': 10} on train/test set 2009-01-01 00:00:00\n",
      "2019-06-12 16:11:23.007216: Running GB with params: {'learning_rate': 0.1, 'n_estimators': 100} on train/test set 2009-01-01 00:00:00\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2019-06-12 16:11:25.025627: Running Baseline with params: {} on train/test set 2010-01-01 00:00:00\n",
      "2019-06-12 16:11:25.217489: Running DT with params: {'min_samples_split': 2, 'criterion': 'gini', 'max_depth': 2} on train/test set 2010-01-01 00:00:00\n",
      "2019-06-12 16:11:25.417099: Running DT with params: {'min_samples_split': 2, 'criterion': 'gini', 'max_depth': 50} on train/test set 2010-01-01 00:00:00\n",
      "2019-06-12 16:11:25.800048: Running DT with params: {'min_samples_split': 2, 'criterion': 'gini', 'max_depth': 100} on train/test set 2010-01-01 00:00:00\n",
      "2019-06-12 16:11:26.179151: Running LR with params: {'penalty': 'l1', 'C': 0.1} on train/test set 2010-01-01 00:00:00\n",
      "2019-06-12 16:11:30.678363: Running LR with params: {'penalty': 'l2', 'C': 0.1} on train/test set 2010-01-01 00:00:00\n",
      "2019-06-12 16:11:31.444259: Running LR with params: {'penalty': 'l1', 'C': 10} on train/test set 2010-01-01 00:00:00\n",
      "2019-06-12 16:12:51.499627: Running LR with params: {'penalty': 'l2', 'C': 10} on train/test set 2010-01-01 00:00:00\n",
      "2019-06-12 16:12:52.548343: Running RF with params: {'n_estimators': 100, 'n_jobs': -1, 'max_features': 'sqrt', 'min_samples_split': 2, 'max_depth': 5} on train/test set 2010-01-01 00:00:00\n",
      "2019-06-12 16:12:53.055366: Running RF with params: {'n_estimators': 1000, 'n_jobs': -1, 'max_features': 'sqrt', 'min_samples_split': 2, 'max_depth': 5} on train/test set 2010-01-01 00:00:00\n",
      "2019-06-12 16:12:55.001819: Running RF with params: {'n_estimators': 100, 'n_jobs': -1, 'max_features': 'sqrt', 'min_samples_split': 2, 'max_depth': 50} on train/test set 2010-01-01 00:00:00\n",
      "2019-06-12 16:12:55.558517: Running RF with params: {'n_estimators': 1000, 'n_jobs': -1, 'max_features': 'sqrt', 'min_samples_split': 2, 'max_depth': 50} on train/test set 2010-01-01 00:00:00\n",
      "2019-06-12 16:12:57.796122: Running RF with params: {'n_estimators': 100, 'n_jobs': -1, 'max_features': 'sqrt', 'min_samples_split': 2, 'max_depth': 100} on train/test set 2010-01-01 00:00:00\n",
      "2019-06-12 16:12:58.324933: Running RF with params: {'n_estimators': 1000, 'n_jobs': -1, 'max_features': 'sqrt', 'min_samples_split': 2, 'max_depth': 100} on train/test set 2010-01-01 00:00:00\n",
      "2019-06-12 16:13:00.668044: Running NB with params: {} on train/test set 2010-01-01 00:00:00\n",
      "2019-06-12 16:13:00.844326: Running BA with params: {'n_estimators': 10} on train/test set 2010-01-01 00:00:00\n",
      "2019-06-12 16:13:02.448709: Running BA with params: {'n_estimators': 100} on train/test set 2010-01-01 00:00:00\n",
      "2019-06-12 16:13:17.192205: Running AB with params: {'n_estimators': 10, 'algorithm': 'SAMME'} on train/test set 2010-01-01 00:00:00\n",
      "2019-06-12 16:13:17.545252: Running AB with params: {'n_estimators': 100, 'algorithm': 'SAMME'} on train/test set 2010-01-01 00:00:00\n",
      "2019-06-12 16:13:19.437905: Running GB with params: {'learning_rate': 0.001, 'n_estimators': 10} on train/test set 2010-01-01 00:00:00\n",
      "2019-06-12 16:13:19.848681: Running GB with params: {'learning_rate': 0.001, 'n_estimators': 100} on train/test set 2010-01-01 00:00:00\n",
      "2019-06-12 16:13:22.245212: Running GB with params: {'learning_rate': 0.1, 'n_estimators': 10} on train/test set 2010-01-01 00:00:00\n",
      "2019-06-12 16:13:22.664947: Running GB with params: {'learning_rate': 0.1, 'n_estimators': 100} on train/test set 2010-01-01 00:00:00\n",
      "2019-06-12 16:13:25.007949: Running Baseline with params: {} on train/test set 2011-01-01 00:00:00\n",
      "2019-06-12 16:13:25.195293: Running DT with params: {'min_samples_split': 2, 'criterion': 'gini', 'max_depth': 2} on train/test set 2011-01-01 00:00:00\n",
      "2019-06-12 16:13:25.401514: Running DT with params: {'min_samples_split': 2, 'criterion': 'gini', 'max_depth': 50} on train/test set 2011-01-01 00:00:00\n",
      "2019-06-12 16:13:25.788939: Running DT with params: {'min_samples_split': 2, 'criterion': 'gini', 'max_depth': 100} on train/test set 2011-01-01 00:00:00\n",
      "2019-06-12 16:13:26.181692: Running LR with params: {'penalty': 'l1', 'C': 0.1} on train/test set 2011-01-01 00:00:00\n",
      "2019-06-12 16:13:30.034225: Running LR with params: {'penalty': 'l2', 'C': 0.1} on train/test set 2011-01-01 00:00:00\n",
      "2019-06-12 16:13:30.926006: Running LR with params: {'penalty': 'l1', 'C': 10} on train/test set 2011-01-01 00:00:00\n",
      "2019-06-12 16:15:00.251157: Running LR with params: {'penalty': 'l2', 'C': 10} on train/test set 2011-01-01 00:00:00\n",
      "2019-06-12 16:15:01.309637: Running RF with params: {'n_estimators': 100, 'n_jobs': -1, 'max_features': 'sqrt', 'min_samples_split': 2, 'max_depth': 5} on train/test set 2011-01-01 00:00:00\n",
      "2019-06-12 16:15:01.872865: Running RF with params: {'n_estimators': 1000, 'n_jobs': -1, 'max_features': 'sqrt', 'min_samples_split': 2, 'max_depth': 5} on train/test set 2011-01-01 00:00:00\n",
      "2019-06-12 16:15:03.408273: Running RF with params: {'n_estimators': 100, 'n_jobs': -1, 'max_features': 'sqrt', 'min_samples_split': 2, 'max_depth': 50} on train/test set 2011-01-01 00:00:00\n",
      "2019-06-12 16:15:03.974109: Running RF with params: {'n_estimators': 1000, 'n_jobs': -1, 'max_features': 'sqrt', 'min_samples_split': 2, 'max_depth': 50} on train/test set 2011-01-01 00:00:00\n",
      "2019-06-12 16:15:06.632492: Running RF with params: {'n_estimators': 100, 'n_jobs': -1, 'max_features': 'sqrt', 'min_samples_split': 2, 'max_depth': 100} on train/test set 2011-01-01 00:00:00\n",
      "2019-06-12 16:15:07.206975: Running RF with params: {'n_estimators': 1000, 'n_jobs': -1, 'max_features': 'sqrt', 'min_samples_split': 2, 'max_depth': 100} on train/test set 2011-01-01 00:00:00\n",
      "2019-06-12 16:15:09.657655: Running NB with params: {} on train/test set 2011-01-01 00:00:00\n",
      "2019-06-12 16:15:09.838316: Running BA with params: {'n_estimators': 10} on train/test set 2011-01-01 00:00:00\n",
      "2019-06-12 16:15:11.812086: Running BA with params: {'n_estimators': 100} on train/test set 2011-01-01 00:00:00\n",
      "2019-06-12 16:15:30.589490: Running AB with params: {'n_estimators': 10, 'algorithm': 'SAMME'} on train/test set 2011-01-01 00:00:00\n",
      "2019-06-12 16:15:30.958867: Running AB with params: {'n_estimators': 100, 'algorithm': 'SAMME'} on train/test set 2011-01-01 00:00:00\n",
      "2019-06-12 16:15:33.170039: Running GB with params: {'learning_rate': 0.001, 'n_estimators': 10} on train/test set 2011-01-01 00:00:00\n",
      "2019-06-12 16:15:33.618322: Running GB with params: {'learning_rate': 0.001, 'n_estimators': 100} on train/test set 2011-01-01 00:00:00\n",
      "2019-06-12 16:15:36.274123: Running GB with params: {'learning_rate': 0.1, 'n_estimators': 10} on train/test set 2011-01-01 00:00:00\n",
      "2019-06-12 16:15:36.885612: Running GB with params: {'learning_rate': 0.1, 'n_estimators': 100} on train/test set 2011-01-01 00:00:00\n",
      "2019-06-12 16:15:39.610483: Running Baseline with params: {} on train/test set 2012-01-01 00:00:00\n",
      "2019-06-12 16:15:39.760980: Running DT with params: {'min_samples_split': 2, 'criterion': 'gini', 'max_depth': 2} on train/test set 2012-01-01 00:00:00\n",
      "2019-06-12 16:15:39.964708: Running DT with params: {'min_samples_split': 2, 'criterion': 'gini', 'max_depth': 50} on train/test set 2012-01-01 00:00:00\n",
      "2019-06-12 16:15:40.366853: Running DT with params: {'min_samples_split': 2, 'criterion': 'gini', 'max_depth': 100} on train/test set 2012-01-01 00:00:00\n",
      "2019-06-12 16:15:40.779443: Running LR with params: {'penalty': 'l1', 'C': 0.1} on train/test set 2012-01-01 00:00:00\n",
      "2019-06-12 16:15:48.704121: Running LR with params: {'penalty': 'l2', 'C': 0.1} on train/test set 2012-01-01 00:00:00\n",
      "2019-06-12 16:15:49.851096: Running LR with params: {'penalty': 'l1', 'C': 10} on train/test set 2012-01-01 00:00:00\n",
      "2019-06-12 16:17:30.056014: Running LR with params: {'penalty': 'l2', 'C': 10} on train/test set 2012-01-01 00:00:00\n",
      "2019-06-12 16:17:31.077732: Running RF with params: {'n_estimators': 100, 'n_jobs': -1, 'max_features': 'sqrt', 'min_samples_split': 2, 'max_depth': 5} on train/test set 2012-01-01 00:00:00\n",
      "2019-06-12 16:17:31.631914: Running RF with params: {'n_estimators': 1000, 'n_jobs': -1, 'max_features': 'sqrt', 'min_samples_split': 2, 'max_depth': 5} on train/test set 2012-01-01 00:00:00\n",
      "2019-06-12 16:17:33.260992: Running RF with params: {'n_estimators': 100, 'n_jobs': -1, 'max_features': 'sqrt', 'min_samples_split': 2, 'max_depth': 50} on train/test set 2012-01-01 00:00:00\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2019-06-12 16:17:33.917487: Running RF with params: {'n_estimators': 1000, 'n_jobs': -1, 'max_features': 'sqrt', 'min_samples_split': 2, 'max_depth': 50} on train/test set 2012-01-01 00:00:00\n",
      "2019-06-12 16:17:36.966529: Running RF with params: {'n_estimators': 100, 'n_jobs': -1, 'max_features': 'sqrt', 'min_samples_split': 2, 'max_depth': 100} on train/test set 2012-01-01 00:00:00\n",
      "2019-06-12 16:17:37.621402: Running RF with params: {'n_estimators': 1000, 'n_jobs': -1, 'max_features': 'sqrt', 'min_samples_split': 2, 'max_depth': 100} on train/test set 2012-01-01 00:00:00\n",
      "2019-06-12 16:17:40.231313: Running NB with params: {} on train/test set 2012-01-01 00:00:00\n",
      "2019-06-12 16:17:40.419189: Running BA with params: {'n_estimators': 10} on train/test set 2012-01-01 00:00:00\n",
      "2019-06-12 16:17:42.559559: Running BA with params: {'n_estimators': 100} on train/test set 2012-01-01 00:00:00\n",
      "2019-06-12 16:18:01.482095: Running AB with params: {'n_estimators': 10, 'algorithm': 'SAMME'} on train/test set 2012-01-01 00:00:00\n",
      "2019-06-12 16:18:01.883409: Running AB with params: {'n_estimators': 100, 'algorithm': 'SAMME'} on train/test set 2012-01-01 00:00:00\n",
      "2019-06-12 16:18:04.181821: Running GB with params: {'learning_rate': 0.001, 'n_estimators': 10} on train/test set 2012-01-01 00:00:00\n",
      "2019-06-12 16:18:04.660807: Running GB with params: {'learning_rate': 0.001, 'n_estimators': 100} on train/test set 2012-01-01 00:00:00\n",
      "2019-06-12 16:18:07.641797: Running GB with params: {'learning_rate': 0.1, 'n_estimators': 10} on train/test set 2012-01-01 00:00:00\n",
      "2019-06-12 16:18:08.125060: Running GB with params: {'learning_rate': 0.1, 'n_estimators': 100} on train/test set 2012-01-01 00:00:00\n",
      "2019-06-12 16:18:11.017434: Running Baseline with params: {} on train/test set 2013-01-01 00:00:00\n",
      "2019-06-12 16:18:11.169905: Running DT with params: {'min_samples_split': 2, 'criterion': 'gini', 'max_depth': 2} on train/test set 2013-01-01 00:00:00\n",
      "2019-06-12 16:18:11.381071: Running DT with params: {'min_samples_split': 2, 'criterion': 'gini', 'max_depth': 50} on train/test set 2013-01-01 00:00:00\n",
      "2019-06-12 16:18:11.823391: Running DT with params: {'min_samples_split': 2, 'criterion': 'gini', 'max_depth': 100} on train/test set 2013-01-01 00:00:00\n",
      "2019-06-12 16:18:12.264929: Running LR with params: {'penalty': 'l1', 'C': 0.1} on train/test set 2013-01-01 00:00:00\n",
      "2019-06-12 16:18:17.186728: Running LR with params: {'penalty': 'l2', 'C': 0.1} on train/test set 2013-01-01 00:00:00\n",
      "2019-06-12 16:18:18.243900: Running LR with params: {'penalty': 'l1', 'C': 10} on train/test set 2013-01-01 00:00:00\n",
      "2019-06-12 16:20:07.811574: Running LR with params: {'penalty': 'l2', 'C': 10} on train/test set 2013-01-01 00:00:00\n",
      "2019-06-12 16:20:08.839624: Running RF with params: {'n_estimators': 100, 'n_jobs': -1, 'max_features': 'sqrt', 'min_samples_split': 2, 'max_depth': 5} on train/test set 2013-01-01 00:00:00\n",
      "2019-06-12 16:20:09.379575: Running RF with params: {'n_estimators': 1000, 'n_jobs': -1, 'max_features': 'sqrt', 'min_samples_split': 2, 'max_depth': 5} on train/test set 2013-01-01 00:00:00\n",
      "2019-06-12 16:20:11.251296: Running RF with params: {'n_estimators': 100, 'n_jobs': -1, 'max_features': 'sqrt', 'min_samples_split': 2, 'max_depth': 50} on train/test set 2013-01-01 00:00:00\n",
      "2019-06-12 16:20:11.917053: Running RF with params: {'n_estimators': 1000, 'n_jobs': -1, 'max_features': 'sqrt', 'min_samples_split': 2, 'max_depth': 50} on train/test set 2013-01-01 00:00:00\n",
      "2019-06-12 16:20:14.748821: Running RF with params: {'n_estimators': 100, 'n_jobs': -1, 'max_features': 'sqrt', 'min_samples_split': 2, 'max_depth': 100} on train/test set 2013-01-01 00:00:00\n",
      "2019-06-12 16:20:15.389807: Running RF with params: {'n_estimators': 1000, 'n_jobs': -1, 'max_features': 'sqrt', 'min_samples_split': 2, 'max_depth': 100} on train/test set 2013-01-01 00:00:00\n",
      "2019-06-12 16:20:18.220206: Running NB with params: {} on train/test set 2013-01-01 00:00:00\n",
      "2019-06-12 16:20:18.404647: Running BA with params: {'n_estimators': 10} on train/test set 2013-01-01 00:00:00\n",
      "2019-06-12 16:20:21.640802: Running BA with params: {'n_estimators': 100} on train/test set 2013-01-01 00:00:00\n",
      "2019-06-12 16:20:49.045214: Running AB with params: {'n_estimators': 10, 'algorithm': 'SAMME'} on train/test set 2013-01-01 00:00:00\n",
      "2019-06-12 16:20:49.452999: Running AB with params: {'n_estimators': 100, 'algorithm': 'SAMME'} on train/test set 2013-01-01 00:00:00\n",
      "2019-06-12 16:20:51.967964: Running GB with params: {'learning_rate': 0.001, 'n_estimators': 10} on train/test set 2013-01-01 00:00:00\n",
      "2019-06-12 16:20:52.475645: Running GB with params: {'learning_rate': 0.001, 'n_estimators': 100} on train/test set 2013-01-01 00:00:00\n",
      "2019-06-12 16:20:55.720607: Running GB with params: {'learning_rate': 0.1, 'n_estimators': 10} on train/test set 2013-01-01 00:00:00\n",
      "2019-06-12 16:20:56.430705: Running GB with params: {'learning_rate': 0.1, 'n_estimators': 100} on train/test set 2013-01-01 00:00:00\n"
     ]
    },
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>model_name</th>\n",
       "      <th>model</th>\n",
       "      <th>parameters</th>\n",
       "      <th>test_set_start_date</th>\n",
       "      <th>baseline</th>\n",
       "      <th>p_at_1</th>\n",
       "      <th>r_at_1</th>\n",
       "      <th>f1_at_1</th>\n",
       "      <th>p_at_2</th>\n",
       "      <th>r_at_2</th>\n",
       "      <th>...</th>\n",
       "      <th>p_at_20</th>\n",
       "      <th>r_at_20</th>\n",
       "      <th>f1_at_20</th>\n",
       "      <th>p_at_30</th>\n",
       "      <th>r_at_30</th>\n",
       "      <th>f1_at_30</th>\n",
       "      <th>p_at_50</th>\n",
       "      <th>r_at_50</th>\n",
       "      <th>f1_at_50</th>\n",
       "      <th>auc-roc</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Baseline</td>\n",
       "      <td>Baseline model</td>\n",
       "      <td>{}</td>\n",
       "      <td>2005-01-01</td>\n",
       "      <td>0.140202</td>\n",
       "      <td>0.944444</td>\n",
       "      <td>0.064394</td>\n",
       "      <td>0.120567</td>\n",
       "      <td>0.918919</td>\n",
       "      <td>0.128788</td>\n",
       "      <td>...</td>\n",
       "      <td>0.409574</td>\n",
       "      <td>0.583333</td>\n",
       "      <td>0.481250</td>\n",
       "      <td>0.281915</td>\n",
       "      <td>0.602273</td>\n",
       "      <td>0.384058</td>\n",
       "      <td>0.182784</td>\n",
       "      <td>0.651515</td>\n",
       "      <td>0.285477</td>\n",
       "      <td>0.778655</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>DT</td>\n",
       "      <td>DecisionTreeClassifier(class_weight=None, crit...</td>\n",
       "      <td>{'min_samples_split': 2, 'criterion': 'gini', ...</td>\n",
       "      <td>2005-01-01</td>\n",
       "      <td>0.140202</td>\n",
       "      <td>0.722222</td>\n",
       "      <td>0.049242</td>\n",
       "      <td>0.092199</td>\n",
       "      <td>0.675676</td>\n",
       "      <td>0.094697</td>\n",
       "      <td>...</td>\n",
       "      <td>0.531915</td>\n",
       "      <td>0.757576</td>\n",
       "      <td>0.625000</td>\n",
       "      <td>0.381206</td>\n",
       "      <td>0.814394</td>\n",
       "      <td>0.519324</td>\n",
       "      <td>0.244421</td>\n",
       "      <td>0.871212</td>\n",
       "      <td>0.381743</td>\n",
       "      <td>0.870593</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>DT</td>\n",
       "      <td>DecisionTreeClassifier(class_weight=None, crit...</td>\n",
       "      <td>{'min_samples_split': 2, 'criterion': 'gini', ...</td>\n",
       "      <td>2005-01-01</td>\n",
       "      <td>0.140202</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.135135</td>\n",
       "      <td>0.018939</td>\n",
       "      <td>...</td>\n",
       "      <td>0.202128</td>\n",
       "      <td>0.287879</td>\n",
       "      <td>0.237500</td>\n",
       "      <td>0.253546</td>\n",
       "      <td>0.541667</td>\n",
       "      <td>0.345411</td>\n",
       "      <td>0.221041</td>\n",
       "      <td>0.787879</td>\n",
       "      <td>0.345228</td>\n",
       "      <td>0.720829</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>DT</td>\n",
       "      <td>DecisionTreeClassifier(class_weight=None, crit...</td>\n",
       "      <td>{'min_samples_split': 2, 'criterion': 'gini', ...</td>\n",
       "      <td>2005-01-01</td>\n",
       "      <td>0.140202</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.135135</td>\n",
       "      <td>0.018939</td>\n",
       "      <td>...</td>\n",
       "      <td>0.202128</td>\n",
       "      <td>0.287879</td>\n",
       "      <td>0.237500</td>\n",
       "      <td>0.253546</td>\n",
       "      <td>0.541667</td>\n",
       "      <td>0.345411</td>\n",
       "      <td>0.221041</td>\n",
       "      <td>0.787879</td>\n",
       "      <td>0.345228</td>\n",
       "      <td>0.720829</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>LR</td>\n",
       "      <td>LogisticRegression(C=10, class_weight=None, du...</td>\n",
       "      <td>{'penalty': 'l1', 'C': 0.1}</td>\n",
       "      <td>2005-01-01</td>\n",
       "      <td>0.140202</td>\n",
       "      <td>0.777778</td>\n",
       "      <td>0.053030</td>\n",
       "      <td>0.099291</td>\n",
       "      <td>0.513514</td>\n",
       "      <td>0.071970</td>\n",
       "      <td>...</td>\n",
       "      <td>0.523936</td>\n",
       "      <td>0.746212</td>\n",
       "      <td>0.615625</td>\n",
       "      <td>0.377660</td>\n",
       "      <td>0.806818</td>\n",
       "      <td>0.514493</td>\n",
       "      <td>0.249734</td>\n",
       "      <td>0.890152</td>\n",
       "      <td>0.390041</td>\n",
       "      <td>0.868135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>LR</td>\n",
       "      <td>LogisticRegression(C=10, class_weight=None, du...</td>\n",
       "      <td>{'penalty': 'l2', 'C': 0.1}</td>\n",
       "      <td>2005-01-01</td>\n",
       "      <td>0.140202</td>\n",
       "      <td>0.777778</td>\n",
       "      <td>0.053030</td>\n",
       "      <td>0.099291</td>\n",
       "      <td>0.540541</td>\n",
       "      <td>0.075758</td>\n",
       "      <td>...</td>\n",
       "      <td>0.449468</td>\n",
       "      <td>0.640152</td>\n",
       "      <td>0.528125</td>\n",
       "      <td>0.335106</td>\n",
       "      <td>0.715909</td>\n",
       "      <td>0.456522</td>\n",
       "      <td>0.226355</td>\n",
       "      <td>0.806818</td>\n",
       "      <td>0.353527</td>\n",
       "      <td>0.805510</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>LR</td>\n",
       "      <td>LogisticRegression(C=10, class_weight=None, du...</td>\n",
       "      <td>{'penalty': 'l1', 'C': 10}</td>\n",
       "      <td>2005-01-01</td>\n",
       "      <td>0.140202</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>0.056818</td>\n",
       "      <td>0.106383</td>\n",
       "      <td>0.864865</td>\n",
       "      <td>0.121212</td>\n",
       "      <td>...</td>\n",
       "      <td>0.486702</td>\n",
       "      <td>0.693182</td>\n",
       "      <td>0.571875</td>\n",
       "      <td>0.358156</td>\n",
       "      <td>0.765152</td>\n",
       "      <td>0.487923</td>\n",
       "      <td>0.246546</td>\n",
       "      <td>0.878788</td>\n",
       "      <td>0.385062</td>\n",
       "      <td>0.833607</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>LR</td>\n",
       "      <td>LogisticRegression(C=10, class_weight=None, du...</td>\n",
       "      <td>{'penalty': 'l2', 'C': 10}</td>\n",
       "      <td>2005-01-01</td>\n",
       "      <td>0.140202</td>\n",
       "      <td>0.944444</td>\n",
       "      <td>0.064394</td>\n",
       "      <td>0.120567</td>\n",
       "      <td>0.918919</td>\n",
       "      <td>0.128788</td>\n",
       "      <td>...</td>\n",
       "      <td>0.470745</td>\n",
       "      <td>0.670455</td>\n",
       "      <td>0.553125</td>\n",
       "      <td>0.338652</td>\n",
       "      <td>0.723485</td>\n",
       "      <td>0.461353</td>\n",
       "      <td>0.228480</td>\n",
       "      <td>0.814394</td>\n",
       "      <td>0.356846</td>\n",
       "      <td>0.826228</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>RF</td>\n",
       "      <td>(DecisionTreeClassifier(class_weight=None, cri...</td>\n",
       "      <td>{'n_estimators': 100, 'n_jobs': -1, 'max_featu...</td>\n",
       "      <td>2005-01-01</td>\n",
       "      <td>0.140202</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.068182</td>\n",
       "      <td>0.127660</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.140152</td>\n",
       "      <td>...</td>\n",
       "      <td>0.537234</td>\n",
       "      <td>0.765152</td>\n",
       "      <td>0.631250</td>\n",
       "      <td>0.395390</td>\n",
       "      <td>0.844697</td>\n",
       "      <td>0.538647</td>\n",
       "      <td>0.274176</td>\n",
       "      <td>0.977273</td>\n",
       "      <td>0.428216</td>\n",
       "      <td>0.922165</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>RF</td>\n",
       "      <td>(DecisionTreeClassifier(class_weight=None, cri...</td>\n",
       "      <td>{'n_estimators': 1000, 'n_jobs': -1, 'max_feat...</td>\n",
       "      <td>2005-01-01</td>\n",
       "      <td>0.140202</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.068182</td>\n",
       "      <td>0.127660</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.140152</td>\n",
       "      <td>...</td>\n",
       "      <td>0.545213</td>\n",
       "      <td>0.776515</td>\n",
       "      <td>0.640625</td>\n",
       "      <td>0.393617</td>\n",
       "      <td>0.840909</td>\n",
       "      <td>0.536232</td>\n",
       "      <td>0.272051</td>\n",
       "      <td>0.969697</td>\n",
       "      <td>0.424896</td>\n",
       "      <td>0.923473</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>RF</td>\n",
       "      <td>(DecisionTreeClassifier(class_weight=None, cri...</td>\n",
       "      <td>{'n_estimators': 100, 'n_jobs': -1, 'max_featu...</td>\n",
       "      <td>2005-01-01</td>\n",
       "      <td>0.140202</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.068182</td>\n",
       "      <td>0.127660</td>\n",
       "      <td>0.972973</td>\n",
       "      <td>0.136364</td>\n",
       "      <td>...</td>\n",
       "      <td>0.537234</td>\n",
       "      <td>0.765152</td>\n",
       "      <td>0.631250</td>\n",
       "      <td>0.404255</td>\n",
       "      <td>0.863636</td>\n",
       "      <td>0.550725</td>\n",
       "      <td>0.269926</td>\n",
       "      <td>0.962121</td>\n",
       "      <td>0.421577</td>\n",
       "      <td>0.924457</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>RF</td>\n",
       "      <td>(DecisionTreeClassifier(class_weight=None, cri...</td>\n",
       "      <td>{'n_estimators': 1000, 'n_jobs': -1, 'max_feat...</td>\n",
       "      <td>2005-01-01</td>\n",
       "      <td>0.140202</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.068182</td>\n",
       "      <td>0.127660</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.140152</td>\n",
       "      <td>...</td>\n",
       "      <td>0.550532</td>\n",
       "      <td>0.784091</td>\n",
       "      <td>0.646875</td>\n",
       "      <td>0.400709</td>\n",
       "      <td>0.856061</td>\n",
       "      <td>0.545894</td>\n",
       "      <td>0.272051</td>\n",
       "      <td>0.969697</td>\n",
       "      <td>0.424896</td>\n",
       "      <td>0.928565</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>RF</td>\n",
       "      <td>(DecisionTreeClassifier(class_weight=None, cri...</td>\n",
       "      <td>{'n_estimators': 100, 'n_jobs': -1, 'max_featu...</td>\n",
       "      <td>2005-01-01</td>\n",
       "      <td>0.140202</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.068182</td>\n",
       "      <td>0.127660</td>\n",
       "      <td>0.972973</td>\n",
       "      <td>0.136364</td>\n",
       "      <td>...</td>\n",
       "      <td>0.537234</td>\n",
       "      <td>0.765152</td>\n",
       "      <td>0.631250</td>\n",
       "      <td>0.404255</td>\n",
       "      <td>0.863636</td>\n",
       "      <td>0.550725</td>\n",
       "      <td>0.269926</td>\n",
       "      <td>0.962121</td>\n",
       "      <td>0.421577</td>\n",
       "      <td>0.924457</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>RF</td>\n",
       "      <td>(DecisionTreeClassifier(class_weight=None, cri...</td>\n",
       "      <td>{'n_estimators': 1000, 'n_jobs': -1, 'max_feat...</td>\n",
       "      <td>2005-01-01</td>\n",
       "      <td>0.140202</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.068182</td>\n",
       "      <td>0.127660</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.140152</td>\n",
       "      <td>...</td>\n",
       "      <td>0.550532</td>\n",
       "      <td>0.784091</td>\n",
       "      <td>0.646875</td>\n",
       "      <td>0.400709</td>\n",
       "      <td>0.856061</td>\n",
       "      <td>0.545894</td>\n",
       "      <td>0.272051</td>\n",
       "      <td>0.969697</td>\n",
       "      <td>0.424896</td>\n",
       "      <td>0.928565</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>NB</td>\n",
       "      <td>GaussianNB(priors=None, var_smoothing=1e-09)</td>\n",
       "      <td>{}</td>\n",
       "      <td>2005-01-01</td>\n",
       "      <td>0.140202</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.022727</td>\n",
       "      <td>0.042553</td>\n",
       "      <td>0.513514</td>\n",
       "      <td>0.071970</td>\n",
       "      <td>...</td>\n",
       "      <td>0.476064</td>\n",
       "      <td>0.678030</td>\n",
       "      <td>0.559375</td>\n",
       "      <td>0.351064</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>0.478261</td>\n",
       "      <td>0.224230</td>\n",
       "      <td>0.799242</td>\n",
       "      <td>0.350207</td>\n",
       "      <td>0.806344</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>BA</td>\n",
       "      <td>(KNeighborsClassifier(algorithm='auto', leaf_s...</td>\n",
       "      <td>{'n_estimators': 10}</td>\n",
       "      <td>2005-01-01</td>\n",
       "      <td>0.140202</td>\n",
       "      <td>0.611111</td>\n",
       "      <td>0.041667</td>\n",
       "      <td>0.078014</td>\n",
       "      <td>0.405405</td>\n",
       "      <td>0.056818</td>\n",
       "      <td>...</td>\n",
       "      <td>0.199468</td>\n",
       "      <td>0.284091</td>\n",
       "      <td>0.234375</td>\n",
       "      <td>0.168440</td>\n",
       "      <td>0.359848</td>\n",
       "      <td>0.229469</td>\n",
       "      <td>0.124336</td>\n",
       "      <td>0.443182</td>\n",
       "      <td>0.194191</td>\n",
       "      <td>0.552944</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>BA</td>\n",
       "      <td>(KNeighborsClassifier(algorithm='auto', leaf_s...</td>\n",
       "      <td>{'n_estimators': 100}</td>\n",
       "      <td>2005-01-01</td>\n",
       "      <td>0.140202</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.045455</td>\n",
       "      <td>0.085106</td>\n",
       "      <td>0.405405</td>\n",
       "      <td>0.056818</td>\n",
       "      <td>...</td>\n",
       "      <td>0.199468</td>\n",
       "      <td>0.284091</td>\n",
       "      <td>0.234375</td>\n",
       "      <td>0.154255</td>\n",
       "      <td>0.329545</td>\n",
       "      <td>0.210145</td>\n",
       "      <td>0.132837</td>\n",
       "      <td>0.473485</td>\n",
       "      <td>0.207469</td>\n",
       "      <td>0.547745</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>AB</td>\n",
       "      <td>(DecisionTreeClassifier(class_weight=None, cri...</td>\n",
       "      <td>{'n_estimators': 10, 'algorithm': 'SAMME'}</td>\n",
       "      <td>2005-01-01</td>\n",
       "      <td>0.140202</td>\n",
       "      <td>0.888889</td>\n",
       "      <td>0.060606</td>\n",
       "      <td>0.113475</td>\n",
       "      <td>0.837838</td>\n",
       "      <td>0.117424</td>\n",
       "      <td>...</td>\n",
       "      <td>0.531915</td>\n",
       "      <td>0.757576</td>\n",
       "      <td>0.625000</td>\n",
       "      <td>0.398936</td>\n",
       "      <td>0.852273</td>\n",
       "      <td>0.543478</td>\n",
       "      <td>0.270988</td>\n",
       "      <td>0.965909</td>\n",
       "      <td>0.423237</td>\n",
       "      <td>0.907226</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>AB</td>\n",
       "      <td>(DecisionTreeClassifier(class_weight=None, cri...</td>\n",
       "      <td>{'n_estimators': 100, 'algorithm': 'SAMME'}</td>\n",
       "      <td>2005-01-01</td>\n",
       "      <td>0.140202</td>\n",
       "      <td>0.888889</td>\n",
       "      <td>0.060606</td>\n",
       "      <td>0.113475</td>\n",
       "      <td>0.945946</td>\n",
       "      <td>0.132576</td>\n",
       "      <td>...</td>\n",
       "      <td>0.534574</td>\n",
       "      <td>0.761364</td>\n",
       "      <td>0.628125</td>\n",
       "      <td>0.391844</td>\n",
       "      <td>0.837121</td>\n",
       "      <td>0.533816</td>\n",
       "      <td>0.263549</td>\n",
       "      <td>0.939394</td>\n",
       "      <td>0.411618</td>\n",
       "      <td>0.910180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>GB</td>\n",
       "      <td>([DecisionTreeRegressor(criterion='friedman_ms...</td>\n",
       "      <td>{'learning_rate': 0.001, 'n_estimators': 10}</td>\n",
       "      <td>2005-01-01</td>\n",
       "      <td>0.140202</td>\n",
       "      <td>0.944444</td>\n",
       "      <td>0.064394</td>\n",
       "      <td>0.120567</td>\n",
       "      <td>0.972973</td>\n",
       "      <td>0.136364</td>\n",
       "      <td>...</td>\n",
       "      <td>0.534574</td>\n",
       "      <td>0.761364</td>\n",
       "      <td>0.628125</td>\n",
       "      <td>0.397163</td>\n",
       "      <td>0.848485</td>\n",
       "      <td>0.541063</td>\n",
       "      <td>0.253985</td>\n",
       "      <td>0.905303</td>\n",
       "      <td>0.396680</td>\n",
       "      <td>0.903254</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>GB</td>\n",
       "      <td>([DecisionTreeRegressor(criterion='friedman_ms...</td>\n",
       "      <td>{'learning_rate': 0.001, 'n_estimators': 100}</td>\n",
       "      <td>2005-01-01</td>\n",
       "      <td>0.140202</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.068182</td>\n",
       "      <td>0.127660</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.140152</td>\n",
       "      <td>...</td>\n",
       "      <td>0.537234</td>\n",
       "      <td>0.765152</td>\n",
       "      <td>0.631250</td>\n",
       "      <td>0.393617</td>\n",
       "      <td>0.840909</td>\n",
       "      <td>0.536232</td>\n",
       "      <td>0.251860</td>\n",
       "      <td>0.897727</td>\n",
       "      <td>0.393361</td>\n",
       "      <td>0.902536</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>GB</td>\n",
       "      <td>([DecisionTreeRegressor(criterion='friedman_ms...</td>\n",
       "      <td>{'learning_rate': 0.1, 'n_estimators': 10}</td>\n",
       "      <td>2005-01-01</td>\n",
       "      <td>0.140202</td>\n",
       "      <td>0.944444</td>\n",
       "      <td>0.064394</td>\n",
       "      <td>0.120567</td>\n",
       "      <td>0.945946</td>\n",
       "      <td>0.132576</td>\n",
       "      <td>...</td>\n",
       "      <td>0.539894</td>\n",
       "      <td>0.768939</td>\n",
       "      <td>0.634375</td>\n",
       "      <td>0.390071</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>0.531401</td>\n",
       "      <td>0.255048</td>\n",
       "      <td>0.909091</td>\n",
       "      <td>0.398340</td>\n",
       "      <td>0.903324</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>GB</td>\n",
       "      <td>([DecisionTreeRegressor(criterion='friedman_ms...</td>\n",
       "      <td>{'learning_rate': 0.1, 'n_estimators': 100}</td>\n",
       "      <td>2005-01-01</td>\n",
       "      <td>0.140202</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.045455</td>\n",
       "      <td>0.085106</td>\n",
       "      <td>0.756757</td>\n",
       "      <td>0.106061</td>\n",
       "      <td>...</td>\n",
       "      <td>0.547872</td>\n",
       "      <td>0.780303</td>\n",
       "      <td>0.643750</td>\n",
       "      <td>0.400709</td>\n",
       "      <td>0.856061</td>\n",
       "      <td>0.545894</td>\n",
       "      <td>0.262487</td>\n",
       "      <td>0.935606</td>\n",
       "      <td>0.409959</td>\n",
       "      <td>0.910958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Baseline</td>\n",
       "      <td>Baseline model</td>\n",
       "      <td>{}</td>\n",
       "      <td>2006-01-01</td>\n",
       "      <td>0.111524</td>\n",
       "      <td>0.277778</td>\n",
       "      <td>0.023810</td>\n",
       "      <td>0.043860</td>\n",
       "      <td>0.540541</td>\n",
       "      <td>0.095238</td>\n",
       "      <td>...</td>\n",
       "      <td>0.268617</td>\n",
       "      <td>0.480952</td>\n",
       "      <td>0.344710</td>\n",
       "      <td>0.191489</td>\n",
       "      <td>0.514286</td>\n",
       "      <td>0.279070</td>\n",
       "      <td>0.125399</td>\n",
       "      <td>0.561905</td>\n",
       "      <td>0.205039</td>\n",
       "      <td>0.714176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>DT</td>\n",
       "      <td>DecisionTreeClassifier(class_weight=None, crit...</td>\n",
       "      <td>{'min_samples_split': 2, 'criterion': 'gini', ...</td>\n",
       "      <td>2006-01-01</td>\n",
       "      <td>0.111524</td>\n",
       "      <td>0.777778</td>\n",
       "      <td>0.066667</td>\n",
       "      <td>0.122807</td>\n",
       "      <td>0.837838</td>\n",
       "      <td>0.147619</td>\n",
       "      <td>...</td>\n",
       "      <td>0.396277</td>\n",
       "      <td>0.709524</td>\n",
       "      <td>0.508532</td>\n",
       "      <td>0.265957</td>\n",
       "      <td>0.714286</td>\n",
       "      <td>0.387597</td>\n",
       "      <td>0.163656</td>\n",
       "      <td>0.733333</td>\n",
       "      <td>0.267593</td>\n",
       "      <td>0.805011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>DT</td>\n",
       "      <td>DecisionTreeClassifier(class_weight=None, crit...</td>\n",
       "      <td>{'min_samples_split': 2, 'criterion': 'gini', ...</td>\n",
       "      <td>2006-01-01</td>\n",
       "      <td>0.111524</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.028571</td>\n",
       "      <td>0.052632</td>\n",
       "      <td>0.351351</td>\n",
       "      <td>0.061905</td>\n",
       "      <td>...</td>\n",
       "      <td>0.271277</td>\n",
       "      <td>0.485714</td>\n",
       "      <td>0.348123</td>\n",
       "      <td>0.196809</td>\n",
       "      <td>0.528571</td>\n",
       "      <td>0.286822</td>\n",
       "      <td>0.128587</td>\n",
       "      <td>0.576190</td>\n",
       "      <td>0.210252</td>\n",
       "      <td>0.661267</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>DT</td>\n",
       "      <td>DecisionTreeClassifier(class_weight=None, crit...</td>\n",
       "      <td>{'min_samples_split': 2, 'criterion': 'gini', ...</td>\n",
       "      <td>2006-01-01</td>\n",
       "      <td>0.111524</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.028571</td>\n",
       "      <td>0.052632</td>\n",
       "      <td>0.351351</td>\n",
       "      <td>0.061905</td>\n",
       "      <td>...</td>\n",
       "      <td>0.271277</td>\n",
       "      <td>0.485714</td>\n",
       "      <td>0.348123</td>\n",
       "      <td>0.196809</td>\n",
       "      <td>0.528571</td>\n",
       "      <td>0.286822</td>\n",
       "      <td>0.128587</td>\n",
       "      <td>0.576190</td>\n",
       "      <td>0.210252</td>\n",
       "      <td>0.661267</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>LR</td>\n",
       "      <td>LogisticRegression(C=10, class_weight=None, du...</td>\n",
       "      <td>{'penalty': 'l1', 'C': 0.1}</td>\n",
       "      <td>2006-01-01</td>\n",
       "      <td>0.111524</td>\n",
       "      <td>0.555556</td>\n",
       "      <td>0.047619</td>\n",
       "      <td>0.087719</td>\n",
       "      <td>0.567568</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.369681</td>\n",
       "      <td>0.661905</td>\n",
       "      <td>0.474403</td>\n",
       "      <td>0.290780</td>\n",
       "      <td>0.780952</td>\n",
       "      <td>0.423773</td>\n",
       "      <td>0.200850</td>\n",
       "      <td>0.900000</td>\n",
       "      <td>0.328410</td>\n",
       "      <td>0.850821</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>LR</td>\n",
       "      <td>LogisticRegression(C=10, class_weight=None, du...</td>\n",
       "      <td>{'penalty': 'l2', 'C': 0.1}</td>\n",
       "      <td>2006-01-01</td>\n",
       "      <td>0.111524</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.042857</td>\n",
       "      <td>0.078947</td>\n",
       "      <td>0.513514</td>\n",
       "      <td>0.090476</td>\n",
       "      <td>...</td>\n",
       "      <td>0.303191</td>\n",
       "      <td>0.542857</td>\n",
       "      <td>0.389078</td>\n",
       "      <td>0.235816</td>\n",
       "      <td>0.633333</td>\n",
       "      <td>0.343669</td>\n",
       "      <td>0.161530</td>\n",
       "      <td>0.723810</td>\n",
       "      <td>0.264118</td>\n",
       "      <td>0.749668</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>LR</td>\n",
       "      <td>LogisticRegression(C=10, class_weight=None, du...</td>\n",
       "      <td>{'penalty': 'l1', 'C': 10}</td>\n",
       "      <td>2006-01-01</td>\n",
       "      <td>0.111524</td>\n",
       "      <td>0.888889</td>\n",
       "      <td>0.076190</td>\n",
       "      <td>0.140351</td>\n",
       "      <td>0.675676</td>\n",
       "      <td>0.119048</td>\n",
       "      <td>...</td>\n",
       "      <td>0.382979</td>\n",
       "      <td>0.685714</td>\n",
       "      <td>0.491468</td>\n",
       "      <td>0.303191</td>\n",
       "      <td>0.814286</td>\n",
       "      <td>0.441860</td>\n",
       "      <td>0.208289</td>\n",
       "      <td>0.933333</td>\n",
       "      <td>0.340573</td>\n",
       "      <td>0.860536</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>177</th>\n",
       "      <td>BA</td>\n",
       "      <td>(KNeighborsClassifier(algorithm='auto', leaf_s...</td>\n",
       "      <td>{'n_estimators': 100}</td>\n",
       "      <td>2012-01-01</td>\n",
       "      <td>0.149533</td>\n",
       "      <td>0.764706</td>\n",
       "      <td>0.050781</td>\n",
       "      <td>0.095238</td>\n",
       "      <td>0.705882</td>\n",
       "      <td>0.093750</td>\n",
       "      <td>...</td>\n",
       "      <td>0.321637</td>\n",
       "      <td>0.429688</td>\n",
       "      <td>0.367893</td>\n",
       "      <td>0.274854</td>\n",
       "      <td>0.550781</td>\n",
       "      <td>0.366710</td>\n",
       "      <td>0.192757</td>\n",
       "      <td>0.644531</td>\n",
       "      <td>0.296763</td>\n",
       "      <td>0.674648</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>178</th>\n",
       "      <td>AB</td>\n",
       "      <td>(DecisionTreeClassifier(class_weight=None, cri...</td>\n",
       "      <td>{'n_estimators': 10, 'algorithm': 'SAMME'}</td>\n",
       "      <td>2012-01-01</td>\n",
       "      <td>0.149533</td>\n",
       "      <td>0.823529</td>\n",
       "      <td>0.054688</td>\n",
       "      <td>0.102564</td>\n",
       "      <td>0.882353</td>\n",
       "      <td>0.117188</td>\n",
       "      <td>...</td>\n",
       "      <td>0.584795</td>\n",
       "      <td>0.781250</td>\n",
       "      <td>0.668896</td>\n",
       "      <td>0.458090</td>\n",
       "      <td>0.917969</td>\n",
       "      <td>0.611183</td>\n",
       "      <td>0.294393</td>\n",
       "      <td>0.984375</td>\n",
       "      <td>0.453237</td>\n",
       "      <td>0.939878</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>179</th>\n",
       "      <td>AB</td>\n",
       "      <td>(DecisionTreeClassifier(class_weight=None, cri...</td>\n",
       "      <td>{'n_estimators': 100, 'algorithm': 'SAMME'}</td>\n",
       "      <td>2012-01-01</td>\n",
       "      <td>0.149533</td>\n",
       "      <td>0.882353</td>\n",
       "      <td>0.058594</td>\n",
       "      <td>0.109890</td>\n",
       "      <td>0.911765</td>\n",
       "      <td>0.121094</td>\n",
       "      <td>...</td>\n",
       "      <td>0.628655</td>\n",
       "      <td>0.839844</td>\n",
       "      <td>0.719064</td>\n",
       "      <td>0.460039</td>\n",
       "      <td>0.921875</td>\n",
       "      <td>0.613784</td>\n",
       "      <td>0.293224</td>\n",
       "      <td>0.980469</td>\n",
       "      <td>0.451439</td>\n",
       "      <td>0.946112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>180</th>\n",
       "      <td>GB</td>\n",
       "      <td>([DecisionTreeRegressor(criterion='friedman_ms...</td>\n",
       "      <td>{'learning_rate': 0.001, 'n_estimators': 10}</td>\n",
       "      <td>2012-01-01</td>\n",
       "      <td>0.149533</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.066406</td>\n",
       "      <td>0.124542</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.132812</td>\n",
       "      <td>...</td>\n",
       "      <td>0.605263</td>\n",
       "      <td>0.808594</td>\n",
       "      <td>0.692308</td>\n",
       "      <td>0.444444</td>\n",
       "      <td>0.890625</td>\n",
       "      <td>0.592978</td>\n",
       "      <td>0.288551</td>\n",
       "      <td>0.964844</td>\n",
       "      <td>0.444245</td>\n",
       "      <td>0.933678</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>181</th>\n",
       "      <td>GB</td>\n",
       "      <td>([DecisionTreeRegressor(criterion='friedman_ms...</td>\n",
       "      <td>{'learning_rate': 0.001, 'n_estimators': 100}</td>\n",
       "      <td>2012-01-01</td>\n",
       "      <td>0.149533</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.066406</td>\n",
       "      <td>0.124542</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.132812</td>\n",
       "      <td>...</td>\n",
       "      <td>0.619883</td>\n",
       "      <td>0.828125</td>\n",
       "      <td>0.709030</td>\n",
       "      <td>0.456140</td>\n",
       "      <td>0.914062</td>\n",
       "      <td>0.608583</td>\n",
       "      <td>0.294393</td>\n",
       "      <td>0.984375</td>\n",
       "      <td>0.453237</td>\n",
       "      <td>0.942100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>182</th>\n",
       "      <td>GB</td>\n",
       "      <td>([DecisionTreeRegressor(criterion='friedman_ms...</td>\n",
       "      <td>{'learning_rate': 0.1, 'n_estimators': 10}</td>\n",
       "      <td>2012-01-01</td>\n",
       "      <td>0.149533</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.066406</td>\n",
       "      <td>0.124542</td>\n",
       "      <td>0.970588</td>\n",
       "      <td>0.128906</td>\n",
       "      <td>...</td>\n",
       "      <td>0.619883</td>\n",
       "      <td>0.828125</td>\n",
       "      <td>0.709030</td>\n",
       "      <td>0.450292</td>\n",
       "      <td>0.902344</td>\n",
       "      <td>0.600780</td>\n",
       "      <td>0.294393</td>\n",
       "      <td>0.984375</td>\n",
       "      <td>0.453237</td>\n",
       "      <td>0.942858</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>183</th>\n",
       "      <td>GB</td>\n",
       "      <td>([DecisionTreeRegressor(criterion='friedman_ms...</td>\n",
       "      <td>{'learning_rate': 0.1, 'n_estimators': 100}</td>\n",
       "      <td>2012-01-01</td>\n",
       "      <td>0.149533</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.066406</td>\n",
       "      <td>0.124542</td>\n",
       "      <td>0.970588</td>\n",
       "      <td>0.128906</td>\n",
       "      <td>...</td>\n",
       "      <td>0.625731</td>\n",
       "      <td>0.835938</td>\n",
       "      <td>0.715719</td>\n",
       "      <td>0.460039</td>\n",
       "      <td>0.921875</td>\n",
       "      <td>0.613784</td>\n",
       "      <td>0.294393</td>\n",
       "      <td>0.984375</td>\n",
       "      <td>0.453237</td>\n",
       "      <td>0.947593</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>184</th>\n",
       "      <td>Baseline</td>\n",
       "      <td>Baseline model</td>\n",
       "      <td>{}</td>\n",
       "      <td>2013-01-01</td>\n",
       "      <td>0.146552</td>\n",
       "      <td>0.882353</td>\n",
       "      <td>0.058824</td>\n",
       "      <td>0.110294</td>\n",
       "      <td>0.882353</td>\n",
       "      <td>0.117647</td>\n",
       "      <td>...</td>\n",
       "      <td>0.422414</td>\n",
       "      <td>0.576471</td>\n",
       "      <td>0.487562</td>\n",
       "      <td>0.283525</td>\n",
       "      <td>0.580392</td>\n",
       "      <td>0.380952</td>\n",
       "      <td>0.185057</td>\n",
       "      <td>0.631373</td>\n",
       "      <td>0.286222</td>\n",
       "      <td>0.777520</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>185</th>\n",
       "      <td>DT</td>\n",
       "      <td>DecisionTreeClassifier(class_weight=None, crit...</td>\n",
       "      <td>{'min_samples_split': 2, 'criterion': 'gini', ...</td>\n",
       "      <td>2013-01-01</td>\n",
       "      <td>0.146552</td>\n",
       "      <td>0.882353</td>\n",
       "      <td>0.058824</td>\n",
       "      <td>0.110294</td>\n",
       "      <td>0.941176</td>\n",
       "      <td>0.125490</td>\n",
       "      <td>...</td>\n",
       "      <td>0.609195</td>\n",
       "      <td>0.831373</td>\n",
       "      <td>0.703151</td>\n",
       "      <td>0.429119</td>\n",
       "      <td>0.878431</td>\n",
       "      <td>0.576577</td>\n",
       "      <td>0.262069</td>\n",
       "      <td>0.894118</td>\n",
       "      <td>0.405333</td>\n",
       "      <td>0.912128</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>186</th>\n",
       "      <td>DT</td>\n",
       "      <td>DecisionTreeClassifier(class_weight=None, crit...</td>\n",
       "      <td>{'min_samples_split': 2, 'criterion': 'gini', ...</td>\n",
       "      <td>2013-01-01</td>\n",
       "      <td>0.146552</td>\n",
       "      <td>0.647059</td>\n",
       "      <td>0.043137</td>\n",
       "      <td>0.080882</td>\n",
       "      <td>0.764706</td>\n",
       "      <td>0.101961</td>\n",
       "      <td>...</td>\n",
       "      <td>0.327586</td>\n",
       "      <td>0.447059</td>\n",
       "      <td>0.378109</td>\n",
       "      <td>0.218391</td>\n",
       "      <td>0.447059</td>\n",
       "      <td>0.293436</td>\n",
       "      <td>0.151724</td>\n",
       "      <td>0.517647</td>\n",
       "      <td>0.234667</td>\n",
       "      <td>0.688909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>187</th>\n",
       "      <td>DT</td>\n",
       "      <td>DecisionTreeClassifier(class_weight=None, crit...</td>\n",
       "      <td>{'min_samples_split': 2, 'criterion': 'gini', ...</td>\n",
       "      <td>2013-01-01</td>\n",
       "      <td>0.146552</td>\n",
       "      <td>0.647059</td>\n",
       "      <td>0.043137</td>\n",
       "      <td>0.080882</td>\n",
       "      <td>0.764706</td>\n",
       "      <td>0.101961</td>\n",
       "      <td>...</td>\n",
       "      <td>0.327586</td>\n",
       "      <td>0.447059</td>\n",
       "      <td>0.378109</td>\n",
       "      <td>0.218391</td>\n",
       "      <td>0.447059</td>\n",
       "      <td>0.293436</td>\n",
       "      <td>0.151724</td>\n",
       "      <td>0.517647</td>\n",
       "      <td>0.234667</td>\n",
       "      <td>0.688909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>188</th>\n",
       "      <td>LR</td>\n",
       "      <td>LogisticRegression(C=10, class_weight=None, du...</td>\n",
       "      <td>{'penalty': 'l1', 'C': 0.1}</td>\n",
       "      <td>2013-01-01</td>\n",
       "      <td>0.146552</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.066667</td>\n",
       "      <td>0.125000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.133333</td>\n",
       "      <td>...</td>\n",
       "      <td>0.637931</td>\n",
       "      <td>0.870588</td>\n",
       "      <td>0.736318</td>\n",
       "      <td>0.467433</td>\n",
       "      <td>0.956863</td>\n",
       "      <td>0.628057</td>\n",
       "      <td>0.291954</td>\n",
       "      <td>0.996078</td>\n",
       "      <td>0.451556</td>\n",
       "      <td>0.964097</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>189</th>\n",
       "      <td>LR</td>\n",
       "      <td>LogisticRegression(C=10, class_weight=None, du...</td>\n",
       "      <td>{'penalty': 'l2', 'C': 0.1}</td>\n",
       "      <td>2013-01-01</td>\n",
       "      <td>0.146552</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.066667</td>\n",
       "      <td>0.125000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.133333</td>\n",
       "      <td>...</td>\n",
       "      <td>0.626437</td>\n",
       "      <td>0.854902</td>\n",
       "      <td>0.723051</td>\n",
       "      <td>0.457854</td>\n",
       "      <td>0.937255</td>\n",
       "      <td>0.615187</td>\n",
       "      <td>0.290805</td>\n",
       "      <td>0.992157</td>\n",
       "      <td>0.449778</td>\n",
       "      <td>0.960133</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>190</th>\n",
       "      <td>LR</td>\n",
       "      <td>LogisticRegression(C=10, class_weight=None, du...</td>\n",
       "      <td>{'penalty': 'l1', 'C': 10}</td>\n",
       "      <td>2013-01-01</td>\n",
       "      <td>0.146552</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.066667</td>\n",
       "      <td>0.125000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.133333</td>\n",
       "      <td>...</td>\n",
       "      <td>0.629310</td>\n",
       "      <td>0.858824</td>\n",
       "      <td>0.726368</td>\n",
       "      <td>0.469349</td>\n",
       "      <td>0.960784</td>\n",
       "      <td>0.630631</td>\n",
       "      <td>0.290805</td>\n",
       "      <td>0.992157</td>\n",
       "      <td>0.449778</td>\n",
       "      <td>0.959769</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>191</th>\n",
       "      <td>LR</td>\n",
       "      <td>LogisticRegression(C=10, class_weight=None, du...</td>\n",
       "      <td>{'penalty': 'l2', 'C': 10}</td>\n",
       "      <td>2013-01-01</td>\n",
       "      <td>0.146552</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.066667</td>\n",
       "      <td>0.125000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.133333</td>\n",
       "      <td>...</td>\n",
       "      <td>0.629310</td>\n",
       "      <td>0.858824</td>\n",
       "      <td>0.726368</td>\n",
       "      <td>0.455939</td>\n",
       "      <td>0.933333</td>\n",
       "      <td>0.612613</td>\n",
       "      <td>0.290805</td>\n",
       "      <td>0.992157</td>\n",
       "      <td>0.449778</td>\n",
       "      <td>0.958570</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>192</th>\n",
       "      <td>RF</td>\n",
       "      <td>(DecisionTreeClassifier(class_weight=None, cri...</td>\n",
       "      <td>{'n_estimators': 100, 'n_jobs': -1, 'max_featu...</td>\n",
       "      <td>2013-01-01</td>\n",
       "      <td>0.146552</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.066667</td>\n",
       "      <td>0.125000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.133333</td>\n",
       "      <td>...</td>\n",
       "      <td>0.617816</td>\n",
       "      <td>0.843137</td>\n",
       "      <td>0.713101</td>\n",
       "      <td>0.459770</td>\n",
       "      <td>0.941176</td>\n",
       "      <td>0.617761</td>\n",
       "      <td>0.289655</td>\n",
       "      <td>0.988235</td>\n",
       "      <td>0.448000</td>\n",
       "      <td>0.956971</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>193</th>\n",
       "      <td>RF</td>\n",
       "      <td>(DecisionTreeClassifier(class_weight=None, cri...</td>\n",
       "      <td>{'n_estimators': 1000, 'n_jobs': -1, 'max_feat...</td>\n",
       "      <td>2013-01-01</td>\n",
       "      <td>0.146552</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.066667</td>\n",
       "      <td>0.125000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.133333</td>\n",
       "      <td>...</td>\n",
       "      <td>0.614943</td>\n",
       "      <td>0.839216</td>\n",
       "      <td>0.709784</td>\n",
       "      <td>0.459770</td>\n",
       "      <td>0.941176</td>\n",
       "      <td>0.617761</td>\n",
       "      <td>0.288506</td>\n",
       "      <td>0.984314</td>\n",
       "      <td>0.446222</td>\n",
       "      <td>0.957036</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194</th>\n",
       "      <td>RF</td>\n",
       "      <td>(DecisionTreeClassifier(class_weight=None, cri...</td>\n",
       "      <td>{'n_estimators': 100, 'n_jobs': -1, 'max_featu...</td>\n",
       "      <td>2013-01-01</td>\n",
       "      <td>0.146552</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.066667</td>\n",
       "      <td>0.125000</td>\n",
       "      <td>0.970588</td>\n",
       "      <td>0.129412</td>\n",
       "      <td>...</td>\n",
       "      <td>0.623563</td>\n",
       "      <td>0.850980</td>\n",
       "      <td>0.719735</td>\n",
       "      <td>0.459770</td>\n",
       "      <td>0.941176</td>\n",
       "      <td>0.617761</td>\n",
       "      <td>0.290805</td>\n",
       "      <td>0.992157</td>\n",
       "      <td>0.449778</td>\n",
       "      <td>0.955854</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>195</th>\n",
       "      <td>RF</td>\n",
       "      <td>(DecisionTreeClassifier(class_weight=None, cri...</td>\n",
       "      <td>{'n_estimators': 1000, 'n_jobs': -1, 'max_feat...</td>\n",
       "      <td>2013-01-01</td>\n",
       "      <td>0.146552</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.066667</td>\n",
       "      <td>0.125000</td>\n",
       "      <td>0.941176</td>\n",
       "      <td>0.125490</td>\n",
       "      <td>...</td>\n",
       "      <td>0.635057</td>\n",
       "      <td>0.866667</td>\n",
       "      <td>0.733002</td>\n",
       "      <td>0.459770</td>\n",
       "      <td>0.941176</td>\n",
       "      <td>0.617761</td>\n",
       "      <td>0.290805</td>\n",
       "      <td>0.992157</td>\n",
       "      <td>0.449778</td>\n",
       "      <td>0.959055</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>196</th>\n",
       "      <td>RF</td>\n",
       "      <td>(DecisionTreeClassifier(class_weight=None, cri...</td>\n",
       "      <td>{'n_estimators': 100, 'n_jobs': -1, 'max_featu...</td>\n",
       "      <td>2013-01-01</td>\n",
       "      <td>0.146552</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.066667</td>\n",
       "      <td>0.125000</td>\n",
       "      <td>0.970588</td>\n",
       "      <td>0.129412</td>\n",
       "      <td>...</td>\n",
       "      <td>0.623563</td>\n",
       "      <td>0.850980</td>\n",
       "      <td>0.719735</td>\n",
       "      <td>0.459770</td>\n",
       "      <td>0.941176</td>\n",
       "      <td>0.617761</td>\n",
       "      <td>0.290805</td>\n",
       "      <td>0.992157</td>\n",
       "      <td>0.449778</td>\n",
       "      <td>0.955854</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>197</th>\n",
       "      <td>RF</td>\n",
       "      <td>(DecisionTreeClassifier(class_weight=None, cri...</td>\n",
       "      <td>{'n_estimators': 1000, 'n_jobs': -1, 'max_feat...</td>\n",
       "      <td>2013-01-01</td>\n",
       "      <td>0.146552</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.066667</td>\n",
       "      <td>0.125000</td>\n",
       "      <td>0.941176</td>\n",
       "      <td>0.125490</td>\n",
       "      <td>...</td>\n",
       "      <td>0.635057</td>\n",
       "      <td>0.866667</td>\n",
       "      <td>0.733002</td>\n",
       "      <td>0.459770</td>\n",
       "      <td>0.941176</td>\n",
       "      <td>0.617761</td>\n",
       "      <td>0.290805</td>\n",
       "      <td>0.992157</td>\n",
       "      <td>0.449778</td>\n",
       "      <td>0.959055</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>198</th>\n",
       "      <td>NB</td>\n",
       "      <td>GaussianNB(priors=None, var_smoothing=1e-09)</td>\n",
       "      <td>{}</td>\n",
       "      <td>2013-01-01</td>\n",
       "      <td>0.146552</td>\n",
       "      <td>0.941176</td>\n",
       "      <td>0.062745</td>\n",
       "      <td>0.117647</td>\n",
       "      <td>0.911765</td>\n",
       "      <td>0.121569</td>\n",
       "      <td>...</td>\n",
       "      <td>0.534483</td>\n",
       "      <td>0.729412</td>\n",
       "      <td>0.616915</td>\n",
       "      <td>0.409962</td>\n",
       "      <td>0.839216</td>\n",
       "      <td>0.550837</td>\n",
       "      <td>0.272414</td>\n",
       "      <td>0.929412</td>\n",
       "      <td>0.421333</td>\n",
       "      <td>0.903078</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>199</th>\n",
       "      <td>BA</td>\n",
       "      <td>(KNeighborsClassifier(algorithm='auto', leaf_s...</td>\n",
       "      <td>{'n_estimators': 10}</td>\n",
       "      <td>2013-01-01</td>\n",
       "      <td>0.146552</td>\n",
       "      <td>0.823529</td>\n",
       "      <td>0.054902</td>\n",
       "      <td>0.102941</td>\n",
       "      <td>0.705882</td>\n",
       "      <td>0.094118</td>\n",
       "      <td>...</td>\n",
       "      <td>0.327586</td>\n",
       "      <td>0.447059</td>\n",
       "      <td>0.378109</td>\n",
       "      <td>0.277778</td>\n",
       "      <td>0.568627</td>\n",
       "      <td>0.373230</td>\n",
       "      <td>0.179310</td>\n",
       "      <td>0.611765</td>\n",
       "      <td>0.277333</td>\n",
       "      <td>0.678798</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>200</th>\n",
       "      <td>BA</td>\n",
       "      <td>(KNeighborsClassifier(algorithm='auto', leaf_s...</td>\n",
       "      <td>{'n_estimators': 100}</td>\n",
       "      <td>2013-01-01</td>\n",
       "      <td>0.146552</td>\n",
       "      <td>0.705882</td>\n",
       "      <td>0.047059</td>\n",
       "      <td>0.088235</td>\n",
       "      <td>0.705882</td>\n",
       "      <td>0.094118</td>\n",
       "      <td>...</td>\n",
       "      <td>0.327586</td>\n",
       "      <td>0.447059</td>\n",
       "      <td>0.378109</td>\n",
       "      <td>0.289272</td>\n",
       "      <td>0.592157</td>\n",
       "      <td>0.388674</td>\n",
       "      <td>0.195402</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.302222</td>\n",
       "      <td>0.699959</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>201</th>\n",
       "      <td>AB</td>\n",
       "      <td>(DecisionTreeClassifier(class_weight=None, cri...</td>\n",
       "      <td>{'n_estimators': 10, 'algorithm': 'SAMME'}</td>\n",
       "      <td>2013-01-01</td>\n",
       "      <td>0.146552</td>\n",
       "      <td>0.882353</td>\n",
       "      <td>0.058824</td>\n",
       "      <td>0.110294</td>\n",
       "      <td>0.882353</td>\n",
       "      <td>0.117647</td>\n",
       "      <td>...</td>\n",
       "      <td>0.571839</td>\n",
       "      <td>0.780392</td>\n",
       "      <td>0.660033</td>\n",
       "      <td>0.452107</td>\n",
       "      <td>0.925490</td>\n",
       "      <td>0.607465</td>\n",
       "      <td>0.288506</td>\n",
       "      <td>0.984314</td>\n",
       "      <td>0.446222</td>\n",
       "      <td>0.942167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>202</th>\n",
       "      <td>AB</td>\n",
       "      <td>(DecisionTreeClassifier(class_weight=None, cri...</td>\n",
       "      <td>{'n_estimators': 100, 'algorithm': 'SAMME'}</td>\n",
       "      <td>2013-01-01</td>\n",
       "      <td>0.146552</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.066667</td>\n",
       "      <td>0.125000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.133333</td>\n",
       "      <td>...</td>\n",
       "      <td>0.597701</td>\n",
       "      <td>0.815686</td>\n",
       "      <td>0.689884</td>\n",
       "      <td>0.457854</td>\n",
       "      <td>0.937255</td>\n",
       "      <td>0.615187</td>\n",
       "      <td>0.289655</td>\n",
       "      <td>0.988235</td>\n",
       "      <td>0.448000</td>\n",
       "      <td>0.950819</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>203</th>\n",
       "      <td>GB</td>\n",
       "      <td>([DecisionTreeRegressor(criterion='friedman_ms...</td>\n",
       "      <td>{'learning_rate': 0.001, 'n_estimators': 10}</td>\n",
       "      <td>2013-01-01</td>\n",
       "      <td>0.146552</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.066667</td>\n",
       "      <td>0.125000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.133333</td>\n",
       "      <td>...</td>\n",
       "      <td>0.629310</td>\n",
       "      <td>0.858824</td>\n",
       "      <td>0.726368</td>\n",
       "      <td>0.455939</td>\n",
       "      <td>0.933333</td>\n",
       "      <td>0.612613</td>\n",
       "      <td>0.289655</td>\n",
       "      <td>0.988235</td>\n",
       "      <td>0.448000</td>\n",
       "      <td>0.950670</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>204</th>\n",
       "      <td>GB</td>\n",
       "      <td>([DecisionTreeRegressor(criterion='friedman_ms...</td>\n",
       "      <td>{'learning_rate': 0.001, 'n_estimators': 100}</td>\n",
       "      <td>2013-01-01</td>\n",
       "      <td>0.146552</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.066667</td>\n",
       "      <td>0.125000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.133333</td>\n",
       "      <td>...</td>\n",
       "      <td>0.623563</td>\n",
       "      <td>0.850980</td>\n",
       "      <td>0.719735</td>\n",
       "      <td>0.469349</td>\n",
       "      <td>0.960784</td>\n",
       "      <td>0.630631</td>\n",
       "      <td>0.291954</td>\n",
       "      <td>0.996078</td>\n",
       "      <td>0.451556</td>\n",
       "      <td>0.961067</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>205</th>\n",
       "      <td>GB</td>\n",
       "      <td>([DecisionTreeRegressor(criterion='friedman_ms...</td>\n",
       "      <td>{'learning_rate': 0.1, 'n_estimators': 10}</td>\n",
       "      <td>2013-01-01</td>\n",
       "      <td>0.146552</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.066667</td>\n",
       "      <td>0.125000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.133333</td>\n",
       "      <td>...</td>\n",
       "      <td>0.629310</td>\n",
       "      <td>0.858824</td>\n",
       "      <td>0.726368</td>\n",
       "      <td>0.454023</td>\n",
       "      <td>0.929412</td>\n",
       "      <td>0.610039</td>\n",
       "      <td>0.290805</td>\n",
       "      <td>0.992157</td>\n",
       "      <td>0.449778</td>\n",
       "      <td>0.957825</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>206</th>\n",
       "      <td>GB</td>\n",
       "      <td>([DecisionTreeRegressor(criterion='friedman_ms...</td>\n",
       "      <td>{'learning_rate': 0.1, 'n_estimators': 100}</td>\n",
       "      <td>2013-01-01</td>\n",
       "      <td>0.146552</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.066667</td>\n",
       "      <td>0.125000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.133333</td>\n",
       "      <td>...</td>\n",
       "      <td>0.620690</td>\n",
       "      <td>0.847059</td>\n",
       "      <td>0.716418</td>\n",
       "      <td>0.461686</td>\n",
       "      <td>0.945098</td>\n",
       "      <td>0.620335</td>\n",
       "      <td>0.291954</td>\n",
       "      <td>0.996078</td>\n",
       "      <td>0.451556</td>\n",
       "      <td>0.955659</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>207 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    model_name                                              model  \\\n",
       "0     Baseline                                     Baseline model   \n",
       "1           DT  DecisionTreeClassifier(class_weight=None, crit...   \n",
       "2           DT  DecisionTreeClassifier(class_weight=None, crit...   \n",
       "3           DT  DecisionTreeClassifier(class_weight=None, crit...   \n",
       "4           LR  LogisticRegression(C=10, class_weight=None, du...   \n",
       "5           LR  LogisticRegression(C=10, class_weight=None, du...   \n",
       "6           LR  LogisticRegression(C=10, class_weight=None, du...   \n",
       "7           LR  LogisticRegression(C=10, class_weight=None, du...   \n",
       "8           RF  (DecisionTreeClassifier(class_weight=None, cri...   \n",
       "9           RF  (DecisionTreeClassifier(class_weight=None, cri...   \n",
       "10          RF  (DecisionTreeClassifier(class_weight=None, cri...   \n",
       "11          RF  (DecisionTreeClassifier(class_weight=None, cri...   \n",
       "12          RF  (DecisionTreeClassifier(class_weight=None, cri...   \n",
       "13          RF  (DecisionTreeClassifier(class_weight=None, cri...   \n",
       "14          NB       GaussianNB(priors=None, var_smoothing=1e-09)   \n",
       "15          BA  (KNeighborsClassifier(algorithm='auto', leaf_s...   \n",
       "16          BA  (KNeighborsClassifier(algorithm='auto', leaf_s...   \n",
       "17          AB  (DecisionTreeClassifier(class_weight=None, cri...   \n",
       "18          AB  (DecisionTreeClassifier(class_weight=None, cri...   \n",
       "19          GB  ([DecisionTreeRegressor(criterion='friedman_ms...   \n",
       "20          GB  ([DecisionTreeRegressor(criterion='friedman_ms...   \n",
       "21          GB  ([DecisionTreeRegressor(criterion='friedman_ms...   \n",
       "22          GB  ([DecisionTreeRegressor(criterion='friedman_ms...   \n",
       "23    Baseline                                     Baseline model   \n",
       "24          DT  DecisionTreeClassifier(class_weight=None, crit...   \n",
       "25          DT  DecisionTreeClassifier(class_weight=None, crit...   \n",
       "26          DT  DecisionTreeClassifier(class_weight=None, crit...   \n",
       "27          LR  LogisticRegression(C=10, class_weight=None, du...   \n",
       "28          LR  LogisticRegression(C=10, class_weight=None, du...   \n",
       "29          LR  LogisticRegression(C=10, class_weight=None, du...   \n",
       "..         ...                                                ...   \n",
       "177         BA  (KNeighborsClassifier(algorithm='auto', leaf_s...   \n",
       "178         AB  (DecisionTreeClassifier(class_weight=None, cri...   \n",
       "179         AB  (DecisionTreeClassifier(class_weight=None, cri...   \n",
       "180         GB  ([DecisionTreeRegressor(criterion='friedman_ms...   \n",
       "181         GB  ([DecisionTreeRegressor(criterion='friedman_ms...   \n",
       "182         GB  ([DecisionTreeRegressor(criterion='friedman_ms...   \n",
       "183         GB  ([DecisionTreeRegressor(criterion='friedman_ms...   \n",
       "184   Baseline                                     Baseline model   \n",
       "185         DT  DecisionTreeClassifier(class_weight=None, crit...   \n",
       "186         DT  DecisionTreeClassifier(class_weight=None, crit...   \n",
       "187         DT  DecisionTreeClassifier(class_weight=None, crit...   \n",
       "188         LR  LogisticRegression(C=10, class_weight=None, du...   \n",
       "189         LR  LogisticRegression(C=10, class_weight=None, du...   \n",
       "190         LR  LogisticRegression(C=10, class_weight=None, du...   \n",
       "191         LR  LogisticRegression(C=10, class_weight=None, du...   \n",
       "192         RF  (DecisionTreeClassifier(class_weight=None, cri...   \n",
       "193         RF  (DecisionTreeClassifier(class_weight=None, cri...   \n",
       "194         RF  (DecisionTreeClassifier(class_weight=None, cri...   \n",
       "195         RF  (DecisionTreeClassifier(class_weight=None, cri...   \n",
       "196         RF  (DecisionTreeClassifier(class_weight=None, cri...   \n",
       "197         RF  (DecisionTreeClassifier(class_weight=None, cri...   \n",
       "198         NB       GaussianNB(priors=None, var_smoothing=1e-09)   \n",
       "199         BA  (KNeighborsClassifier(algorithm='auto', leaf_s...   \n",
       "200         BA  (KNeighborsClassifier(algorithm='auto', leaf_s...   \n",
       "201         AB  (DecisionTreeClassifier(class_weight=None, cri...   \n",
       "202         AB  (DecisionTreeClassifier(class_weight=None, cri...   \n",
       "203         GB  ([DecisionTreeRegressor(criterion='friedman_ms...   \n",
       "204         GB  ([DecisionTreeRegressor(criterion='friedman_ms...   \n",
       "205         GB  ([DecisionTreeRegressor(criterion='friedman_ms...   \n",
       "206         GB  ([DecisionTreeRegressor(criterion='friedman_ms...   \n",
       "\n",
       "                                            parameters test_set_start_date  \\\n",
       "0                                                   {}          2005-01-01   \n",
       "1    {'min_samples_split': 2, 'criterion': 'gini', ...          2005-01-01   \n",
       "2    {'min_samples_split': 2, 'criterion': 'gini', ...          2005-01-01   \n",
       "3    {'min_samples_split': 2, 'criterion': 'gini', ...          2005-01-01   \n",
       "4                          {'penalty': 'l1', 'C': 0.1}          2005-01-01   \n",
       "5                          {'penalty': 'l2', 'C': 0.1}          2005-01-01   \n",
       "6                           {'penalty': 'l1', 'C': 10}          2005-01-01   \n",
       "7                           {'penalty': 'l2', 'C': 10}          2005-01-01   \n",
       "8    {'n_estimators': 100, 'n_jobs': -1, 'max_featu...          2005-01-01   \n",
       "9    {'n_estimators': 1000, 'n_jobs': -1, 'max_feat...          2005-01-01   \n",
       "10   {'n_estimators': 100, 'n_jobs': -1, 'max_featu...          2005-01-01   \n",
       "11   {'n_estimators': 1000, 'n_jobs': -1, 'max_feat...          2005-01-01   \n",
       "12   {'n_estimators': 100, 'n_jobs': -1, 'max_featu...          2005-01-01   \n",
       "13   {'n_estimators': 1000, 'n_jobs': -1, 'max_feat...          2005-01-01   \n",
       "14                                                  {}          2005-01-01   \n",
       "15                                {'n_estimators': 10}          2005-01-01   \n",
       "16                               {'n_estimators': 100}          2005-01-01   \n",
       "17          {'n_estimators': 10, 'algorithm': 'SAMME'}          2005-01-01   \n",
       "18         {'n_estimators': 100, 'algorithm': 'SAMME'}          2005-01-01   \n",
       "19        {'learning_rate': 0.001, 'n_estimators': 10}          2005-01-01   \n",
       "20       {'learning_rate': 0.001, 'n_estimators': 100}          2005-01-01   \n",
       "21          {'learning_rate': 0.1, 'n_estimators': 10}          2005-01-01   \n",
       "22         {'learning_rate': 0.1, 'n_estimators': 100}          2005-01-01   \n",
       "23                                                  {}          2006-01-01   \n",
       "24   {'min_samples_split': 2, 'criterion': 'gini', ...          2006-01-01   \n",
       "25   {'min_samples_split': 2, 'criterion': 'gini', ...          2006-01-01   \n",
       "26   {'min_samples_split': 2, 'criterion': 'gini', ...          2006-01-01   \n",
       "27                         {'penalty': 'l1', 'C': 0.1}          2006-01-01   \n",
       "28                         {'penalty': 'l2', 'C': 0.1}          2006-01-01   \n",
       "29                          {'penalty': 'l1', 'C': 10}          2006-01-01   \n",
       "..                                                 ...                 ...   \n",
       "177                              {'n_estimators': 100}          2012-01-01   \n",
       "178         {'n_estimators': 10, 'algorithm': 'SAMME'}          2012-01-01   \n",
       "179        {'n_estimators': 100, 'algorithm': 'SAMME'}          2012-01-01   \n",
       "180       {'learning_rate': 0.001, 'n_estimators': 10}          2012-01-01   \n",
       "181      {'learning_rate': 0.001, 'n_estimators': 100}          2012-01-01   \n",
       "182         {'learning_rate': 0.1, 'n_estimators': 10}          2012-01-01   \n",
       "183        {'learning_rate': 0.1, 'n_estimators': 100}          2012-01-01   \n",
       "184                                                 {}          2013-01-01   \n",
       "185  {'min_samples_split': 2, 'criterion': 'gini', ...          2013-01-01   \n",
       "186  {'min_samples_split': 2, 'criterion': 'gini', ...          2013-01-01   \n",
       "187  {'min_samples_split': 2, 'criterion': 'gini', ...          2013-01-01   \n",
       "188                        {'penalty': 'l1', 'C': 0.1}          2013-01-01   \n",
       "189                        {'penalty': 'l2', 'C': 0.1}          2013-01-01   \n",
       "190                         {'penalty': 'l1', 'C': 10}          2013-01-01   \n",
       "191                         {'penalty': 'l2', 'C': 10}          2013-01-01   \n",
       "192  {'n_estimators': 100, 'n_jobs': -1, 'max_featu...          2013-01-01   \n",
       "193  {'n_estimators': 1000, 'n_jobs': -1, 'max_feat...          2013-01-01   \n",
       "194  {'n_estimators': 100, 'n_jobs': -1, 'max_featu...          2013-01-01   \n",
       "195  {'n_estimators': 1000, 'n_jobs': -1, 'max_feat...          2013-01-01   \n",
       "196  {'n_estimators': 100, 'n_jobs': -1, 'max_featu...          2013-01-01   \n",
       "197  {'n_estimators': 1000, 'n_jobs': -1, 'max_feat...          2013-01-01   \n",
       "198                                                 {}          2013-01-01   \n",
       "199                               {'n_estimators': 10}          2013-01-01   \n",
       "200                              {'n_estimators': 100}          2013-01-01   \n",
       "201         {'n_estimators': 10, 'algorithm': 'SAMME'}          2013-01-01   \n",
       "202        {'n_estimators': 100, 'algorithm': 'SAMME'}          2013-01-01   \n",
       "203       {'learning_rate': 0.001, 'n_estimators': 10}          2013-01-01   \n",
       "204      {'learning_rate': 0.001, 'n_estimators': 100}          2013-01-01   \n",
       "205         {'learning_rate': 0.1, 'n_estimators': 10}          2013-01-01   \n",
       "206        {'learning_rate': 0.1, 'n_estimators': 100}          2013-01-01   \n",
       "\n",
       "     baseline    p_at_1    r_at_1   f1_at_1    p_at_2    r_at_2    ...     \\\n",
       "0    0.140202  0.944444  0.064394  0.120567  0.918919  0.128788    ...      \n",
       "1    0.140202  0.722222  0.049242  0.092199  0.675676  0.094697    ...      \n",
       "2    0.140202  0.000000  0.000000  0.000000  0.135135  0.018939    ...      \n",
       "3    0.140202  0.000000  0.000000  0.000000  0.135135  0.018939    ...      \n",
       "4    0.140202  0.777778  0.053030  0.099291  0.513514  0.071970    ...      \n",
       "5    0.140202  0.777778  0.053030  0.099291  0.540541  0.075758    ...      \n",
       "6    0.140202  0.833333  0.056818  0.106383  0.864865  0.121212    ...      \n",
       "7    0.140202  0.944444  0.064394  0.120567  0.918919  0.128788    ...      \n",
       "8    0.140202  1.000000  0.068182  0.127660  1.000000  0.140152    ...      \n",
       "9    0.140202  1.000000  0.068182  0.127660  1.000000  0.140152    ...      \n",
       "10   0.140202  1.000000  0.068182  0.127660  0.972973  0.136364    ...      \n",
       "11   0.140202  1.000000  0.068182  0.127660  1.000000  0.140152    ...      \n",
       "12   0.140202  1.000000  0.068182  0.127660  0.972973  0.136364    ...      \n",
       "13   0.140202  1.000000  0.068182  0.127660  1.000000  0.140152    ...      \n",
       "14   0.140202  0.333333  0.022727  0.042553  0.513514  0.071970    ...      \n",
       "15   0.140202  0.611111  0.041667  0.078014  0.405405  0.056818    ...      \n",
       "16   0.140202  0.666667  0.045455  0.085106  0.405405  0.056818    ...      \n",
       "17   0.140202  0.888889  0.060606  0.113475  0.837838  0.117424    ...      \n",
       "18   0.140202  0.888889  0.060606  0.113475  0.945946  0.132576    ...      \n",
       "19   0.140202  0.944444  0.064394  0.120567  0.972973  0.136364    ...      \n",
       "20   0.140202  1.000000  0.068182  0.127660  1.000000  0.140152    ...      \n",
       "21   0.140202  0.944444  0.064394  0.120567  0.945946  0.132576    ...      \n",
       "22   0.140202  0.666667  0.045455  0.085106  0.756757  0.106061    ...      \n",
       "23   0.111524  0.277778  0.023810  0.043860  0.540541  0.095238    ...      \n",
       "24   0.111524  0.777778  0.066667  0.122807  0.837838  0.147619    ...      \n",
       "25   0.111524  0.333333  0.028571  0.052632  0.351351  0.061905    ...      \n",
       "26   0.111524  0.333333  0.028571  0.052632  0.351351  0.061905    ...      \n",
       "27   0.111524  0.555556  0.047619  0.087719  0.567568  0.100000    ...      \n",
       "28   0.111524  0.500000  0.042857  0.078947  0.513514  0.090476    ...      \n",
       "29   0.111524  0.888889  0.076190  0.140351  0.675676  0.119048    ...      \n",
       "..        ...       ...       ...       ...       ...       ...    ...      \n",
       "177  0.149533  0.764706  0.050781  0.095238  0.705882  0.093750    ...      \n",
       "178  0.149533  0.823529  0.054688  0.102564  0.882353  0.117188    ...      \n",
       "179  0.149533  0.882353  0.058594  0.109890  0.911765  0.121094    ...      \n",
       "180  0.149533  1.000000  0.066406  0.124542  1.000000  0.132812    ...      \n",
       "181  0.149533  1.000000  0.066406  0.124542  1.000000  0.132812    ...      \n",
       "182  0.149533  1.000000  0.066406  0.124542  0.970588  0.128906    ...      \n",
       "183  0.149533  1.000000  0.066406  0.124542  0.970588  0.128906    ...      \n",
       "184  0.146552  0.882353  0.058824  0.110294  0.882353  0.117647    ...      \n",
       "185  0.146552  0.882353  0.058824  0.110294  0.941176  0.125490    ...      \n",
       "186  0.146552  0.647059  0.043137  0.080882  0.764706  0.101961    ...      \n",
       "187  0.146552  0.647059  0.043137  0.080882  0.764706  0.101961    ...      \n",
       "188  0.146552  1.000000  0.066667  0.125000  1.000000  0.133333    ...      \n",
       "189  0.146552  1.000000  0.066667  0.125000  1.000000  0.133333    ...      \n",
       "190  0.146552  1.000000  0.066667  0.125000  1.000000  0.133333    ...      \n",
       "191  0.146552  1.000000  0.066667  0.125000  1.000000  0.133333    ...      \n",
       "192  0.146552  1.000000  0.066667  0.125000  1.000000  0.133333    ...      \n",
       "193  0.146552  1.000000  0.066667  0.125000  1.000000  0.133333    ...      \n",
       "194  0.146552  1.000000  0.066667  0.125000  0.970588  0.129412    ...      \n",
       "195  0.146552  1.000000  0.066667  0.125000  0.941176  0.125490    ...      \n",
       "196  0.146552  1.000000  0.066667  0.125000  0.970588  0.129412    ...      \n",
       "197  0.146552  1.000000  0.066667  0.125000  0.941176  0.125490    ...      \n",
       "198  0.146552  0.941176  0.062745  0.117647  0.911765  0.121569    ...      \n",
       "199  0.146552  0.823529  0.054902  0.102941  0.705882  0.094118    ...      \n",
       "200  0.146552  0.705882  0.047059  0.088235  0.705882  0.094118    ...      \n",
       "201  0.146552  0.882353  0.058824  0.110294  0.882353  0.117647    ...      \n",
       "202  0.146552  1.000000  0.066667  0.125000  1.000000  0.133333    ...      \n",
       "203  0.146552  1.000000  0.066667  0.125000  1.000000  0.133333    ...      \n",
       "204  0.146552  1.000000  0.066667  0.125000  1.000000  0.133333    ...      \n",
       "205  0.146552  1.000000  0.066667  0.125000  1.000000  0.133333    ...      \n",
       "206  0.146552  1.000000  0.066667  0.125000  1.000000  0.133333    ...      \n",
       "\n",
       "      p_at_20   r_at_20  f1_at_20   p_at_30   r_at_30  f1_at_30   p_at_50  \\\n",
       "0    0.409574  0.583333  0.481250  0.281915  0.602273  0.384058  0.182784   \n",
       "1    0.531915  0.757576  0.625000  0.381206  0.814394  0.519324  0.244421   \n",
       "2    0.202128  0.287879  0.237500  0.253546  0.541667  0.345411  0.221041   \n",
       "3    0.202128  0.287879  0.237500  0.253546  0.541667  0.345411  0.221041   \n",
       "4    0.523936  0.746212  0.615625  0.377660  0.806818  0.514493  0.249734   \n",
       "5    0.449468  0.640152  0.528125  0.335106  0.715909  0.456522  0.226355   \n",
       "6    0.486702  0.693182  0.571875  0.358156  0.765152  0.487923  0.246546   \n",
       "7    0.470745  0.670455  0.553125  0.338652  0.723485  0.461353  0.228480   \n",
       "8    0.537234  0.765152  0.631250  0.395390  0.844697  0.538647  0.274176   \n",
       "9    0.545213  0.776515  0.640625  0.393617  0.840909  0.536232  0.272051   \n",
       "10   0.537234  0.765152  0.631250  0.404255  0.863636  0.550725  0.269926   \n",
       "11   0.550532  0.784091  0.646875  0.400709  0.856061  0.545894  0.272051   \n",
       "12   0.537234  0.765152  0.631250  0.404255  0.863636  0.550725  0.269926   \n",
       "13   0.550532  0.784091  0.646875  0.400709  0.856061  0.545894  0.272051   \n",
       "14   0.476064  0.678030  0.559375  0.351064  0.750000  0.478261  0.224230   \n",
       "15   0.199468  0.284091  0.234375  0.168440  0.359848  0.229469  0.124336   \n",
       "16   0.199468  0.284091  0.234375  0.154255  0.329545  0.210145  0.132837   \n",
       "17   0.531915  0.757576  0.625000  0.398936  0.852273  0.543478  0.270988   \n",
       "18   0.534574  0.761364  0.628125  0.391844  0.837121  0.533816  0.263549   \n",
       "19   0.534574  0.761364  0.628125  0.397163  0.848485  0.541063  0.253985   \n",
       "20   0.537234  0.765152  0.631250  0.393617  0.840909  0.536232  0.251860   \n",
       "21   0.539894  0.768939  0.634375  0.390071  0.833333  0.531401  0.255048   \n",
       "22   0.547872  0.780303  0.643750  0.400709  0.856061  0.545894  0.262487   \n",
       "23   0.268617  0.480952  0.344710  0.191489  0.514286  0.279070  0.125399   \n",
       "24   0.396277  0.709524  0.508532  0.265957  0.714286  0.387597  0.163656   \n",
       "25   0.271277  0.485714  0.348123  0.196809  0.528571  0.286822  0.128587   \n",
       "26   0.271277  0.485714  0.348123  0.196809  0.528571  0.286822  0.128587   \n",
       "27   0.369681  0.661905  0.474403  0.290780  0.780952  0.423773  0.200850   \n",
       "28   0.303191  0.542857  0.389078  0.235816  0.633333  0.343669  0.161530   \n",
       "29   0.382979  0.685714  0.491468  0.303191  0.814286  0.441860  0.208289   \n",
       "..        ...       ...       ...       ...       ...       ...       ...   \n",
       "177  0.321637  0.429688  0.367893  0.274854  0.550781  0.366710  0.192757   \n",
       "178  0.584795  0.781250  0.668896  0.458090  0.917969  0.611183  0.294393   \n",
       "179  0.628655  0.839844  0.719064  0.460039  0.921875  0.613784  0.293224   \n",
       "180  0.605263  0.808594  0.692308  0.444444  0.890625  0.592978  0.288551   \n",
       "181  0.619883  0.828125  0.709030  0.456140  0.914062  0.608583  0.294393   \n",
       "182  0.619883  0.828125  0.709030  0.450292  0.902344  0.600780  0.294393   \n",
       "183  0.625731  0.835938  0.715719  0.460039  0.921875  0.613784  0.294393   \n",
       "184  0.422414  0.576471  0.487562  0.283525  0.580392  0.380952  0.185057   \n",
       "185  0.609195  0.831373  0.703151  0.429119  0.878431  0.576577  0.262069   \n",
       "186  0.327586  0.447059  0.378109  0.218391  0.447059  0.293436  0.151724   \n",
       "187  0.327586  0.447059  0.378109  0.218391  0.447059  0.293436  0.151724   \n",
       "188  0.637931  0.870588  0.736318  0.467433  0.956863  0.628057  0.291954   \n",
       "189  0.626437  0.854902  0.723051  0.457854  0.937255  0.615187  0.290805   \n",
       "190  0.629310  0.858824  0.726368  0.469349  0.960784  0.630631  0.290805   \n",
       "191  0.629310  0.858824  0.726368  0.455939  0.933333  0.612613  0.290805   \n",
       "192  0.617816  0.843137  0.713101  0.459770  0.941176  0.617761  0.289655   \n",
       "193  0.614943  0.839216  0.709784  0.459770  0.941176  0.617761  0.288506   \n",
       "194  0.623563  0.850980  0.719735  0.459770  0.941176  0.617761  0.290805   \n",
       "195  0.635057  0.866667  0.733002  0.459770  0.941176  0.617761  0.290805   \n",
       "196  0.623563  0.850980  0.719735  0.459770  0.941176  0.617761  0.290805   \n",
       "197  0.635057  0.866667  0.733002  0.459770  0.941176  0.617761  0.290805   \n",
       "198  0.534483  0.729412  0.616915  0.409962  0.839216  0.550837  0.272414   \n",
       "199  0.327586  0.447059  0.378109  0.277778  0.568627  0.373230  0.179310   \n",
       "200  0.327586  0.447059  0.378109  0.289272  0.592157  0.388674  0.195402   \n",
       "201  0.571839  0.780392  0.660033  0.452107  0.925490  0.607465  0.288506   \n",
       "202  0.597701  0.815686  0.689884  0.457854  0.937255  0.615187  0.289655   \n",
       "203  0.629310  0.858824  0.726368  0.455939  0.933333  0.612613  0.289655   \n",
       "204  0.623563  0.850980  0.719735  0.469349  0.960784  0.630631  0.291954   \n",
       "205  0.629310  0.858824  0.726368  0.454023  0.929412  0.610039  0.290805   \n",
       "206  0.620690  0.847059  0.716418  0.461686  0.945098  0.620335  0.291954   \n",
       "\n",
       "      r_at_50  f1_at_50   auc-roc  \n",
       "0    0.651515  0.285477  0.778655  \n",
       "1    0.871212  0.381743  0.870593  \n",
       "2    0.787879  0.345228  0.720829  \n",
       "3    0.787879  0.345228  0.720829  \n",
       "4    0.890152  0.390041  0.868135  \n",
       "5    0.806818  0.353527  0.805510  \n",
       "6    0.878788  0.385062  0.833607  \n",
       "7    0.814394  0.356846  0.826228  \n",
       "8    0.977273  0.428216  0.922165  \n",
       "9    0.969697  0.424896  0.923473  \n",
       "10   0.962121  0.421577  0.924457  \n",
       "11   0.969697  0.424896  0.928565  \n",
       "12   0.962121  0.421577  0.924457  \n",
       "13   0.969697  0.424896  0.928565  \n",
       "14   0.799242  0.350207  0.806344  \n",
       "15   0.443182  0.194191  0.552944  \n",
       "16   0.473485  0.207469  0.547745  \n",
       "17   0.965909  0.423237  0.907226  \n",
       "18   0.939394  0.411618  0.910180  \n",
       "19   0.905303  0.396680  0.903254  \n",
       "20   0.897727  0.393361  0.902536  \n",
       "21   0.909091  0.398340  0.903324  \n",
       "22   0.935606  0.409959  0.910958  \n",
       "23   0.561905  0.205039  0.714176  \n",
       "24   0.733333  0.267593  0.805011  \n",
       "25   0.576190  0.210252  0.661267  \n",
       "26   0.576190  0.210252  0.661267  \n",
       "27   0.900000  0.328410  0.850821  \n",
       "28   0.723810  0.264118  0.749668  \n",
       "29   0.933333  0.340573  0.860536  \n",
       "..        ...       ...       ...  \n",
       "177  0.644531  0.296763  0.674648  \n",
       "178  0.984375  0.453237  0.939878  \n",
       "179  0.980469  0.451439  0.946112  \n",
       "180  0.964844  0.444245  0.933678  \n",
       "181  0.984375  0.453237  0.942100  \n",
       "182  0.984375  0.453237  0.942858  \n",
       "183  0.984375  0.453237  0.947593  \n",
       "184  0.631373  0.286222  0.777520  \n",
       "185  0.894118  0.405333  0.912128  \n",
       "186  0.517647  0.234667  0.688909  \n",
       "187  0.517647  0.234667  0.688909  \n",
       "188  0.996078  0.451556  0.964097  \n",
       "189  0.992157  0.449778  0.960133  \n",
       "190  0.992157  0.449778  0.959769  \n",
       "191  0.992157  0.449778  0.958570  \n",
       "192  0.988235  0.448000  0.956971  \n",
       "193  0.984314  0.446222  0.957036  \n",
       "194  0.992157  0.449778  0.955854  \n",
       "195  0.992157  0.449778  0.959055  \n",
       "196  0.992157  0.449778  0.955854  \n",
       "197  0.992157  0.449778  0.959055  \n",
       "198  0.929412  0.421333  0.903078  \n",
       "199  0.611765  0.277333  0.678798  \n",
       "200  0.666667  0.302222  0.699959  \n",
       "201  0.984314  0.446222  0.942167  \n",
       "202  0.988235  0.448000  0.950819  \n",
       "203  0.988235  0.448000  0.950670  \n",
       "204  0.996078  0.451556  0.961067  \n",
       "205  0.992157  0.449778  0.957825  \n",
       "206  0.996078  0.451556  0.955659  \n",
       "\n",
       "[207 rows x 27 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "importlib.reload(pipeline)\n",
    "importlib.reload(loop)\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "results = loop.iterate_over_models_and_training_test_sets(models_to_run, models, parameters_grid, train_test_sets)\n",
    "results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Observe best models for each train/test set, for different metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "importlib.reload(pipeline)\n",
    "\n",
    "#Lets obtain the best model for each train/test set, for each metric\n",
    "metrics_to_display = ['p_at_5','p_at_10', 'auc-roc']\n",
    "\n",
    "best_models_per_metric = {}\n",
    "\n",
    "for metric in metrics_to_display:\n",
    "    #indices of rows that have max value in specific metric for each train/test set\n",
    "    idx = results.groupby(['test_set_start_date'])[metric].transform(max) == results[metric]\n",
    "\n",
    "    #save table of best models at the specific metric\n",
    "    best_models_per_metric[metric] = results[idx]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Best models for Precision at 5%"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": true
   },
   "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>model_name</th>\n",
       "      <th>parameters</th>\n",
       "      <th>test_set_start_date</th>\n",
       "      <th>baseline</th>\n",
       "      <th>p_at_5</th>\n",
       "      <th>r_at_5</th>\n",
       "      <th>f1_at_5</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>RF</td>\n",
       "      <td>{'n_estimators': 100, 'n_jobs': -1, 'max_featu...</td>\n",
       "      <td>2005-01-01</td>\n",
       "      <td>0.140202</td>\n",
       "      <td>0.989362</td>\n",
       "      <td>0.352273</td>\n",
       "      <td>0.519553</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>RF</td>\n",
       "      <td>{'n_estimators': 100, 'n_jobs': -1, 'max_featu...</td>\n",
       "      <td>2005-01-01</td>\n",
       "      <td>0.140202</td>\n",
       "      <td>0.989362</td>\n",
       "      <td>0.352273</td>\n",
       "      <td>0.519553</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>DT</td>\n",
       "      <td>{'min_samples_split': 2, 'criterion': 'gini', ...</td>\n",
       "      <td>2006-01-01</td>\n",
       "      <td>0.111524</td>\n",
       "      <td>0.627660</td>\n",
       "      <td>0.280952</td>\n",
       "      <td>0.388158</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>GB</td>\n",
       "      <td>{'learning_rate': 0.001, 'n_estimators': 10}</td>\n",
       "      <td>2006-01-01</td>\n",
       "      <td>0.111524</td>\n",
       "      <td>0.627660</td>\n",
       "      <td>0.280952</td>\n",
       "      <td>0.388158</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>GB</td>\n",
       "      <td>{'learning_rate': 0.001, 'n_estimators': 100}</td>\n",
       "      <td>2006-01-01</td>\n",
       "      <td>0.111524</td>\n",
       "      <td>0.627660</td>\n",
       "      <td>0.280952</td>\n",
       "      <td>0.388158</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>DT</td>\n",
       "      <td>{'min_samples_split': 2, 'criterion': 'gini', ...</td>\n",
       "      <td>2007-01-01</td>\n",
       "      <td>0.154198</td>\n",
       "      <td>0.953846</td>\n",
       "      <td>0.306931</td>\n",
       "      <td>0.464419</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>RF</td>\n",
       "      <td>{'n_estimators': 100, 'n_jobs': -1, 'max_featu...</td>\n",
       "      <td>2007-01-01</td>\n",
       "      <td>0.154198</td>\n",
       "      <td>0.953846</td>\n",
       "      <td>0.306931</td>\n",
       "      <td>0.464419</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>LR</td>\n",
       "      <td>{'penalty': 'l1', 'C': 10}</td>\n",
       "      <td>2008-01-01</td>\n",
       "      <td>0.145802</td>\n",
       "      <td>0.938462</td>\n",
       "      <td>0.319372</td>\n",
       "      <td>0.476562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89</th>\n",
       "      <td>GB</td>\n",
       "      <td>{'learning_rate': 0.001, 'n_estimators': 100}</td>\n",
       "      <td>2008-01-01</td>\n",
       "      <td>0.145802</td>\n",
       "      <td>0.938462</td>\n",
       "      <td>0.319372</td>\n",
       "      <td>0.476562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>GB</td>\n",
       "      <td>{'learning_rate': 0.1, 'n_estimators': 10}</td>\n",
       "      <td>2008-01-01</td>\n",
       "      <td>0.145802</td>\n",
       "      <td>0.938462</td>\n",
       "      <td>0.319372</td>\n",
       "      <td>0.476562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103</th>\n",
       "      <td>RF</td>\n",
       "      <td>{'n_estimators': 1000, 'n_jobs': -1, 'max_feat...</td>\n",
       "      <td>2009-01-01</td>\n",
       "      <td>0.146154</td>\n",
       "      <td>0.984615</td>\n",
       "      <td>0.336842</td>\n",
       "      <td>0.501961</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105</th>\n",
       "      <td>RF</td>\n",
       "      <td>{'n_estimators': 1000, 'n_jobs': -1, 'max_feat...</td>\n",
       "      <td>2009-01-01</td>\n",
       "      <td>0.146154</td>\n",
       "      <td>0.984615</td>\n",
       "      <td>0.336842</td>\n",
       "      <td>0.501961</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>123</th>\n",
       "      <td>RF</td>\n",
       "      <td>{'n_estimators': 100, 'n_jobs': -1, 'max_featu...</td>\n",
       "      <td>2010-01-01</td>\n",
       "      <td>0.170803</td>\n",
       "      <td>0.985294</td>\n",
       "      <td>0.286325</td>\n",
       "      <td>0.443709</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124</th>\n",
       "      <td>RF</td>\n",
       "      <td>{'n_estimators': 1000, 'n_jobs': -1, 'max_feat...</td>\n",
       "      <td>2010-01-01</td>\n",
       "      <td>0.170803</td>\n",
       "      <td>0.985294</td>\n",
       "      <td>0.286325</td>\n",
       "      <td>0.443709</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>139</th>\n",
       "      <td>DT</td>\n",
       "      <td>{'min_samples_split': 2, 'criterion': 'gini', ...</td>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>0.148040</td>\n",
       "      <td>0.952941</td>\n",
       "      <td>0.320158</td>\n",
       "      <td>0.479290</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143</th>\n",
       "      <td>LR</td>\n",
       "      <td>{'penalty': 'l2', 'C': 0.1}</td>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>0.148040</td>\n",
       "      <td>0.952941</td>\n",
       "      <td>0.320158</td>\n",
       "      <td>0.479290</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>166</th>\n",
       "      <td>LR</td>\n",
       "      <td>{'penalty': 'l2', 'C': 0.1}</td>\n",
       "      <td>2012-01-01</td>\n",
       "      <td>0.149533</td>\n",
       "      <td>0.988235</td>\n",
       "      <td>0.328125</td>\n",
       "      <td>0.492669</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>167</th>\n",
       "      <td>LR</td>\n",
       "      <td>{'penalty': 'l1', 'C': 10}</td>\n",
       "      <td>2012-01-01</td>\n",
       "      <td>0.149533</td>\n",
       "      <td>0.988235</td>\n",
       "      <td>0.328125</td>\n",
       "      <td>0.492669</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>168</th>\n",
       "      <td>LR</td>\n",
       "      <td>{'penalty': 'l2', 'C': 10}</td>\n",
       "      <td>2012-01-01</td>\n",
       "      <td>0.149533</td>\n",
       "      <td>0.988235</td>\n",
       "      <td>0.328125</td>\n",
       "      <td>0.492669</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>169</th>\n",
       "      <td>RF</td>\n",
       "      <td>{'n_estimators': 100, 'n_jobs': -1, 'max_featu...</td>\n",
       "      <td>2012-01-01</td>\n",
       "      <td>0.149533</td>\n",
       "      <td>0.988235</td>\n",
       "      <td>0.328125</td>\n",
       "      <td>0.492669</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>170</th>\n",
       "      <td>RF</td>\n",
       "      <td>{'n_estimators': 1000, 'n_jobs': -1, 'max_feat...</td>\n",
       "      <td>2012-01-01</td>\n",
       "      <td>0.149533</td>\n",
       "      <td>0.988235</td>\n",
       "      <td>0.328125</td>\n",
       "      <td>0.492669</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>192</th>\n",
       "      <td>RF</td>\n",
       "      <td>{'n_estimators': 100, 'n_jobs': -1, 'max_featu...</td>\n",
       "      <td>2013-01-01</td>\n",
       "      <td>0.146552</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.341176</td>\n",
       "      <td>0.508772</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>193</th>\n",
       "      <td>RF</td>\n",
       "      <td>{'n_estimators': 1000, 'n_jobs': -1, 'max_feat...</td>\n",
       "      <td>2013-01-01</td>\n",
       "      <td>0.146552</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.341176</td>\n",
       "      <td>0.508772</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    model_name                                         parameters  \\\n",
       "10          RF  {'n_estimators': 100, 'n_jobs': -1, 'max_featu...   \n",
       "12          RF  {'n_estimators': 100, 'n_jobs': -1, 'max_featu...   \n",
       "24          DT  {'min_samples_split': 2, 'criterion': 'gini', ...   \n",
       "42          GB       {'learning_rate': 0.001, 'n_estimators': 10}   \n",
       "43          GB      {'learning_rate': 0.001, 'n_estimators': 100}   \n",
       "47          DT  {'min_samples_split': 2, 'criterion': 'gini', ...   \n",
       "54          RF  {'n_estimators': 100, 'n_jobs': -1, 'max_featu...   \n",
       "75          LR                         {'penalty': 'l1', 'C': 10}   \n",
       "89          GB      {'learning_rate': 0.001, 'n_estimators': 100}   \n",
       "90          GB         {'learning_rate': 0.1, 'n_estimators': 10}   \n",
       "103         RF  {'n_estimators': 1000, 'n_jobs': -1, 'max_feat...   \n",
       "105         RF  {'n_estimators': 1000, 'n_jobs': -1, 'max_feat...   \n",
       "123         RF  {'n_estimators': 100, 'n_jobs': -1, 'max_featu...   \n",
       "124         RF  {'n_estimators': 1000, 'n_jobs': -1, 'max_feat...   \n",
       "139         DT  {'min_samples_split': 2, 'criterion': 'gini', ...   \n",
       "143         LR                        {'penalty': 'l2', 'C': 0.1}   \n",
       "166         LR                        {'penalty': 'l2', 'C': 0.1}   \n",
       "167         LR                         {'penalty': 'l1', 'C': 10}   \n",
       "168         LR                         {'penalty': 'l2', 'C': 10}   \n",
       "169         RF  {'n_estimators': 100, 'n_jobs': -1, 'max_featu...   \n",
       "170         RF  {'n_estimators': 1000, 'n_jobs': -1, 'max_feat...   \n",
       "192         RF  {'n_estimators': 100, 'n_jobs': -1, 'max_featu...   \n",
       "193         RF  {'n_estimators': 1000, 'n_jobs': -1, 'max_feat...   \n",
       "\n",
       "    test_set_start_date  baseline    p_at_5    r_at_5   f1_at_5  \n",
       "10           2005-01-01  0.140202  0.989362  0.352273  0.519553  \n",
       "12           2005-01-01  0.140202  0.989362  0.352273  0.519553  \n",
       "24           2006-01-01  0.111524  0.627660  0.280952  0.388158  \n",
       "42           2006-01-01  0.111524  0.627660  0.280952  0.388158  \n",
       "43           2006-01-01  0.111524  0.627660  0.280952  0.388158  \n",
       "47           2007-01-01  0.154198  0.953846  0.306931  0.464419  \n",
       "54           2007-01-01  0.154198  0.953846  0.306931  0.464419  \n",
       "75           2008-01-01  0.145802  0.938462  0.319372  0.476562  \n",
       "89           2008-01-01  0.145802  0.938462  0.319372  0.476562  \n",
       "90           2008-01-01  0.145802  0.938462  0.319372  0.476562  \n",
       "103          2009-01-01  0.146154  0.984615  0.336842  0.501961  \n",
       "105          2009-01-01  0.146154  0.984615  0.336842  0.501961  \n",
       "123          2010-01-01  0.170803  0.985294  0.286325  0.443709  \n",
       "124          2010-01-01  0.170803  0.985294  0.286325  0.443709  \n",
       "139          2011-01-01  0.148040  0.952941  0.320158  0.479290  \n",
       "143          2011-01-01  0.148040  0.952941  0.320158  0.479290  \n",
       "166          2012-01-01  0.149533  0.988235  0.328125  0.492669  \n",
       "167          2012-01-01  0.149533  0.988235  0.328125  0.492669  \n",
       "168          2012-01-01  0.149533  0.988235  0.328125  0.492669  \n",
       "169          2012-01-01  0.149533  0.988235  0.328125  0.492669  \n",
       "170          2012-01-01  0.149533  0.988235  0.328125  0.492669  \n",
       "192          2013-01-01  0.146552  1.000000  0.341176  0.508772  \n",
       "193          2013-01-01  0.146552  1.000000  0.341176  0.508772  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best_models_per_metric['p_at_5'].iloc[:, [0,2,3,4,11,12,13]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Best models for Precision at 10%"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>model_name</th>\n",
       "      <th>parameters</th>\n",
       "      <th>test_set_start_date</th>\n",
       "      <th>baseline</th>\n",
       "      <th>p_at_10</th>\n",
       "      <th>r_at_10</th>\n",
       "      <th>f1_at_10</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>RF</td>\n",
       "      <td>{'n_estimators': 1000, 'n_jobs': -1, 'max_feat...</td>\n",
       "      <td>2005-01-01</td>\n",
       "      <td>0.140202</td>\n",
       "      <td>0.867021</td>\n",
       "      <td>0.617424</td>\n",
       "      <td>0.721239</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>RF</td>\n",
       "      <td>{'n_estimators': 1000, 'n_jobs': -1, 'max_feat...</td>\n",
       "      <td>2005-01-01</td>\n",
       "      <td>0.140202</td>\n",
       "      <td>0.867021</td>\n",
       "      <td>0.617424</td>\n",
       "      <td>0.721239</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>RF</td>\n",
       "      <td>{'n_estimators': 100, 'n_jobs': -1, 'max_featu...</td>\n",
       "      <td>2006-01-01</td>\n",
       "      <td>0.111524</td>\n",
       "      <td>0.569149</td>\n",
       "      <td>0.509524</td>\n",
       "      <td>0.537688</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>RF</td>\n",
       "      <td>{'n_estimators': 1000, 'n_jobs': -1, 'max_feat...</td>\n",
       "      <td>2006-01-01</td>\n",
       "      <td>0.111524</td>\n",
       "      <td>0.569149</td>\n",
       "      <td>0.509524</td>\n",
       "      <td>0.537688</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>RF</td>\n",
       "      <td>{'n_estimators': 100, 'n_jobs': -1, 'max_featu...</td>\n",
       "      <td>2006-01-01</td>\n",
       "      <td>0.111524</td>\n",
       "      <td>0.569149</td>\n",
       "      <td>0.509524</td>\n",
       "      <td>0.537688</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>RF</td>\n",
       "      <td>{'n_estimators': 1000, 'n_jobs': -1, 'max_feat...</td>\n",
       "      <td>2006-01-01</td>\n",
       "      <td>0.111524</td>\n",
       "      <td>0.569149</td>\n",
       "      <td>0.509524</td>\n",
       "      <td>0.537688</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>RF</td>\n",
       "      <td>{'n_estimators': 100, 'n_jobs': -1, 'max_featu...</td>\n",
       "      <td>2007-01-01</td>\n",
       "      <td>0.154198</td>\n",
       "      <td>0.847328</td>\n",
       "      <td>0.549505</td>\n",
       "      <td>0.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>RF</td>\n",
       "      <td>{'n_estimators': 100, 'n_jobs': -1, 'max_featu...</td>\n",
       "      <td>2007-01-01</td>\n",
       "      <td>0.154198</td>\n",
       "      <td>0.847328</td>\n",
       "      <td>0.549505</td>\n",
       "      <td>0.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>LR</td>\n",
       "      <td>{'penalty': 'l1', 'C': 0.1}</td>\n",
       "      <td>2008-01-01</td>\n",
       "      <td>0.145802</td>\n",
       "      <td>0.801527</td>\n",
       "      <td>0.549738</td>\n",
       "      <td>0.652174</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>RF</td>\n",
       "      <td>{'n_estimators': 100, 'n_jobs': -1, 'max_featu...</td>\n",
       "      <td>2008-01-01</td>\n",
       "      <td>0.145802</td>\n",
       "      <td>0.801527</td>\n",
       "      <td>0.549738</td>\n",
       "      <td>0.652174</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>RF</td>\n",
       "      <td>{'n_estimators': 1000, 'n_jobs': -1, 'max_feat...</td>\n",
       "      <td>2008-01-01</td>\n",
       "      <td>0.145802</td>\n",
       "      <td>0.801527</td>\n",
       "      <td>0.549738</td>\n",
       "      <td>0.652174</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>RF</td>\n",
       "      <td>{'n_estimators': 1000, 'n_jobs': -1, 'max_feat...</td>\n",
       "      <td>2008-01-01</td>\n",
       "      <td>0.145802</td>\n",
       "      <td>0.801527</td>\n",
       "      <td>0.549738</td>\n",
       "      <td>0.652174</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>113</th>\n",
       "      <td>GB</td>\n",
       "      <td>{'learning_rate': 0.1, 'n_estimators': 10}</td>\n",
       "      <td>2009-01-01</td>\n",
       "      <td>0.146154</td>\n",
       "      <td>0.830769</td>\n",
       "      <td>0.568421</td>\n",
       "      <td>0.675000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>122</th>\n",
       "      <td>LR</td>\n",
       "      <td>{'penalty': 'l2', 'C': 10}</td>\n",
       "      <td>2010-01-01</td>\n",
       "      <td>0.170803</td>\n",
       "      <td>0.941606</td>\n",
       "      <td>0.551282</td>\n",
       "      <td>0.695418</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>RF</td>\n",
       "      <td>{'n_estimators': 1000, 'n_jobs': -1, 'max_feat...</td>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>0.148040</td>\n",
       "      <td>0.852941</td>\n",
       "      <td>0.573123</td>\n",
       "      <td>0.685579</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>166</th>\n",
       "      <td>LR</td>\n",
       "      <td>{'penalty': 'l2', 'C': 0.1}</td>\n",
       "      <td>2012-01-01</td>\n",
       "      <td>0.149533</td>\n",
       "      <td>0.906433</td>\n",
       "      <td>0.605469</td>\n",
       "      <td>0.725995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>193</th>\n",
       "      <td>RF</td>\n",
       "      <td>{'n_estimators': 1000, 'n_jobs': -1, 'max_feat...</td>\n",
       "      <td>2013-01-01</td>\n",
       "      <td>0.146552</td>\n",
       "      <td>0.896552</td>\n",
       "      <td>0.611765</td>\n",
       "      <td>0.727273</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    model_name                                         parameters  \\\n",
       "11          RF  {'n_estimators': 1000, 'n_jobs': -1, 'max_feat...   \n",
       "13          RF  {'n_estimators': 1000, 'n_jobs': -1, 'max_feat...   \n",
       "33          RF  {'n_estimators': 100, 'n_jobs': -1, 'max_featu...   \n",
       "34          RF  {'n_estimators': 1000, 'n_jobs': -1, 'max_feat...   \n",
       "35          RF  {'n_estimators': 100, 'n_jobs': -1, 'max_featu...   \n",
       "36          RF  {'n_estimators': 1000, 'n_jobs': -1, 'max_feat...   \n",
       "56          RF  {'n_estimators': 100, 'n_jobs': -1, 'max_featu...   \n",
       "58          RF  {'n_estimators': 100, 'n_jobs': -1, 'max_featu...   \n",
       "73          LR                        {'penalty': 'l1', 'C': 0.1}   \n",
       "77          RF  {'n_estimators': 100, 'n_jobs': -1, 'max_featu...   \n",
       "80          RF  {'n_estimators': 1000, 'n_jobs': -1, 'max_feat...   \n",
       "82          RF  {'n_estimators': 1000, 'n_jobs': -1, 'max_feat...   \n",
       "113         GB         {'learning_rate': 0.1, 'n_estimators': 10}   \n",
       "122         LR                         {'penalty': 'l2', 'C': 10}   \n",
       "147         RF  {'n_estimators': 1000, 'n_jobs': -1, 'max_feat...   \n",
       "166         LR                        {'penalty': 'l2', 'C': 0.1}   \n",
       "193         RF  {'n_estimators': 1000, 'n_jobs': -1, 'max_feat...   \n",
       "\n",
       "    test_set_start_date  baseline   p_at_10   r_at_10  f1_at_10  \n",
       "11           2005-01-01  0.140202  0.867021  0.617424  0.721239  \n",
       "13           2005-01-01  0.140202  0.867021  0.617424  0.721239  \n",
       "33           2006-01-01  0.111524  0.569149  0.509524  0.537688  \n",
       "34           2006-01-01  0.111524  0.569149  0.509524  0.537688  \n",
       "35           2006-01-01  0.111524  0.569149  0.509524  0.537688  \n",
       "36           2006-01-01  0.111524  0.569149  0.509524  0.537688  \n",
       "56           2007-01-01  0.154198  0.847328  0.549505  0.666667  \n",
       "58           2007-01-01  0.154198  0.847328  0.549505  0.666667  \n",
       "73           2008-01-01  0.145802  0.801527  0.549738  0.652174  \n",
       "77           2008-01-01  0.145802  0.801527  0.549738  0.652174  \n",
       "80           2008-01-01  0.145802  0.801527  0.549738  0.652174  \n",
       "82           2008-01-01  0.145802  0.801527  0.549738  0.652174  \n",
       "113          2009-01-01  0.146154  0.830769  0.568421  0.675000  \n",
       "122          2010-01-01  0.170803  0.941606  0.551282  0.695418  \n",
       "147          2011-01-01  0.148040  0.852941  0.573123  0.685579  \n",
       "166          2012-01-01  0.149533  0.906433  0.605469  0.725995  \n",
       "193          2013-01-01  0.146552  0.896552  0.611765  0.727273  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best_models_per_metric['p_at_10'].iloc[:, [0,2,3,4,14,15,16]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Best models for AUC-ROC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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>model_name</th>\n",
       "      <th>parameters</th>\n",
       "      <th>test_set_start_date</th>\n",
       "      <th>baseline</th>\n",
       "      <th>auc-roc</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>RF</td>\n",
       "      <td>{'n_estimators': 1000, 'n_jobs': -1, 'max_feat...</td>\n",
       "      <td>2005-01-01</td>\n",
       "      <td>0.140202</td>\n",
       "      <td>0.928565</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>RF</td>\n",
       "      <td>{'n_estimators': 1000, 'n_jobs': -1, 'max_feat...</td>\n",
       "      <td>2005-01-01</td>\n",
       "      <td>0.140202</td>\n",
       "      <td>0.928565</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>RF</td>\n",
       "      <td>{'n_estimators': 1000, 'n_jobs': -1, 'max_feat...</td>\n",
       "      <td>2006-01-01</td>\n",
       "      <td>0.111524</td>\n",
       "      <td>0.885851</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>RF</td>\n",
       "      <td>{'n_estimators': 1000, 'n_jobs': -1, 'max_feat...</td>\n",
       "      <td>2006-01-01</td>\n",
       "      <td>0.111524</td>\n",
       "      <td>0.885851</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>GB</td>\n",
       "      <td>{'learning_rate': 0.001, 'n_estimators': 100}</td>\n",
       "      <td>2007-01-01</td>\n",
       "      <td>0.154198</td>\n",
       "      <td>0.916188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>RF</td>\n",
       "      <td>{'n_estimators': 1000, 'n_jobs': -1, 'max_feat...</td>\n",
       "      <td>2008-01-01</td>\n",
       "      <td>0.145802</td>\n",
       "      <td>0.925141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>RF</td>\n",
       "      <td>{'n_estimators': 1000, 'n_jobs': -1, 'max_feat...</td>\n",
       "      <td>2008-01-01</td>\n",
       "      <td>0.145802</td>\n",
       "      <td>0.925141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>109</th>\n",
       "      <td>AB</td>\n",
       "      <td>{'n_estimators': 10, 'algorithm': 'SAMME'}</td>\n",
       "      <td>2009-01-01</td>\n",
       "      <td>0.146154</td>\n",
       "      <td>0.938359</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>123</th>\n",
       "      <td>RF</td>\n",
       "      <td>{'n_estimators': 100, 'n_jobs': -1, 'max_featu...</td>\n",
       "      <td>2010-01-01</td>\n",
       "      <td>0.170803</td>\n",
       "      <td>0.942200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>RF</td>\n",
       "      <td>{'n_estimators': 100, 'n_jobs': -1, 'max_featu...</td>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>0.148040</td>\n",
       "      <td>0.941272</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>166</th>\n",
       "      <td>LR</td>\n",
       "      <td>{'penalty': 'l2', 'C': 0.1}</td>\n",
       "      <td>2012-01-01</td>\n",
       "      <td>0.149533</td>\n",
       "      <td>0.949455</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>188</th>\n",
       "      <td>LR</td>\n",
       "      <td>{'penalty': 'l1', 'C': 0.1}</td>\n",
       "      <td>2013-01-01</td>\n",
       "      <td>0.146552</td>\n",
       "      <td>0.964097</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    model_name                                         parameters  \\\n",
       "11          RF  {'n_estimators': 1000, 'n_jobs': -1, 'max_feat...   \n",
       "13          RF  {'n_estimators': 1000, 'n_jobs': -1, 'max_feat...   \n",
       "34          RF  {'n_estimators': 1000, 'n_jobs': -1, 'max_feat...   \n",
       "36          RF  {'n_estimators': 1000, 'n_jobs': -1, 'max_feat...   \n",
       "66          GB      {'learning_rate': 0.001, 'n_estimators': 100}   \n",
       "80          RF  {'n_estimators': 1000, 'n_jobs': -1, 'max_feat...   \n",
       "82          RF  {'n_estimators': 1000, 'n_jobs': -1, 'max_feat...   \n",
       "109         AB         {'n_estimators': 10, 'algorithm': 'SAMME'}   \n",
       "123         RF  {'n_estimators': 100, 'n_jobs': -1, 'max_featu...   \n",
       "146         RF  {'n_estimators': 100, 'n_jobs': -1, 'max_featu...   \n",
       "166         LR                        {'penalty': 'l2', 'C': 0.1}   \n",
       "188         LR                        {'penalty': 'l1', 'C': 0.1}   \n",
       "\n",
       "    test_set_start_date  baseline   auc-roc  \n",
       "11           2005-01-01  0.140202  0.928565  \n",
       "13           2005-01-01  0.140202  0.928565  \n",
       "34           2006-01-01  0.111524  0.885851  \n",
       "36           2006-01-01  0.111524  0.885851  \n",
       "66           2007-01-01  0.154198  0.916188  \n",
       "80           2008-01-01  0.145802  0.925141  \n",
       "82           2008-01-01  0.145802  0.925141  \n",
       "109          2009-01-01  0.146154  0.938359  \n",
       "123          2010-01-01  0.170803  0.942200  \n",
       "146          2011-01-01  0.148040  0.941272  \n",
       "166          2012-01-01  0.149533  0.949455  \n",
       "188          2013-01-01  0.146552  0.964097  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best_models_per_metric['auc-roc'].iloc[:, [0,2,3,4,26]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot of all model types performance at different train/test sets, for the different metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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H53nUajWklCwm4hxzHFbi/KonThB/8IFdE4R3C7KyjAqaQTXrT2ISNKu5wHdh6SKUpiA9APF2vKkpvKkp+MFTCMPAGhrEGh7GHh7G6OnZYBFsRRAEOI6zenNdd8PzYrXIC2deoDRdCgM3ABwoXCywNL+Elbau+PkohV12iC7XMRphcIsuFDEgiSCl6WQ0i5wVI3fX3cQfehAzFsArfwUz41t/5tD74fAnwpPwW0T5kqDkEhQcgmJTRIsObGVBCwG6Fd6ugrB07NwsztnppiXqEbg2yougd2chl0FXik6gE4kbeMzWl5ltFCj7dSDUaZRCs0CzFU5GMdWrmDygSEuPjnKR9OQUsWqd1YhnRTNCWoGSKE/hjwV4l3y8bIR6f5ISi0xHfQxNIwBsBSLw2FfQ+RdTKfypMkHdX783W+5jJRJhsbuLhUQaIiZCiMunuVff3RXzGUr4dETkFtc/KvQQeDUYPxFeqKw8h/D3fuoPYPCh7Q+4ZoARCcX5CvcyiOCVLbyCjr8MKtBCl7cw4Ar/I5qto6RCS5hoUQMtaqAaAUau5WW7GbklhPXkyZMU182p1mo1ch0dnC8U6QrCeTZZqVA/fZrYfffdqGHeFNS+/iWMXAJnqgS+AkNgt0UIKnUSd3QjrChuvnll7TVg8TyUpiEzCNEMIFC+T+PiCJWLI+H8mW1BXx+yswvV0Y5nWavCuXLv+/6GcfjSp+yWKbklym4ZJ3DwZsNAkvXnWYVCVAVsM+UrAkm02CBXqJPyApJCJ6Pb5HSLpG6t1nbV4nFi991H7P770G0Dzj4Op37AliZEqg+O/jTk9l7TsZUNP7RCi+6qJSq3sZqvBS1hoqdt9IyNkbbR0zYioiPEMM6p56g9/SZ+RWClGsQe3ot529148zW82dBSUr7EAgbjHQwCZbfGbG2R2doirvRWjWCqoEV0RNSkEDcoZjX0vcfpsG3ayxXil0aRyxsjeAWhNbpyuZqeh/9m+QivzZ4mUZZYLngm1OJwpO8oTnoIcitWdxBa4jJYtcylISh0tzOXSFAxdIQKECuvX0bUkAwmfAbiAVFjhwfZqYB5mVdCN8PlV0L6Tbf9xvWUFPiNJF4tiVdLI90rCKEQIPS1uWVdx0gGmFmwD1tU39DQ8nMIWUIZOWRyP4lHju5sv1q8o9wSwlq9zB0kpcTxPFQqBcvLq8srT/+Q6D33XNW6eq8il6ap/OCfMLMpzub6eK1tkIYVIUbAA4cHuf+eftyLT6PVPaqLDSrLDrVlj3pd4tWWcP06XqwN17DxWKcXrgujo+ENEJaFlkyhp5LoqRTCNJFKUnbLq7eaX9s0Pk1qa9bqZctXsCM2qWSKDiNG12yR3NQcGWVjGNEtf+1GZyfx9z1E9OjRsPTfzGl4/evQ2CI6Vbfh0Mdh749ssi6c8RK1F+fx8w2MrE3kSA49Ya25cosOqnEtaTVboAn0tBWKaNrGyNjoKXuDi/By7Hvvx773/s3L96Sx96RRUuEv1cMgmdkqQcklacVIWoPsTw+w5BSZrS6y0MgjlUI2AmgEBMuALtCiBtMxh9mIgX3bbfQmk7SVSpgjI/izc1uOSVVi7B2x8fUGviaJS422xQgyHYPsSqDSGsKy8PYNM5/NMhcEBDIMHNtgeCoVipsK6MqlGOrK0JEw0PzGmvXp1cP5aq/evDWX++vcqXYCPAeMdV6BwAuX7wClIHCj+LUUXj2JX0+EuVc7fLNmVDBjJcxYCSNaQWgyzMaqLKB7eWruI/iyHUMskNCfxdZ+CbhnZ5/f4h3jlhDWXC6H53nU6/XVZYVCgcH+PigVIQj/UYPlZRqvv0706K15FVj9+v+Lcn3ejHdwqm0voNDiKapS8uR4gTeCOB3Zh6AxCcxBFsiCCiRB3Seoe8h6EeVZ4fzUNvNOynXxlxZpLMzgBR4NE6q2wo2ZeDFr21JwUpNhLh+shsoqpcCAzz72WQY6BojMLFJ95gTO+fPNd1lbehLtgweJv+8hrL17Qyu4uggvfBUW3tz64PTcBbd/GqIbi4lIN6B+ZonKD8LeEUop/MU69TfzmH0JjOTV3bZbISwdPWM3RdTCyNhoCQux0zJ5O92OJjA7YpgdMaJ3tCNr3gZrtl1kaI9k8KTPfD3PTHWB4opVFihkxVsNsvEjOheiZUaiBpnbb6f3fe+jrVBEnj+HNzG5us3l8xeRIkaghUE3vga69KmeuUCQztKRtNFtG+PAfgq9vcxISaFUAs/bNP4VItEog4ODDA4OEr3WQDop14R34iQ88+/DeVMjAk4ptFaP/DikekIR9huht8avg9dANjy8WgK/lsSrpVDBzqeThBZgxEqY0TJGrIRuuluvmL/AsurkTEOQdwxy6BzWY3Sf/jL0t4T1ZuOWENZjx47xncefwC85KF+idFCmIJHJEL3rLuovvrS6buWpp4jcccdNWcZtNwlmLlI98SwAr6b7UEohLBtFmN+nlGJ+fp6Ojg5o2wepXiiMQ20JoWsYCQsjYaFQKE8i6zUCGUH61mpRIl/6eNLDDTx86SFXXvAgWlMk8gEakiBq4GQsGrkITi6CsgS6oaPFNNyLLmigGzoykAgpuPeB4wwvOlS/+efU5ue33UdhmkTvvpv4gw9gtLc3d9yHi9+F80+EbsfLiebg6E+j2g8TlD2ChVLoyi05BCUXVfdxLhVRnkQYzc4kugAlCJbqOxLWVVfuihW66sp953+DWsxcs2YDhZ8PrVl9tkqfZtAX76TmN5itLTJTXaQRrEVcr7dmF+aqLEVn0WMW3bcfpe+DHyS5sMgr//Q81OoE5sbjEug6ptvgxWg35Z4huve2E5cVVOHKxSE6OzsZGhqis7NztaDDte+0FlqkdgKOfBKSPXD6y2FUcOdtcOzzG8Rr/XHx5moENQesAIwAksGaG1v5a25sGZY9hAAjUseIVzCjRXSziPDroVhv4c5WCmpOhAszx3mt9CNIpSGQWI7iuUv3cL/7NN1vba9b7CK3hLC2B0mO1/bwknSZlwUsDNJakupiCfe+u+Cl06sl6fzZOZzz54kcPHiDR/3OUv3a76P80HKvGhZKCLR1Jz8hBN56i8GMQseh0LVWGId66FIXCISpo5k6QgTUKFLVbBoNH7NYIVaukQh8dBWgywBNSXQV3q/KSBmYh5gRJRnLktp/mK4jx4k/eojHe58Lo4KdAMMS3J3J8cBz5yjWTm+7b3o6Rez+B4jdcxwttm7+bOEcvPo3UF0T49CjaBG4CYK2BwjidxC8EiArF3ccsRluVCAvL6f3Fly5NxKhb23NmrM14vMx9ib7KLhlZmuLzNfy+OuLVqyzZifmLzBmjTDvKpbbhunYf5T2hVkiMsAIfDzDpKEZzHf3MDM0gHDLLJ4vo2uCzqRNdzqCqa9z99v2qnUai8W2GPnbpP+ejUKqFLLs4s01o3cX66v/K6tozbnRLdBiJmZXDKMzhtERRbO2Xo/AB7+OV62xPF0kP10lP1vHqfnMVOcJEOiGBKWhNAtLq3O2eG9LWG9Cbglhrf5wmg4nwUci9/CSe4GyqqOZBv5inTPT09xx+DDOm2suwOpTT99SwhpcepnaCy8DoXboUuJb9ob0EaUUpmliWRa2ba/eh7d7sBqLMPED5ksXmKbKuKqQF+vyR4UGh7oRsUES+QbJmRKpmRKRxeqqYNm6HZbDs1MkzSTGylzbVJH61Peof+d7HDl1ioNjY2Guo6ah9fcjH9w6z9Ds7yf+0ENEbjuC0NedzBoleOPvkGOnCdwogdtJ4EYI3CjSjaCsLOSGoRyDcn3Lz16PZusoT4Kx0cI0MhHsA9lddeW+k2xlzUbnarTPtuMW6iw0lpmpLpJ3Ns5Pu4GklHfQFLSVG/hH7mR8Tw9VWyA1hSbBrDnU0l0Idy0eIpCKmWKD2VKDjoTNbcP9HDkwTHd391u3TneAM16i+vws/nwNYWhoKQvN3EYMt0AYGkZHFKMzhtkVQ4ubV/Q+KKko5xuhkM5UKS3Wm/8SUdCjkATXCDDUUjO4SaPhS+JGgZLRaiRyM3JLCKtsBIioAZ5kn9nLafciKpAoR1EsFikeOkhknbC6Y2O44+NYg4M3cNTvEEpR+dqXUDL8V16wEqTxWNBiEAQIITDN8MTw4Q9/mHvuWbuS96THRGmC0eIoI/URZrIOyo5DYSkMANmwHQmlaVR5jnKqj/KxHor3H2I40sdwOUrXvIcxMbdWx3kLSs8+ixxfl/oiJXJ8nBKQWhFXTRC57TbiDz2ENTAQbtqX+MsNgkKDYORVgpHXCeoGKrhz4wZ0E7J7INGx8+OnCayhJO54JUyDiJugQnd46rGh92zy/lbWbHK+xsBsjcp0gdnyPNPVRRZrFSrORhdnTdSoRWHliirQIEjYZJRHtOKzPhVZFwZJPUPSyeItm0ydmUNlSsS2s/reJn7ZxZuqhBdAuoBAoRbqV54vF6BnbMzOplWai4bTAVfAqfuhkE5XWJ6t4W3ReWc9ViKKX29D90sYqoFlgdt2jFRHq/XlzcgtIaxGLhIm3Rcd0lqcDi3NgldAj9gAXFha4q7hvQQjo6vvqTz1FLkvfOFGDfkdwz97gtqrYV9RXwhG4m10DQxhWXHm5+cJgoBYLMZDDz3EsbuPMVWZYqQwwmhxlPHyeNgyaz2xbBjgU1sMXcTrIi4tNAa1CMP1BntVha5DH0LseXhDkFNQKuGOjOCMjOKOXCQorbUCk5PNAJj11oqUyMlJRMQmevxeokfvBqIERYfKszMEJQdZ9cCpQv5iM21ii+CWRBdkhzZFpK5Hi5mhKzdloafCoKIVK3RDVHAuQux453tWVLdivTUbD7pJzA0w/cwY5uQiaT9PJSgSNJsNNKiFnVnWRZVJFBVVoz/eQ8PzUX6EhJYjriURK119FOQrLvmKSzpq0puJkoxc31NYsFhHaGJ1vjz0QshN8+UiamB2xppiGkWzrzwOGUiKC3XyM1Xy01Uqha2rgW2FrmsM3t7GzPkCsXQb0aSJ2whw6z7HHmo5gm9GbglhjR3vpPTkWFiAXEr2iC4WZRGa5dYajQbze/fStk5YnbPn8ObmMbvew0WupaTyt3+46oodj2bx4ikivUNUSpeYSEzwRvoNrKjFZGWSJ59/kkawg0ovAoi3o8U76PcD9i5PMSwFvVoEY+UkGQTwxtdh9Ptw8GPQfx9oGnoqRfTYMaLHjoUtuJaWcC6O4I6OgJRomSHMofcj4p0ot4LMX0B5DtH7PoesBVRPFoB1AS8yCAW+vE3fSiseun3t5NrwTS2cB10noFdNaxlM3VJCeiVmKw3+4tUpljQJgzlsP0O25pMsF8ipMvP5iW16BSn2JLvpjXcS0SMsVBxmiw3cy+czgWLdo1j3SEQMetIRMjFrmzIS18a28+VusOraNbtiaEnrqsFl9bK76t5dnqsRbLEf25HI2OR64uR646Q7omi6xuxokbMnZikt1Um1RTn24QG69757anbfStwyRfid8RLl70/iTpbRbJ2xSJ6JYB6rPxnOo2gad41PoM+snYCjd91FptnL9b2Id/oJFn/nN0FBTTN5ITuAeeRuJqIuryy8wnx8nvn4PIEKkEpyR/sdDGeGr/iZXbEu9qb3MpweZjA1iK3bYeWaS0/Bhe+sVbO5nEQ3HP4x6L5z29KAZx/+JPbhn0KY0Wa1Gh2hGQTFcTKf+tjmN9QWIX9pre7seoQOuQH03j1hWst6Ab1BEbnvBV4YW+bvTk/hXVY5Kmbp/My9AxzsSPAbv/5v8KS/2mN05d5A51/cubGDklKwVHWYLjRoeNu7S6OWTk86QlvcvqbKkpezGuFtamEQXjQso2hkI2Q/feCK7w08yfJcjfxMheWZGrXyNqkzW2CaOtme2KqY2rHdqQDXKsL/znBLWKwQWhTWF45QenIMWfUYlgnmZpcJig5GWxQpJVMD/QyuE9b6q6+Q+NCj781G6IFH5et/tlqF7mKiHeIp9K5ezo09gau5LMWX0DQNDQ0v8LhQuLBJWDN2ZlVI96b3Eje3KO9nWLD/R2HofWFqy8j3IbjMFVaZhVN/FFZwOvzj0LExeEw6PrGHfxFZkWst01SAkmD07Nv4WX4D8iNQX7NcNdNBt+rhrbcf/dhH0No73tXBRDuSLcIbAAAgAElEQVThcivn0EPdu2LleIHk71+Z5rnR5U2v9WejfP7+QbLx0JV6/PZjPPvqqbCDj9CQKizof8/Ru8n85P5N788C+5TizEyZ759fYCK/zcUZkmzM5+H9HdyzJ7shkninOBMlyt8ZR9gGWlRHNQJkwyd2z+a5TKUU1YKz6t4tztfXUsiuggCSbRFyvQlyPXFSbZH3/G/xVuKWEVYIk+EjB7PUXprH0HSG0/2cKYyiZ2yErjEnJe3ZDLGVsmxSUX3mGdKf+MSNHfgu4D33DRqXwso4S1acZSuGPXwIIQR1v85sahalrZ0kdKHT8BtEjegGIc3a2Z1bd2Y0rK2755Ewb3Tsmc19RQvj8OzvQftBOPxJVGYQd7RE/fVF9HQvsIQsV1a6s6EloujptvC9SiJqU+i1s+hWBT3ZFFKrEVawiebg6Geh6/a3fwDfBcyOFnnum6N4jYAgkNRKLrOjRY49NsDeOzuwrtP85HLV5cvPjTO5vDmC+oG9OT5xZ88GkfvoZ8L/pxdffxlPepiayfHb71pdvhUCwW39aY70pRhdrPL9cwucm9tcZjBf9/nGqzN899w879vfzkPDbUSuIaI3MpRGPDa0Yb488UjfqpvfcwKWZ6urLl6nvjn3dDvsiEGuN7RIs91xTHt3ArBa3HhuGVfwCipQlJ64hKz7KKV4bv416rG1YtaJWp1Dr7++Ol8jDIOOX/s19MRbL7R+0+HVyf+rn8eZyhMgOJUdwMt0YB9/HxLJ30z8DedS5zCN0B2lCx0UpCNpvvmT37x+btJaHs5+GyafZ6skUd+JUmvcT2DuAzO2uRBD88SkJyzSDwr08W8gGjObXYFCh30fggMf2Viq7j3O9798lvxMdYNLMvAlhqnRsz9DJGaQbIuSyNok2yIkc5FrFtuzs2X+6vkJ6pe5aU1d8KljfdwztHvenulCne+fW+DVqSLbncZsQ+PB4Tbev7+NZOTa3atKKkpLjaZVWqG81NhxaWdNCNKd0VBMe+LEM/bb+t85Pb7MV1+YZHSxymBbjJ+9d4Bjg9d2fFuu4HeGW8pihTBNwD6Ypf7yAkIIDqQHeSl/Nqx2owvKEZulRJz2SphPp3yf2slnSf7oj97gkV8/3Kf+CmcqD8BkNIOjW9jDhxFCMFuZxe63kSWJF3gYwsAyLRzp8Iu3/+L1nXuM5eDuL4Ru4jPfClt0AUpq1PM9OIVOwmoRpyHegZ7pwZsNEKbA6IoiEMhag2Tfa5jnnwk/8/Lh5fbBnT8DyVsverK0WMepbawmpekCt1mzuFHzadTKLEysRV5H4ibJXGRVaJO5yJaWlZSK756Z57tn5zeJWlvc4gsPDtKT3t0evb2ZKJ+7f5DHKg5PnV/gxbECvtw4GMeXfP/cAj+8sMi9e7I8cqCDXHz7iyvfCxh/I8+ZZ2YoztcQmiDZHiGWsnc0pmjCbAppgmxXDP0tFv+ouwGLFYeFisNi2eHF8WW+/eosAHrTZfzbT57j1x47eM3i2mL3ueWEFcDek6JxJo9yAnKRNO1WhuVSDSMbQWgak11dZCsjqy2oas81G6HbO/vnuplR9QLlb38DgIZmMBHLomXa0DI5Gn6D+cg8Q51DBFbAhcIFAhmQtJN88cgX+fTBT+/OoJLdcN8vovKX8E5+l9oFifIvsy6qC5jaPJEDHfhiL34pQDcLJFJPYzdmN3+mlYDbPhVGG9+igUhmRMedD9CNtZO7DBRWZHsXZKPq0ah6G8Q2GjfXhLYtghY3+NrL05yf3+yKva0nyWfvGSC6S3mmW9GesPn03f186HAXz1xY5ORoHueyCFxfKp4dyXNyZImjnUnu6U6TRKNe8WhUPOoVl3rZo7hYY2GsgmYINF0gPcnCWIWOIbYUV93QyHaFQUfZnjix1M49IoFU5KsuC2WHxcrabaHsULmsatfzl/JIBVbzu9Q1QTJi8rUXp1rCehNySwqr0DUiB7LUX1sEYH96gJMLr6HSNkITeOk005bFgBu60GS9ETZCf/j9N3LY1wX3e3+BOx+eNEfi7Uihr1qrU40p5IBEIBjODHNX51188dgXsXbQ//PtElQ96m+aePVHoK0Y1mld16rLjBeIdUygNaZheTQMUNJNyO0HLivoMPi+sObr2+iT+l4gnrGRIyVAoukCw9DQNJ3ufWk0IXYcaFOvetSrHvPjZaoNn/PzFRo6xCMafkQniGoEUZ2P3tnDBw6037CI6nTU5ONHe3hkuJ1n3pznhXOLOFUfzZVonkJ3JZonGX+jwjgzZKImvekoiejaabA4X0czxOrFiN7MYy3O11eFNZGxV927K6kw26GUouL4LFYuE9Cyw1LVRe7Qr1x1fKLr5oobnqQnY1whkKvFjeSWFFYAe2+axtk8ypPEzSj9sU5mSoVmIJPOTGcnXZOTrEhK9cQzYSN04917yFR5jvKT/wBA3oyyaMXR2zrRU2kKToFCurChYsxHhj6y66KqAkXj/DKNs/m15t6RdJh2U1tCq44QTZ/BihehsgDTLzWbfZthdafpl6D37rBaUqovDE7KXTkl6FagVnLxPUnHUILifB23EdC1J8XRD/bTvTeNDCSVgkMl36Ccdygv1akUXLaNuVAwX24wtlQLGzMEoLkSs+Rj6hr7O+JEzpR5c8FbdSEncjbGNQQO7RSlFG4joNG0MkOrc+2x2/AxgfulxYKnmC17myxYgELdo1D3SNoGPZkImaiF2wgwrI1CaZhhKYvDD/aQ64ltmQrj+pKlamhtLjVFdKEpog1v5/mr2xG3DRxPYhkCQxMIAZWGz0BuF2olt3jbvHtV4hr52qs/5A9f+QozjXN0Rw7yS3f+V/zY/sM03gznGvem+phdWEKlVFh5paOdSzMzHFxphF5uNkK/99077+888Sd4+ToSuBjvAE3H2HsIqSTTwTRk1tYdSg5xe9vuRs96CzVqpxeQW+X7CYgc20/k4L2IuRfDIKfxE6GorgQgrdwvj8L9v7xln9RblalzYdpLLGUTS9mk26Mc/+jQ6uuarpFqi5JqW5sHDQJJddmhnG+Et6UG1aJLEEguLVZZrG7+npK2wb7OBJahUSu71Mouc5dKQDjdHU1ZG+dss5EdzTvKQNKo+tTLLvWKR73sNl22Ho2yS7ADU0/TBF3pCJ2pCEsVh5liY1OQFUDZ8SnPVYiZOnFdIIQgEjOwIgZ2zEDKcO60ezhFoeYxPldmoemyXay4LFYcCrXtW9q9FXQN2uI27QmLjqTNnX0p/valKdriFumYSaURUG54/PIje6/rdltcH24JYf3qqz/kN57/P/FkFQTMNUb5rVP/Ae3YF3nU6EH5ElMz2BvvZaQ8FwYyGQZLHe1UZudYaXFc+eEPiR4//q5shK6Wxyh//ykApqIZ6rqJ3tGNnkgwW52l3llfdeEJBB/b+7Fdc+lJx6f+6iLueHnL1432KLFjHegrc1oD90Pv8bBKE3rYjmuFZFeYxrPv0V0Z6045Pb7M116cYiJfYyAX4zPH+27Y3FfgSWZHNhbC7z2Y2WbtNXRdI9UeJdW+JrZzhTp/+f1RCo5AN0z0RoDuhBZYdyrCQC627RS2IrSca6WNYhtLWUgpWZqq4dQ8okmL7uEU0YS1an06NX/H0bdXQwhoT9q0J2wKNZfpqkMxkASWQFoa0tSQlqBoaRgVn+TFKo7rUlUBUTTSpg63Z/jHb7y+qfDF2yUVMWhP2HQ0x9eetGhP2ORiFtplea1HelIbfmO//Mje1vzqTcotIay/++If4MoSmgh3V9IA4vzxmb/lY3f+rzTOhlf3ffFOpvKLeCmFEAKjq4uL8/PcKRUCCJbyNN54k+gd77I8SKVoPP6H+EUHR9MZj2ZBNzD3HsINXGatWUR07Z/43q576Y5f/yhapdRqTqrawj0mLJ3o0XasweRmUdcN6Lk7bPHmO2FT6lguPGtG2677WK9GIBWLzZJ7Jy4u8tUXJ1EKNAFzpQanJ5b5tccO8sFDne/4nOPcWAl/3fE1bZ2OweQV3rE1r00V+eoLkziBhNzalICtCT6xv5Me06S8VKecd6gVnR0JoQIWJssbAoQqBYe50RIdQ4kdR99eiRWLM5q0iCRMoglzw2Pd1LbNhZ0xJC8YHgfqgmQAeT3gpOnT26jTbkbe0ngsXTRF06ajed+eCAX0WnJsjw1mW0L6LuGWEFZXNjBEDMmaK6sml1logL0/Q+NCAQKFJjT2x/t5vTyOnrIQlkUl18bS4iLNtthUn36KyO23vatK3qm5Nyk/8wIAo7E2AqGhd/WjRaNMVy4R9AeIZp5K1IjywYEPXvcx+AWH2ul5gvzWtYatvWmit7dt36sSwobT3/t1iKRCS9Uphy3gHvrV6z7eFZRSlOo+s6Wwfdlcs43ZQtlZTe14/lIex1NYhoZU4AaKihPw64+f4cXxAoe6kxzuTrGnLYbxFqoBXet4p89trH7Uuy+Dfg3bDaTiH16f5anzi5te60za/NyDQ3QkVwQwPNEHnqRSaFBaCl3IlXyDWsndUmx3EiB0NQxDI5o0iSSspnA2HydN7Ji5ydq7nOGOBMMdCaYKdX6wLhd2dKmGE9d4Nb12vFxf4izVaE9uL6xCQDZmrlqfbfHwviNhk4oa76rzRYu3zy0hrO2RHvKOTiWYY6UQgVQu4CEsHXtvGudCWG2pzU6TLkUpKz9smdbdxejSEjml0ABvegb34kXs/ZtLr92UKEX9W39EUHEpGhHm7SQYFubQPspumaXUEmJdH9EfHfxRYub1C4hQnqT+5hLOxcKWjcL1lEXs7k6Mth3kPPbfA4/+b3D6y2HUcGYoFNV1TanfDnU3YG5FQEsNZosN5krOlvNy67k8YhPCAgnVZjTo4oUlfnhhCdvQONCV4HB3ikPdSRJX6YjyVigtNigvr5WLFEDvgau7gVff3/D4ynPjjC5ujja9qz/Np4/3YRubL350UyPdESPdsfbb8b2ASn7jnG2t7G4ZILQ+v3Zl3HbMWBXLSDy0OlesT8PSrotY9V2WC/v9s/ObrMiV7xIgaupNt621aoF2JG1ycestlVBs8d7klhDW//qOz/Bbp/4DJglcSigVoISPqSX4wfjzPHLgOM5IEWToAt4f6+PF6kVEwkSLRnGyGabzy/Q3P6/y1NPvGmFVE89TOfUGCriQCO1uo2cQYdtM1S5Bz9q6PfEe7u68+/psVym86Sq1VxZQW5R9E4ZG5EgOe1/m2mqk9t/ztoXUD+Rq55QVAZ0tORTrby0AZX3E5gpeoIhfJpyOL3ltqsRrUyWEgIFsjMM9SQ53J+lORa6LUExdZq229SWIJHZWcWh0scpfPjdOubHx+9I1+MTRXh4czl3TGA1TJ9MVI9O1TmzdAK8RUC04oMD3AzRdQ0lFtMvizg/2h27buHHFNJbrzUou7ImLS1ycryAJc0Wjhk4gJR2pCP/yE0c2factWmzFLfEr+czRMP/0j1/5Gybqr6AJk6QxSNLM8ocv/Rfu/OgB7KEUzmgY8JEwY3TV08yrajjX2t3N2PIyXQpMwB0dxZ2cxOrvv8JWbwICn9q3/pSg5jEdSVPVbTAjmAN7WKgvUG2vbjhRfnzvx9HE2z+ZBVWP+ul5vLmtc+zM3gSxO9vRdqmDxwpKKQo1b0s37k7zB69EKmqEATzZCE+8MUcubpOKGMwUGyxWHPZ3bJ9HqxSM52uM52s88foc6ajJkZ7QZTzcEX9L1o/b8Fm4LCBsJ0FLSimeOr/IP7w+u+m4pKMmn79/kMG26+PFMCydOz/Uz3PfHMWKGlhRHbce9ha95+NDtPUlrv4hu8jn7x/kt588RzJikogYVBo+5YbHL7xvT0tUW+yYW65W8D9dGOHfPve7KNbcTh/Ycxv/052/QPnJsVV3pRt4PM95VKTZs/XsWbpLZVbs1MiRw2Q/97m3sxu7jjz7XRZ++/+i4cCp7CC+pmEMHUYMDfKm/yZ+75plcmf7nXz6wNurrLRlTuo6tJhJ9K4OrJ7rX7ih5vpNy3PFCnWYKzW2zF+8VmxDoysVoTtth/epCN3pCDFr7US7VVTw4Z4UF+YrnJ0tc3auvMkS3A5LF+zvTHCo6TJOR3d2ATL2+hIjpxdWn0cTJg/8xPAVrcyGF/DVFyZ5fbq06bX9nQl+9r6BXXFZv1Ndd94KN1OE9/WmVSv4neGWuwT7kX17+e7FD/DC0vdWlz07cYYTg6c4PrgPdyw8wVi6yWDQwSW1GM619vQwUy7ToyAONN48gzc/j9l5kzZC9x1q3/4yshFwKdGBLzSw4pj9g0zUZvD6vNWAJVu3eWzosbe1uavmpB7IEjmcWy2g/1Y5Pb7MX5wc58JChaRtcKArgaFrlK6hy8h2aCJ0CXanQ/HsagpoNmZe1QW6XcTmHX1p7uhLo5RicrnOmdkyZ2ZKTBe3bxjvBoo3Zsq8MRNan32ZyOq8bH82uuVYlFRMny9sWNZ34Mqdh2aLDf7i5BiLlc3f2aOHOvjwka6rBgG9Vbr3pm8aIb2cVvRti7fLLSesQgj+uwc/yv/47XOU5RQAbiD581e+xeH7fhVjnFWrtU9rY1aVcISHlkwiYjFGqjWONj+r+sNnyHz6J2/IflwN+cY/UH19grJhM2unQAiM/r3UcFlMLCLMtRPmB/o/QMJ6ay64a85JfYuUGx5ff2mSP/7hGH6gMHXBYtnl3FyFo32pK0ZsbkU6atKdCkV0RUA7EvauRe0KIRjIxRjIxXjsti6KNY+zc2XOzJa4MF+5Yn7kVKHBVKHBP56ZJxkxONSV5HBPkv2didVAoqXpKo3q2hyxrgm6920vXC+NL/P1lzY3JI+aOj9zXz+Hu1Nvc49btLh1ueWEFaA7HeWnD/0kf/rmfyJopuBMFSr8+aWv80t9n8KfDDvbaEJjWHTxhpoI51p7eli+eJG8ghxQf/k0yUc/iJ7ZedTlO4JTpvoPXyVwJRfSzUShSAKzp5dL3iiqS61aq+3Rdu7vvv+aN/G2clJ3SMMLeH26xMsTBS4sVHhuNI8fqNVC5CvBQqNXSIWwDW2TBdqdiryjReK3Ih0zuX9vjvv35nB9ychihTMzZc7Mlq8YRFVu+JwaW+bU2DKGJhjuiHOoO4l2fmM+Zuee1JZdafxA8q1XZ3h2JL/ptd50hC88OHTF7i8tWrS4OrsurEKIjwH/DtCBP1BK/cZlrw8Cf0pYUE8H/hel1OO7Pa5PHt3HMxMPc6byXSA0Uk9OXOC+I+e5nd7V9XJenGwiRcEto2cyCNtmxHHIKhBSUT1xgtTHP77bw70m5Ct/T/XMHHN2krIRASEwB4bJ+yXKufKGKNyP7/k4hnZtP4Or5qTuSRG9o/3KOanb4AWSs7NlXp4scGamvKEN2JXSWnQNOhLr5kGbApqOXt2Ne6OxDI3D3SkOd6dQSjFbaqyK7MRybdteo75UnJurcGG8ROpilaipk42ZZGIWPfs3X+wVai5/cXLrhuT37cny43f1tlJGWrS4DuyqsAohdOD3gMeASeB5IcQ3lFJvrFvtXwJ/rZT6j0KI24DHgT27OS4A29D5+eM/wv/x9AhFeQkIrYG/nnyS/zn3BWJ5Y2UfGBbdvEi5mdfaTW1sjBmgF6ideoHEBz6AFr9JOqlUl6g8+U3cAEZTYUUiEUshOjuZkecQiTWROZI7wnBm5wXrr2tO6jqkVIwsVnl5osBr08Vti5avT2tJRgySEQMlFV3pCP/6x2/f9eIL7wRCCHrSUXrSUR493EnF8Tk7W+LMbJnzc5Utg7Hs5dDCrXsB9WLAhONx+uQoh7qSaELxwlho8ZcbPgPZ6Abr3tQFP3FXL/fuyb1j+9iixXud3bZY7wcuKKVGAIQQXwE+BawXVgWsTOikgeldHtMqd/Sl+ZG+j/D4xH/GJ7yKH89XeLznB/yU+uBq6kmsotPb0cV0YQ69rQ0xPc0lz6NDgel5VE8+R/JDN7ZW7QrBi39L7cIiY7E2PKGDpmEM7GPWncfpcVZdwIYw+MjQR3b0mbuRk7oSzPPyZIFXJos7ipi9byjLa9NFetIRsnF7NRXinz049J4Q1a1I2Ab3DOW4ZyiHH0guLVU5M1vmzZkS+aoHUmEVNrqOnZyJ6wY8+cYsr06VsAwNUxe4vuLVqRJH+6A9GSEXN/nCA0P0Zna3IXmLFrcauy2sfcDEuueTwAOXrfOvgSeEEL9KGHD74a0+SAjxK8CvAAwODl6XwQkh+Mzd+3l9/hEuOk8AYWL/M8WL3G0dYJ83sLruoN/OnL5IABhdXXiTk4wD+4DayWeJP/x+NOsGz00Vp6h87ztUMJmJhNcqIp7Bb0uxYJxDWGui9/6+95OJbD837IyXqL04j7dQA18hYgZGcvP+mb1xYnd27DgndaHs8PJEgZcnC1tGo15OLm5yV3+GuwYydKUim1IhbqVC5Iausb8zyf7OJJ842sNCxeGFF2a5dKlBuREWrVe6wE2F/9ajSzVMXVt1766fk37kYAc//Q43JG/R4lbhZghe+hzwJ0qp3xJCPAT8ZyHEHUqpDT4vpdSXgC9BmMd6vTbenrD51G338sevXGI5OAfAfNnhifRJfs7NkmxGyxrLAcN7hjg/NYLR0YE/M8N0ENCrIFpvUD91ivj73ne9hvWW8E/9DdXRPBcSvSgE6AbGwDDj3iyyW65aqxk7w/v7tm/a7oyXKD05hvIkQdUDX6KWG9CXWBXXa8lJLdY9Xpks8PJEganC9mkmKyRsnaP9GY71ZxjIbUwvaaVChAghwnJ6dYj0pPADSanuU80auHboFt5qTtoyBFFT4589OHTTzz23aPFuZbeFdQoYWPe8v7lsPb8IfAxAKXVCCBEB2oH56zqSyRfg5O9DcRI6bwsLujdL433gYAfPjz3Cs8vTeCqMrjzdWOSYPMP96m50EZ6ceuppJiIRGo0GRmcn3swMI8DtQPWZZ4jdf/+Na4S+eIHKD55mwUhQNEPXnkhkqaTMsIH5OhftR4Y+gqltb2HWXpxHOgGq7odSbGiAJFiqY6SsHeWk/v/svXl4XXd95//6nnPuLl3t+y5LsizvS7xlw0lsHNqEpGEJ0HaaKaGU6Q86odCHMAMMbYGZQvoAw0DD0rIOdAiEAGlJQnbH+75Jlqx93+++nvP9/XG1XUm2ZFuyJfu8nsePrr73nHO/15Lu53y29ycYjXOmK1HR2zIUuGQBzjg2TaGu0M2GknSqclIWrX/yZsI3lNDghYQ3m5Vi5R3vqMTmtNA2HKRrNEjPaGhCTcmiCvLcDkoznaZRNTFZRBbbChwGqoUQFSQM6qPA+6cd0w7cC/yrEGIVYAcGWEg6j8JLnwVvDwgFBhsSU1J2fRqKN2NRFf5oYwVdr99Jc+zfgUQF6gF7M4XeHMrciQHRRl+ImtVVnLpwJmFYe/sYwmBUQrrXR+jUKZybNi3o1ueFlMQP/BR/m5fm9LEwuWpBLamgR/TClIlhlWmV1GbWXvZysR4/RjCW/OGrCpDgvrf0kj2p0bhBfW/CmDb0+dDnED3SFEFNfiobitOpLUg1K1KvkOm6wJmFLhwpiYhCRbaLJ+6r4akXL2CzKFgUgQQCEZ1HNhXdgN2amNw6LKphlVLGhRB/BfyORCvN96SUZ4UQnweOSCmfAz4OfFsI8V9JFDL9mVxoncVjP4DAAIwPKPf3gT0DTvx4wmutyUtle+kqvK3tDOpnATgZGWA9HaRH0kmzJZrtM0dtpKenMzo6ipaTTby/n4vAJiDw5ps4Nmy4/oPQe0/hO3CcdkcG0bHWGZGWxZAjRihncoC5gsLe8ssPMNcDMYyIjh7RiUQMDN1AsSjYMu24SlJnGFXdkDT1+znZMcq5Hu+cEoJCQGW2iw0l6awuTDNzfFdJNBynvy1ZlKOoJjlEvqE0gyd21yTlpP/irptHns9kYTh69GiupmnfAdYA5t3t3BjAmXg8/sHNmzfPGlld9LjlWE/q89PWPjPl8Tng0gm/haD/HBh64lN9nNAwtO2DeBS0xF3+H64tpKF3G75gJxHpIS4lB22dZHvd1GXVoSka8e4Aq7bUsP/EIbS8POIDAwSQ9EnIHxwiUl+Pva5uUd9OEoZBbN/PGO4K0ZkxFnXXbFBUTK+jG2GbfM9bC7aS48y55KWkLgkc6iFuU4n0BdFl4k4nhEF4MIyyNZ8MEhW97cNBTnSMcrrTQyB6+bFqAMUZDtYXp7O2OG3e2rcml6a32YMxpcfX4bKQOUu+28xJm8yFpmnfyc/PX5WTkzOiKMryE4+/zhiGIQYGBup6e3u/Azw42zFLoXhp8clfB0MXwd8D4zVReizhwb71Ndj6ONjTSHNa2F1XiOfk27gYfQ6J5KwxyBbVS4qvncq0SpDg6JMUFhbS3d2NmpmJPjREi4BsCf7X38C2atX1y2F1HMR75DwXU7ITBUuAkp5DjyOIkTNZsJRiSZlzgHnozAD6SASPN0ZUgkMkbs1CCEbsGu0NI5xOVTjZMcpIcO4Ra9kpVjaUpLOuOH3KYGyTa0Uaku4LybrAhdVXOH7PxGSSNaZRnT+KosicnBxPb2/vmksdc2sY1g3vT+RU08vA0wHRIOhRyFud+P6Np2DrhyCtiJ0rsjnaVox3aD39+gkQ8JbWQUbIQbptmEx7JtEOHyvvrKa3txdLfj760BAxEn1FFd3dRFtasFXOX3jhqolHib71/+gZMhhJHRvrZXEQyc9mOL036YP23tJ7samXNm7RTh+Ri4mxeSFfNPF+4gYNMQMBaKEYui/MYOrlQ71uu8a64nTWl6RRlD67YLzJtTHcEyA0RRdYmUMX2MRkDhTTqF4ZY/9flwyb3xrx9OLNiUKltBJw5YIzEwo3QspYWDQ8Cvu+Cn1nURXBQxuKyNU24hAJ5aImyxADBGn3thM1oiBB6QizYsUKFIdjQiu4U0AYCLzxxvV5X62vM3qsmYuuMT1gASIjh+4U76TkBlCcUsz6nPWXvIzujxI8lkgVxCI6MaC/3vsAACAASURBVN3AF9NpiOjEdUlUl4QiOvGYwWzlvQ6Lym3lGXzwzgr+dm8tf7CugOIMs/J0sZhetJRblorVfmvcI5vcnKiqurm2trZu5cqVdXV1datefPHFBZWye+SRR8r/5V/+JQPgve99b9nRo0evbGrHFXLr/DUWb54oVCIWhmPfT+Rex9EjcOjbsPphyivv5rbybKItb6Mx+ixS6BywdPIH0WravG1UpVcTafNSeU8Z7e3t6Pn56KOjSKAFWHWxmVhXF5aiRay+jAaI7HuWZp+V8Lg4g8WJLyeFYM7whFETCO6vuP+SRk7qBoGDvci4gZQw2h8kBDRGDMZ9U3XsOqPSwDoSI5ppxaIKVhW4WV+cTs3Y6DaTxSfkjzLcHUham160ZGKymOy/OOj82eGOrG5P2FaYZo+897aSoR0rsoPXck2bzWbU19efA3jmmWfcTz75ZPHu3bsbFmbHyfzsZz9rW4zrTuXWMaxTsdjhtsfh7C+gdap3KRNrgQH2rn6Qc91e8o3N9MQP0ewYoj9UhBoRDIYGyXFkE2/1U1tby8mTJ1FSUzF8PgYEFEqwv/EmGY++d9Hegmx8kf5TXXQ4x+bBCoHMzKEn04uwTxrRjbkbKUwpvMRVIHRqEN0TASDoiRAN65zU4wxYISUGigRdgN8KQSFZ5ZdsvCufdRWZ2C1mRe/1pvvCaJJMc2qmndSsRb35NrlF+Oj/PV421zFD/ojW0OdzaoqQmiJk+1DA/mbTYPrKvNRgVortsrqkX3vfxnkZNI/Ho6alpcXHHit79+6t8ng8ajweF5/5zGe6//iP/3jU6/UqDz74YGVPT4/VMAzxyU9+svvxxx8feeONN5xPPPFESTAYVDIyMuI//vGPW8vKypIKQrZu3bryy1/+csddd90VdDqdG//8z/+8/4UXXkiz2+3Gb37zm6aSkpJ4d3e39thjj5V1dXVZAZ566qn2PXv2BGbb72zcum6GosDad8GaR4Bp3lzrG6Sc+B57a9PJVtfgUvKRAo7Yu4nGDTp9HUT0CJEWD0U5Bbjdbiz5+ROnNwOh8+eJDw4uzt5DI0T2P8+FSArG+N6tLkayrcRzJn+37aqde0rvueRlIu1eIi2JvKoeN/AMhWiPxekSEFKhOUPwaqnCaKqC4lDJT7NTk5OCszdqGtUbgK4b9Izlwccpqk43Q+4m142OkZBNU4S0qIoUQmBRFakpQnaMhK6pOjESiSi1tbV1FRUVqz/2sY+Vffazn+0BcDqdxm9/+9umc+fOnX/ttdcuPPnkk8WGYfCLX/zCnZ+fH2toaDjX2Nh49o/+6I+8kUhEfPSjHy391a9+dfHs2bPn/9N/+k+Df/M3f3PZsGEoFFJ27Njhb2hoOLdjxw7/17/+9RyAv/iLvyh54okn+s6cOXP+l7/85cUPf/jD5Vfyfm5Nj3UqFXeBMxuO/msiHDzOwHm2hEY4lbqbmPcuLkR/SbNziH5/AUVqCq2eVmoya4he9LB69Wre8nhQnE6MYBCfgH4pcb75JukPLfwgdFn/77SfHWHQmsgBoyjEMzLpy/ch1MkP2V0lu3BZZk9V6L4ooROTOhye/hAD4ThnMUi1aQwEozTbwe20YDgtKH0RqnIS8o4DHT4GO/1kF1/dcHSTq2OgzUdsSmuTxaKSW24OJDe5foSiumrTRFIFo6YIGYrq13SnPTUU/NJLL7kee+yxigsXLpw1DEP89V//dfGBAwdSFEWhv7/f2tnZqW3atCn06U9/uuQv//Ivi975znd69u7d6z98+LC9sbHRcc8999QAGIZBTk7OZdsXLBaLfPTRRz0AmzdvDrz00ktugH379rkbGxsnplP4/X7V4/EoaWlpc8jeJDANK0BeHdz+MTj0dKKQaQzF38t75c/5pn4PPm0bXfE3OeXqJSNYjhB++gL9FDSrZNaUU1BQQOfICNHmZgBaBWSfOEHqrl2oaQtYsenrJbj/ReqN1ETyE5AWFwO5JCbajpHrzGVL/pZZLyHjBoGDPcgxMYeQP0b/cIjTGBiKwG5VsWQ7SVV03A4Ljkwnt2W5STEmjXbj4V7S8yrQTM/1ujG9aCl/RRrqZWQlTUwWGodV1aNxXbGoYiIjETekcFjVuZvZ58l9990XGBkZ0Xp6erRnnnkmbWhoSDt9+vR5m80mi4qK1oZCIWXdunWRY8eOnXvmmWfS/vt//+9FL730kvc973nPaFVVVejEiRP1830tTdOkMiboo2ka8XhcQKJX/9ixY+edTudVVUubhnWctCK48wk4/B0YbZ9YThUh3hX+Jb9R7sFrKaHJ2claXwF2Q6U70EWazY3t4iirVq2ir6+PWFcXMhIhAnQaEvf+/bj37l2wbcpzv6ahMUxIHTPWiko4I43R0mhSSPD+8vsnxt5NJ3hyAN2bmCwjDUlXp4/z0iA45u3qdpXilWn83duqSLElfkV8w2GO/kcb46JY4WCclpODVG/JW7D3ZnJpvEMhvEPJAwwKqy89ncjE5EqZTw50/8VB51devFCUatP0FJum+yNx1ReJqx/fXdN1rQVM4xw/ftxuGAZ5eXlxj8ejZmdnx2w2m/z1r3+d2t3dbQVobW215Obmxj/ykY8MZ2Rk6N/97nez//7v/753eHhYe+mll1z33XdfIBKJiNOnT9u2bNky9+SPadxxxx3eL37xi7l/93d/1wfw1ltvOXbu3Bma7/mmYZ2KPQ12/H9w4kfQc3JiuTTNwh1dv8biuo1/t1o4l9JHaqCEdIeFFk8r9iYnGVWVVFRUcKG/n2hb4vezQ0DB4SOJQehO57Xvb7iF0QP7aJ4i/mtYXfQXxRGOSaO6JmsN5Wnls14i0uYl2uad+L6j00drJMaANnm+UuniP99ROWFUIVEkU1KbQfv54Ym1roYR8srduLPNeZ6LzXRBiMwCF073DR5TaHLLsWNFdvDj0DW1KvhDd1X2XqtRHc+xQsJb/OY3v9mqaRof/OAHh++///6qmpqaunXr1gUrKirCAEePHnV86lOfKlYUBU3T5P/5P/+nzW63y5/+9KcXP/rRj5b6fD5V13Xxl3/5l31XY1iffvrpjg9+8IOlNTU1dbqui23btvl27tzZPveZCcRCy/JeD7Zs2SKPHDmyeC8gJdT/Bppemlga9Ee4OBBgv6uAV61RHupbR47Njk1TKHAVsGL7OpRyFy///vd4jx5FxhKh/TwJm3e9jdRd1zgIXUrkG1/l9WeP0cuYkVYt+HKz6NwOYswwWhQL/2XDf5nQNp6K7o3gfaUD9MTPvG84RHObl7NWgTHm7cpcO489Wkeee2alqR4zOPzbliRxgpR0G5vvLzen0SwisYjO/l80oU+RMFx7d7GZ4za5YoQQR6WUSTmikydPtq5fv36RKi1vXk6ePJm9fv368tmeMxM0syEErHoA1r8vMQ0HyHLZcNst7Aj0sD4yQIOrJzFcWkJvoJfBcx1YFI3aVavQcifDo30C+vYfwIjOPdT7svSfo+vwyUmjCsStTvrK4xNGFeDOojtnNaoybuA/0DNhVEeDUTo7fTRZJo2qsCq89501sxpVANWiUL01OfTrH43QWT886/EmC0PvRU+SUbW7LGQVLmj/vImJyQJiGtbLUbodtn8ELE6EgPIsJwK4NyiJKoeJESEQjSORtPQ3E2geprS0lIzKClAni3qawmGCR49e/T4MA+P0rzjVPeXHpVrxZajEiibDtZn2THYU7phxupSS4PF+DH/C0/SF4zR3eGkTEFLGhSTg3rdXUJF3eS8oqzCFvGmVqK0nBwn5r/HGwWRWpJR0NSYXLZm6wCYmSxvTsM5FdjXc8V/BmY3DqlKQZseGyu0xjSHtBJFImLghiehhGo+eREjB6nXrsOTmTlzCI6DtjTeQ+lUWznUdpf5oCz45ORUmbLUxVCuSCpbeXv52NGVm2jza4iXakRgxFozqNPZ4GYoYDKqTRnXd2hw2r59fIVLV5lwsU8a96YbkwsE+lmNaYakz3BMg5E/WBS4wdYFNTJY0pmGdDym5CeOauYLCdAdWTaHIcKBZu7Hro4QCfgCGRgdoPV9Pbm4uBXV1E2FkgKZAAP+JE1f+2nqc8MlfUz8weS2p2QhkSuJZk8UrNRk11GTUzDg9PhoheCrRrxqJGzT0+giGdNqmdMmU56awa3f5vLdktWus2JSbtDbcG6C/1XeJM0yululFSzmlpi6wiclSxzSs88WWAts/glq6lbLMRJ5zg+6k39aOFvMRDSYqbS8eOU0oGmLNxo1YcrInTg8DF1597cq9urY3OXmqj9h4D6mAkKYxtG6yElcVKnvK9sw4VcZ0Agd7wJDEdIP6Xi/RiE6zISfyqsXpDm6/uwSb88pmpOZXuknPTa50bjraRyyyYO1stzxhf4yhLn/SmqkLbGKy9DEN65WgarDhA2Rseoh0pwULCoWWYQxhYET8yNAoBHX2Hfk9qamprNi8OWm4eovXg/fMmfm/XizE8NF/p2148hq6ZiOQG8NImfRadhTuIMuRlXSqlJLAsX6MQIy4IWno9RGJGrTHDMJj+bk8t52VKzKuqh9SCMHKbflJ1cDRiM7FsSk5JtdOd+M0XeAMG+5sUxfYxGSpYxrWK0UIRM3byX/bX4CikY0AywBSQjwagvAIoXODNAzVs2rTJuxZkwZPB878/uV5e62y6fccq/dNzGZHCCKKZHjDpCF0W93cWXTnjHMjzR5iXX4MKWns8xGM6gxIyXgZTLbLSnmWk5Xb8q+6EMbptlK2Jtmg9zR7GOmdt1a1ySUwdIPui9OGmddkmLrAJjcl42PjqqqqVq9cubLus5/9bJ6u6zzzzDPu2trautra2jqn07mxvLx8TW1tbd3DDz9cfqP3fDnMZM1Vkla1DWO7SuzAt8nVhvBE84npEjUexTFq8OaB5yne8xes2rmT4889N3Fe58gwQ2fPkr3mksPnE4S9tB15hSHPpDRlRLPiL9aRtskE6Z6yPVjVZKGA+HCY0OlBpISmfj++cJyAAt1BHYQg3WGhIieFklWZpGZemwdUWpdJf6uXgHeyKvjCoT62/EE5qjlK7qrpb/clhdU1i0JemakLbLIEaHnDyfEfZuHpsJFWEmHjnwxRceeCjY3r6urS3v3ud1d6vV71n/7pn7ofeeSRc5A8lWYh3sZiYhrWa+C2jZt4uu8/U9P2f0mLjDKopxGLG9g0neymEL/N/S6PbPovNL65D//w0MR5J195hXtWr76s9xGr/w9ON0cZH4oqhcAgzOi6ycrdcnc5dVl1SecZUZ3AoV6kIWke9DMajBEX0BozkEKQatOoyk3BkWKhfG0214qiKqzcns+xFyZFSYK+KG2nh6jckHPN179VmV60lF+Zhmoxb1RMFpGf//mcY+MIDGr0n3WiWCSKRTLcaufiy+nkrg7iyr7s2Dje9d15jY0rKiqKf+c732nduXNn3Ve+8pXucS3f5cTy2/ESQlMV9txWx+u5H8Dj1siSFgwJcUPiiqbQe+YIZ87+hHV3JYdqBwcH6bpcrtU/wPnDBwh6J9sswqqGv0rD0BLeqoLC3vK9ScZZSknwaB96MEbHcJChsd7SZqtCLCpxWFRq8lJRFEHNbXkL9kGdluOkaFqetv3cMIHRyCXOMLkcvuEwnsFkWVKzaMlkSTDSZkOxSFSrRAhQrQkDO9J2TWPjplNXVxfVdZ2urq5l6fyZhvUaqcpNZVVpHq/nPUDE6caOQtwwkFJS4l3Bf5z9CaniHFnpyYbn1GuvYRizTyDynvwVTV1xxitXdCEQ+BheNelhbsnfQp4rue800jRKrCdAz2iIXk9CHrPLpRLwxrFpCivzU1FVQW5pKllFCyuHV7khB9uUNhApJQ0He83e1quge9oUm8x8UxfYZIkQC6goluQ/asUiiQXMMVdTMA3rAvCOtQVYLRb2FazHppaBhJguSY2mY4tk8OvGZ1ibH0xoEI/hGxjk4qlTM64lR9o4deIcMd9kzjIqwLs6BTmWs3RqTt5W8rak8+JDIUJnBun3RegcSXg7XptCT1jHKgQr81OxagqaplC1ObkHdSHQrCrVtyUbes9giJ6m0UucYTIbsahOX4s3aa2wxpxiY7JEsLh0jFhyDsuICSyuBe2zO3funFVVVYqKii4fXl6iLEs3e6mR5rCwuy6P35zqoSOthEyvTlzvwDAkJd5qztgP0G6tpyjmpEvLAUUFJOfeeouyujqs1klvpPvgs/T2xSa81aiioKpehquqJo65t/ReHNpkH6sRSeRVh/1RWocSFbkxRXDBoWLrjbOyIBX72NzUyg05V9yzOl+yS1LILkphcErv5cVjA2QVpSzaa95s9DZP0wV2amQvcHTBxGRW5pMDbXnDySt/X4TVrWNL0Yn4VaJelV3/retaC5jG6e7u1h5//PGyxx57rH855lfhFvJYe1s8/MfTp/nZPxzi1Z/U09viWdDr76jMoiDNTlu6htBKiKl5RAxwhzNxRzJ4ITZAaUEQNTgEeiJ3Gu7vp/7k5Hi6eO85ztR3oI9Nj5FIDD3M8NpMGGuJKXQVsjF348Q5UkoCR3oZGQ5xccA/YZDrMy1YhqLU5KXiHBv/5s6yL+oMTyEE1bflJQ3fjscNGo+Yva3zQUo5o2ipoMrUBTZZQlTcGWTXf+vCmRHD32vBmRFbCKM6Pjauqqpq9a5du2ruvfde75e//OXuhdr29eaW8Fh7Wzzs+3kTQU8EoQoG2nx4B8JsfbCC/IqF0V1VFME7NxTyrdeaGXGopIVK8eoxNHyUeKs4m3OYF3O9rL0YoyVEYvarZqPpyBEqV60ixeWicd9z+IYiE8YxogisWpDRytKJ17m/4v6kgqXwhRFG27009vsnIs3taRqhoM6adCepjsSPeFzQYbE/pO0uC5Xrs2k8OmlMBzp8DHb6yC5OvcyZJiO9QYJTUgCKEBRWmWFgkyVGxZ3BhfJOx9F1fc4pJYcOHWpYyNdcTG4Jj/X0K52EfFEUTUEIQTSiE/JFqX+rZ0FfpyzLxZayDNrSNVQsOJQCPKTgjhWREkmjkzDxohA2PQqhUYgGifX3c/bkSfwXD9DYOjThrRpItIiXvo35E+pNG3I2UJxaPPF6sYEgIyf6udDnwxgLH3rsCu12hVWKhXTXZIi5pDaDlIzro9pTVJOBOyv5tRoP9xGPmXKHl2N60VJOaSpWxy1x72ticlNxSxhW/2gEVUv21GIxna4LIwuubbt3TT4xt5VRu4JNpKGJVHykUBLdDMArRWEKo2PzSyM+CI7SdeY0h15/gejwZItFBAPFbeApSXgsNtXGvaX3TjxvhOMMv9VNQ6+P+NiM1ZgiOJdtZQM2slImq9/troXpWZ0vQhnzjqd61sE4LSfNWcqXIhyIMdiZrAtsFi2ZmCxPbgnDmpnvwp3tRJuS+zN0iVAEx19sJxKMXebsK8Nl03j76nza0i0IwKXkE4sLXOESnM41xFVBc4GX1PiYEY2FiDefwTvoQw8lCuBiSOzhEXo2FU54q3cX302KNVHEIg3J8P5u6ttGiMYnW3bO51hZn5ZCpkj+sS5kz+p8ScmwU7Iqufeyq2EE77T+TJME03WBU9JtpOU4Lnm8iYnJ0uWWMKwrd+Sjxw1Ss+1oFgU9bmDEJWm5DgKeCMd+107Qu3CDum8rzyCl0IXXpqCg4VLy8UfiFPlXQu4qmsotuIKJvPywaqFJS+WUz85FRwbDqgVDD2PkWPAVJCTschw5bM3fOnF937lB6s8OEI5NGtW2dI3KqgyyRpOr0xejZ3W+lK/NxuGarAaWQMPB3omwtUkCQzdmtCUV1aSbusAmJsuUW8Kw5leksfWBClxuG44UC85UKzllKTjdiXBpOBjj2AttC+ZNCSF4aGMx7ekJo2IVqWgyFeeAE7vIQS9dT1OxTtiIM+BIw1AEqjQwFMGAI42w1OneVDThre6t2IuqJNplwj1+zrzeTiAyaUBH7Qqu1VnUhFV0fdJoLVbP6nxRNYWarflJa/7RCJ3nh2/QjpYmAx1+olN1gTWFvHJzmLmJyXLlljCskDCud79/JQ9+bCMP/80myuqSp7LEIjonft/BcM/CTGYpTHdQsyYHv3VM1EHJIxIR5HdngNVJ387b6LWnInQDoUvQJUKXKPE4g+48ArmJCtpVmauoTKsEIB6Icfy3TfhCk0Y1qgpCqzK4ryAzqX8UFrdndb5kFrrIK08Wj289NUjIt3ARguVO17SiJVMX2MRkeXNL/vWqqsLqO4soWJHsFehxg9OvdNLX6r3EmVfG7tX5DOYmvGIFFadSgLPPhS2iEUt1ElVtWKJj+V0BQoItHCUuEsZQE9rEAHNDNzj83AW8nkn9XQkMr3Dzru1lNE+bg7rYPatXQtXmXCzWScUz3ZBcONRnyh0C/pEwnoHkSIlZtGRyq+F0OjdOX3viiScKc3Nz19XW1tatWLFi9T//8z9n3oi9XQ23pGGFycrVstXJnqshJef3ddPZMHKJM+eP3aKyfUcJQUsipGsVLlQ9nfSOhEG3hyOAxBqLYYnFsUYj6KqCI5gwnncU3UG6PfEhe+CFZrzdyR7pSKGTd+2povf8COHgpBd7vXpW54vVrrFiU3JIerg3sGA3MMuZrmmCEBl5TlxpC6pnbmKyoBzqOeT81BufKvmzf/+zqk+98amSQz2HnIv1Wh/+8If76uvrzz377LNNH//4x8sikcjS+FCbg0VvkhNC7AW+CqjAd6SUX5rlmPcAnyPhhJ2UUr5/sfc19rpUbsjBaleTBA0k0Hikj1g4Tvm67GsqIllXks6Fcjc0JpSeHEoOGf1+BktGqWps5NzqNRCPo+px4qqKrmmsPFtPu203O4t2AnDwYCe+c0NJ1w25Ldz/4EqMQJzO+uSc5fXsWZ0v+ZVu+lo8jPRN9pU3He0nqzAFi+3W1O+OR/UZNxfmFBuTG8UnX//knGPjRkIjWuNoo1NVVKkJTXb4O+z7u/enV6dXBzMcGZfV9f1fd/2veY2Nm421a9dG7Ha7MTg4qC4H/eBF9ViFECrwDeB+oA54nxCibtox1cCngNullKuBv17MPc1GcW0mdTsLZxjQ1jNDiZDlNVSxCiG46+4yImNeq4KCSxbiakkl3z/AyoYzqHqcqM2GqsdZ2XCGfP8Aby9/OxbFwrELgwy8lazsZVgVdjy8kjSHJTFBZspz17tndb4IIajZmo8yxYuORXQuHrt15Q57W7zoU9qlbA6N7GJTF9hk6dLp77SpiiotikUKIbAoFqkqquz0dy5qmOXNN990lpWVhZeDUYXF91i3Ak1SymYAIcRPgXcC56Yc8zjwDSnlCICU8oZ80uZVuNFsCmdf70bXJz/suptGiUV0Vt1egKpe3X1IXpqDjDU5BI8n3ppFOCgdruTIXWVsf+4MBZ2txKwCS1RijRtceN9O3p+xknOdHi6+2IJ7imFXFEHd/SvIy3bRWT+Mbzic9Fo3omd1vjjdVsrWZNFyalIooqfZQ16Fm4x81w3c2fVHSjmjaKnQ1AU2WeKE4iHVptqS5l1qQpOheGhRwk7f+ta38n7yk59kt7a22n760582LcZrLAaL/QlcBHRM+b5zbG0qNUCNEGKfEOLAWOj4hpBVmMKG+0qSCm0goXV76uXOa5Lk23p7KcI+eV0XOXTkl/GrPelEHCquoEHEofLc2zM4vyOf1qEgB353EXd48ndYCFixs5iSqkzCgRjN05SMbmTP6nwprcuckUO8cKgv6WbmVmC0L5jUOy2EoGCJFJuZmFwKh+bQ4zKedPcXl3Hh0ByLolf64Q9/uK+pqens97///Ysf+chHyoPB4LK481wKQqQaUA28DSgGXhdCrJVSJlV1CCE+BHwIoLS0dPo1Fgx3toONe0o59XJHUkHQaH+QEy+2s25XyVXpt9psKuVbCmh5sxMAgWClt4xfbrjAqe2TodtcRy6Gr59fv9jEytEpilACymuzqdhaACTyk1PDiDe6Z3W+KKrCym35HH+hbSKEHfRFaTs9ROWGnBu6t2vh9CsvcPQ3zxLwjOJKS2fzHz7E2l17Lnn89KKlnJIUbKYusMkNZD450EM9h5z/+8T/LkqxpOgui0sPxAKqP+ZX/2rDX3VtLdi6oML8U/nABz7g+d73vhf4xje+kfWJT3xiyWujLrbH2gWUTPm+eGxtKp3Ac1LKmJSyBbhAwtAmIaV8Wkq5RUq5JSdncT+AXWk2Nu4pw+W2Jq37RiIce6GNkP/qejCrNxeQ6p701hy4qRmpnZhK49Ac2FQX0eFMKnsjSeeWFrqpuq8cIQSDnT4GOnxJz1duvPE9q/MlLccxo6Wk/dwwgdHIJc5Y2px+5QVe++H38A4Nosdj+EeGeP1H/8LpV16Y9fjZdIHNoiWT5cDWgq3Bv9rwV11ptrTYQHDAkmZLiy2EUQ2Hw0peXt668X+f+9zn8qYf87nPfa7nG9/4Rr6uL/1hHot9i3wYqBZCVJAwqI8C0yt+nwXeB/yLECKbRGi4eZH3NSd2l4WNe8o4/WonnimKTCF/jGO/a2fdrmJSM6+s8lZoCtXbCjnxUguGhBQlnzq/j874MAVZTnQDmvqH+cDQB7BMyasWZzqo3l2OYlWJx3QaD/clXXcp9azOl8r1OQy2+4mEE1EBKSUNB3vZuKd02Un5Hf3Ns0gpUYQARKKsXCTWZ/Naey56knp4XWk20nJNXWCT5cHWgq3BhfZODcOYc2zcnXfeGWxtbT2zkK+7WCyqxyqljAN/BfwOOA/8m5TyrBDi80KIB8cO+x0wJIQ4B7wCfEJKOTT7Fa8vFpvK+ntKyCpMLqyJhuOceLGd0b4r/93KXpVNfnbiejaRRq6opny4kpFQkL5RC+/wPkpubNJ7yU+zU3lHCVpW4oO35eTg7D2ry8wYaVaV6tuSb0o9g6EZg76XA/7hIaRMzhHruk5gdKZ0o6EbdDdO0wWuNnWBTUxuJha9fFRK+byUskZKuUJKVtJTxAAAIABJREFU+Q9ja5+RUj439lhKKZ+QUtZJKddKKX+62Hu6ElSLwpq7i2fI8sXjBidf7mCg3XeJM2dHWBQqNudjH6vclfEUqntvp+Xko7gvPkDZ6KRRzU6xUlmXg31s2LV3KETXNOGKklVLr2d1vmSXpJA9rdiq+cTAgk4bWmzisRhCVWF68ZWuo6jaDHWpwU4/0fDkjZGqKeRVJv9umZiYLG+WZl/GEkNRBKt2FlBSm6yoZRiSs2900d10ZV6WszqDsrxUwjGdoUAUiy65Q2psD8JQIEo4ppPutLCiNB3XljyEEEhDcuFg38ye1TVLr2d1vgghqL4tD3XKOL943KDxyPLpbW05fpjMohIMw8CIxzHk2FfDIL2giM5zp5OO72pI/l3Jr3SjWW5NgQwTk5sV07DOEyEEKzblsGJa5er4KLS2M0Pz1r5VrCp5q7OJ6gaKEKiKoC4msAuBIgThuE5Vfiop2wtQxlp/Oi+M4BtZPj2r88XussyoBh7omFmctRQJjI7QduoE2UUl5FXVoFg0jHgcxaKRV1VDdlEJjYf3Ew74x46PMDqQnD4oqjaLlkxMbjaW96fydUYIQenqLGq35TM9I9Z8coCmo/3zNq626nSkIsiQUB2DtdHE1yyhYFEUXOty0MZCvOFAjJZl2LM6X4qq03FnJYezGw/3XVPf8GIjpaThrdeRRiIEnF1Uwrp79/LBr3+XujvvIbsoUQyvR2PU73sdmDnFJj3XiSvd1AU2MbnZmNOwCiHWTXlsEUL8NyHEc0KILwghFk18eSlTUJXOmruLUKep5HQ2jHB+Xw/GPMQOFJuGmmphhaFgkRABrAgqddBcFmyVk5N3mo70zexZ3TKjGn3ZMj4QYWoBTyQUp+XE0m1X629tZqizI2mtZtvtpGRkUnXbjqT1gdZmuhsb6W2Zrgu8vCq5TUxM5sd8PNZ/nfL4S0AV8BXAAXxrEfa0LMguTmXdPSVo00KxfW1eTr/ahR6b27iuS3EQV0DRFKyagqIKwgJWZTgnjMxAh4+BaT2PlRtzbjoxgZQMO6WrknPYXRdGFmz4/EISj8W4sP+NpLWMwiLyViTar0vq1uDOTb7xOf4fLxGLTPbpWu0a2SWpi79ZE5NlgKqqm2tra+uqq6tX33PPPVWDg4MqQENDg9Vut2+qra2tG/8XDoeXfAn9fAzr1DdxL/C4lPI14Algw6LsapmQnudk4+5SbPZkIzfcG+DE79uTqj9nPd8QuHOdaIpAIlFUQVqZm/Sx0+IxnaYjy79ndb6UrcvC4ZoUuRjPXxvXMARhMWg5cYSwf/JmRyiC2p13TdwMCUWh7q5dE7q/Eslo7wijPZOFTIXV6UkDCUxMlguBgwedXZ/8ZEnrH/9JVdcnP1kSOHjwmiOXNpvNqK+vP9fY2Hg2PT09/o//+I8ThRclJSWR+vr6c+P/7Hb70vpAmIX5uD1pQoiHSRhhm5QyBok2GSHEkn+Di01Khp2Nby/l1MudBH2TikzeoTDHX2hn/b0l2F2zKyJpmXbcVhV3ig0jZqC5rUhdoowdf7P0rM4XVVWo2ZbPyZcnQ6z+0Qgd54dnzM29UQQ9o7SdOpG0VrpmPSmZyftLzcymbP0mWo8fJRrSiccM/EPNuDJKsafkUFiVhonJUqLr438z59i4+PCwFrlwwSk0TaJpMtbRYQ/seyvdVlMT1DIzL+tJFH3ly/MaG7d9+/bAqVOnlrViynw81teAB4E/BA4IIfIAhBD5wNJNgl1HHClWNu4pnaHEFPRFOfa7NgKe2aX6nJtykZE4wqqiZdmRusQIx3Fuyr3pelbnS2aBi/yK5L7OtlODhHxXJyO5kEgpqX/rdeQUSTWr00nlpq2zHl+58TacaWlJP/+hjqNkFjqWjfykiclUYp2dNqFpUlgSY+OExSKFpslY58KMjYvH47zyyiupDz300ERfWkdHh208DPwnf/IniycUv4DMaVillI9N+9c3tt4rpbx3/DghxO7F3OhSx2rX2HBfCRl5yVGRSCjO8d+14xmYmSu0lbpx7y5DcVnQPVEUlwX37jKsxak3Xc/qlbBiU27ShCHdkIm5uPOsuF4sBtpaGOpoT1qr2X47mtU66/GqprFiy52E/ZOCF/GID/SrnvdsYnJDMYJBFU1L/kPUNGkEg9fUjB2JRJTa2tq6nJyc9QMDA5aHHnpootJvaij4hz/8YfvlrrNUWMh2m/+5gNdalmgWlXW7isktTS5KicV0Tr7UzlCXf8Y5tlI3GQ9VkfOf15DxUBW2UvdN27M6X6x2bcaknuHeAH3TqmqvJ3o8RsNbryetZRQUkr+i5rLnhQMpuDImI2yaRaG/9TSB0ZHLnGVisjRRnE6dePLYOOJxoTid19QbN55jbW9vPy2l5Etf+tLSH9V1GRaytPTmTPxdIYqqUHd7IRZ7X9JoMN2QnH6ti9rt+eRXXjq/drP3rM6XvAo3vc0eRqboMTcd7Sez0IXVfv0roluOH51ZsHT73ZfNdxuGpLtplPTCdYR8vRjxCM50G1I3OPfGK2z5w4dv2ny5yfJjPjnQwMGDzoGvfa1IcaXoSkqKbvj9qhHwqzkf/WiXa9u2axbmT01NNb72ta+1v/vd767627/92+UjwTaNhXSBbvlCpnGEIqjekkfF2uTQrZSS8/t76Dg/U5x9nJu9Z3W+CCGo2Zqf1Csci+pcPDZw3fcS9IzSeup40lrJ6pkFS9MZ6vQTCcVRNRsZhesRApypibDxaE83XQ3nFm3PJiaLgWvbtmDORz/apaanxeL9/RY1PS22UEZ1nNtvvz1UW1sbevrppzPnPnppcnM1Qy4hhBCUr8vGYldpPJycL2061k80HKdyQ06Sx3Kr9KzOF6fbStnabJpPThrT3hYP+ZVuMvJdlzlz4UgULL0xo2BpxebZC5amMlVpyZlegqYNoCiTa40H3yKntAKb85bUWTFZpri2bQsupCEFCAaDSXeuL7/8ctP448bGxrML+VrXg3l7rEKIGVVf09ZaF2JDNxtFNRnU3VE4o2ex/dwwDQd6kWM9mrdaz+p8KanLxJWW/KvXcLAXfR7qVgtBomApOUJWs+3SBUvjBDyRpDC2EIItf7AHRZu8SYpHIjTsf322001MTJYxVxIK3n+5NSnlH137dm5OcsvcrNtVnDTFBaCn2cOZ17vQdeOW61mdL8q43OGUtZA/RtvpxR/Zq8fjNExTWEovKCS/6vIFS8CMubJpOQ6yS3Ko2rI9ab3vYhMDbS3XvlmTW4YbXR1vMjdzxhjH+lWLAIcQYiOTRUpuwIxhzZOMfBcb7ivh9CudRCOTYcXBLj/Hf9eGfyS51/VW6FmdL2k5Dgpr0um6MErQG8HTH6L9zBDNxwdY87Yi8isWR2yh9eRRwr7JKTvTFZYuhR4z6G32JK0V1SSm2JSuWUfvxQt4BybrMs7ve42MgqI5vWCTWxcpJY2H9nPqxecZ7e+jbO161uzaTUHVyhu9NZNZmE/y7u3AnwHFwFNT1n3Ak4uwp5sWd5aDjXvKOPlyB+FAorcx6I3Q0zRKNKxjtauk5TrILEihfO2t0bM6Xyo35NB2epCBNj+KJlCtCr0tHvpavazakU/Rykxc6TacbguKeu01eUGvh5YTx5LWSlavIzVr7p9LX6uH+JQCNKtdI6ckUdUtFIVVd+7i0LP/NpEGiPj9XDx6kJU77rzmfZvcPEgp8fT30dfcxMWjB+k4expVU1FUjdG+Xt762Y/Z+d4PmMZ1CTKnYZVSfh/4vhDiESnlM9dhTzc1TreVTXtKOfVKJ/3t3glDoVkV4jGDgTY/VZtzZ4SNb3U0i4oelwmjOv5/I0CPG9Qf7MUzlOj7FULgdFtJSbfhGvuXkm7D5tKuKKzesH9mwVLlPAqWpJRJbVYABSvSkoy9OzuH0rUbaDs5Wa/RfuYk+StqSMu99SrATSaRUuId6KevpYm+i40TLV79zU2omoqqJRS79FgMe3oGZ1/9vWlYlyDzLjeVUj4jhPgDYDVgn7L++cXY2M2MzWlhw+5SfvVPx5MMhaoJ7E6F3iYvK7cW3OBdLj1iER2X25qUi1ZUQTQ8aQCllAQ8kYSM4JSaI01TkgytK92GK8OWpPA0zkBbC4NtrUlr1Vt3YrHOrdrm6Q/hH50M6wuYtQCtctNW+lsuEvKOiV5IOPfGy2x7+D0oyjWJ2JgsM6SU+IYG6LvYRG9zY1L6YZxIOJT0+xcJBsgoLMIz0DfjWJMbz7wNqxDiWyRyqruA7wDvAg4t0r5ueixWFatDAwGRMUOhqgqZhS68Q0tvVNpSwJ3lIDCmGRwJxpGAoUus9rkNUTxu4BkM4Zk2hs7m0JKMrSNVnRhMPk56fgEF1fPzCrobk73V7JLUWYcwaBYLq+54G8eef25izT80RNupE1Rs2Dyv1zJZviSM6eCYZ9pEyOu57PE2uwM9HseZkYnDnYY9JYVoMEhazs0R4RBCbP7gBz/Y9+1vf7sT4DOf+Uye3+9Xn3rqqe4nnnii8Ec/+lF2ZmZmPBKJiJ07d/p+8IMftKvq0r0BvZIGyZ1SynVCiFNSyv8hhPgK8O+LtbFbgbRsByF/DGeqgR4zcLitxGMG7qxlPdhh0Vi5I59Dv27BlWEnI18h5IsR8kepWJ+DxaYSGI0kebPzIRKKEwnFGe4JAODpPYenrwPNoqDZVCw2lZqdmwkHYthdlsuGk6OhOAPtyd7G5dqlsopLya+uobfxwsRa87FD5FWswJl2a7dZ3YxIKfGPDNF3sYm+liaCo6NznqNoKjmlFRSuXMX5N1/B5kzB5nQRCQaIBoNseeDh67DzZDobhp3n9/Vk+YbDttRMe2TV7QVDxSszr6mv1Wq1yueffz6jp6ent6CgYMYf8Yc//OG+z3/+8326rrN169aVzz//fOoDDzww07VfIlyJYR2/1Q8KIQqBIcCMV14D44bC6tCwp1qIhnSioTgb7iu50VtbkuRXpLH1gQoa9vfiHQrhznKw+R3lSVXBsahOYCRCYDSCfzTxNTAaSSomuhTxSABvf33iccwgHjPQ7KU0nwzTfLIZVVNwpSXnb13ptgmJxe6mUYwprRDOVCsZ+ZcvnF+5/Q6GOtqIhRPhYyOuc/7NV9n0jnfe8q1WNwv+kWH6mhvpa24iMDK3RrSiqWSXlJFXWU12aTmaJRHxyCwq5uyrv8cz0EdaTh5bHnh4QfOrL3z37Jxj40K+qDbU5XcqqpCKKqR3MGTvOD+cnlWUEnSkWi97V7vnz1dfUjJRVVX5p3/6pwNf+MIX8r7+9a93Xeq4SCQiIpGIkpWVdWV30NeZKzGsvxFCpAP/CBwjIWH47UXZ1S3CbIZiw30li9Y+cjOQX5F22f8fi1UlPc9J+pQpQ1JKIsH4DGMb9ESTDOFI9wmknDTAimYjPW/1xPd63MA7FMY7lDwgwWrXMHSDrgsjhAPxieruqs25cxpHq8NJzfY7OfvqSxNrw12d9DTWU1izau7/EJMlSWB0hN6L48b00hKm4whVJbu4lLwVVeSUVszaelVQtfKGFyp5B0M2RRVS1RQJoGpCgoF3MGSby7DOxSc+8Yn+tWvXrv7c5z7XO/25b33rW3n/9m//ltXd3W29++67PTt37lzS+bIrKV76u7GHzwghfgPYpZQTiQEhxG4p5YsLvcGbnbkMhcm1I4TA7rJgd1mShhkYukHIF8M/EqH7QhN9jYOomjKh1ZxesBZFm7u3dLQ/MKO6e7DdP0Nt61IUVK+kp7GB4a7JAe8XDrxJdkkZVofZKr5cCHpG6R3zTP1DcwuYCEUhq6SU/MpqssvK51Ucd6OJRXVVsyhJ4R9FFTIW1a854ZmZmWm8+93vHvrSl76U63A4kl5jPBQciUTEO97xjsqnn34640Mf+tCSHRF1VSK0UsoIMH169/8ETMNqsmxQ1ESlsD1FpfHgGbIKE/rDhiFxuLNZsWUrAU+UwEiUwGiEWGz2yVie/tCM6m5rikrTkf4JYYjLIYRg1Z1vY//Pf4IRT7xGLByh4cCbrN21Z4HercliEPR66GtOtMb4hgbnPF4ogsyiUvJXVJFTVoHFtrxEYCxWVdfjhpLwVBMYuhQWq3pNY+PG+dSnPtW3adOmukcffXTW/0ybzSb37Nnjff3111NvOsN6CcyEkMmypO3U8cm2FxItPBv23Ic7e3K4xtRw8tSQctAbJRrW0ayTfaqKIkjLcVxRdbfTncaKTVtpPDSpHNrbeIGCqpVkl8yZ+jK5joR83oQxbW5KUtC6FAljWkJeRRW55ZVY7EvTmF4uBzpOZ8Ow89BzLUUWh6Zb7aoeDetqLBRXtz5Y0XWtBUwAeXl5+gMPPDDyk5/8JPt973vfDLffMAzeeuutlA0bNizoEICFZiENqylgabLsCPm8tJw4krRWXLcWd3ZO0tolw8mGJBbRCYyGEUJBInG4LBgGV1zdXbpuAz0XLySFEc+/+So73vX+iQIWkxtDyO+jv/niDDnKSyIgs7CEvMoV5FaswGq/OSr9i1dmBnmQrqlVwRt3l/QuhFEd59Of/nTv97///aQ/wPEcazweF6tWrQp+4hOfWNKzWm/NeWQmJmM07H9zIvwKYHE4qNqybd7nK4pgzd1FE9XdVod61dXdiqJSd+c9HPrV/5u4TQ37fDQfO0TNttuv6FomV8/pV17g6G+eJTA6gtXhILeiCmeqe+4TBWQUFJFfWU1uReVNmx8vXpkZXEhDCslj40pKSuKhUGji+6eeeqr7qaee6l7I11tsrkQgwg58BLiDxJ/9m8A3pZTjJZKtC747E5NFZLC9lYHW5qS16q07rzjvtZDV3Wm5eZSuWU/76ZMTa22njpO/ohp3du4VX88kGSkl8ViUeCRCLBIhFgmPPU58bTp6kHOvv4xQFKSUhDweWo4dJq+qhuyiWW6UBGTkF5JXWUVuRZU5W9cEuDKP9QckhPe/Pvb9+4EfAu8Gc2ycyfLC0PUZI+HS8vIprKm9qustZHX3ii3b6W+5OKETi4Rzr7/CtofejVBMDWkpJXosRiwamdVATl2PR8KJr9GxY6LRieEHs9Fw4A0wJEIRiVYpTYF4nKGO1iTDmp5fQN6YZ2p3pVzyeia3JldiWNdIKeumfP+KEOLcQm/IxOR60HrqGEHPFBk5AbW3zz0S7nqgWSzU3vE2TvzHbybWfIMDtJ85Rdm6DTdwZwtHT1MDZ1/9PSO93aRkZFK5eSsZ+YVjxnCmgUwYxUljeTnjeC3Eo9GkYfQAqArxaJS0vHzyKqvIq6jCnmIaU5NLcyWG9ZgQYruU8gCAEGIbcGSOc0xMlhwhn5eW49MKllatWVKh1pzScvJWVNF3sWlirenIAXLKK3C6l3ffc09TA6/98HtEAn70eBxPXw/tZ06RV1mF6wZLOWpWK0YsnvBUSaggCamSkpHF1ne+64buzWT5cCWGdTPwlhCifez7UqBBCHEakFLKdQu+OxOTReDCgWkFS3Y7VVu238Adzc7KHXcx1NlBPDIudxinft9rbNz7wJLwrK8GPR5j37/9GP/wIKqW0F4eH4U22tN1XQyrarGg2WxYrLaJrxa7Hc1qQ7VaOPvq71E1DYvDiR6LEY9GuO1BM9NlMn+uxLDuXbRdmJhcJwY72uhvmaVgaQn2FtqcTqq37eT8669MrA11tNN3sZH8qpobuLOrY6Sni3Ovv8xQZ/sMlSFF1YiE59/3q2gqFlvCGFpsMw2kxWbHYht/LvF4/FjlMlNRVu64g5yyikRVsGcUV1o6O9/zflOow+SKuBJJwzmbh01MljKGrtPwVvJIOHduHoUrl64mb9HKOnobGxjpmew2qN//BlnFpUvyZmA24tEojYf303n2NJAYgRaPRSc8Vc1mw9B1nGlp5FfXzGogNduUx1Yb6vQ86AKydtce05BeZ1RV3VxdXR2SUqKqqvzqV7/avnv37sD485///Odz/+Ef/qG4u7v7ZFZW1oKoPC0mi97HKoTYC3wVUIHvSCm/dInjHgF+DtwmpTRztyYLTtup4zMKllbdfveSDqsm5A53sf+ZnyL1MbnDUIgLB/ex+u57b/Du5maos4Nzb7ycNLw7vaCIvuYmFE0ju6QMIRSiwSA73/uBGy4ybzI37WdPOc+88kKWb2DAlpqTE1mza89Q6ep119TXarPZjPr6+nMAzzzzjPvJJ58s3r17d8P48z//+c8z16xZE/jRj36U/rGPfWxuIeYbzKIaViGECnwD2A10AoeFEM9JKc9NOy4V+BhwcDH3Y3LrEvL7aJ5esFS7GnfO0ilYuhSu9AwqN23h4uHJP4/uhvPkV9WQNVtv5RIgFglz4cA+uhvOz3jOlZbO2nvfjn94EN/Q4KKMQDO5cn77tf81p3Zm0OvRBtvbnIqqSkVV5Wh/r73t1In07NKyoNOddtnpNn/w0U/OK+rp8XjUtLTJa509e9YWDAbVr371q21f+MIXCm55wwpsBZqklM0AQoifAu8Eprfp/B0JEf9PLPJ+TG5RLux/EyM++Xdvsduoum3HDdzRlVG+bhO9TY1JI8jq33yV7Y+8b1HDolfDQFsL5998lUggMOM5hzuNurvuIbOw6AbszORa8fT32RRVlaqmjY2NS3z19PfZ5jKslyMSiSi1tbV1kUhEDA4OWp5//vkL48/94Ac/yHj44YeH9+7d63/88cftHR0dWklJyZKex7rY3eZFQMeU7zvH1iYQQmwCSqSUv73chYQQHxJCHBFCHBkYGFj4nZrctAx1dtDfcjFpreq2HcsmRwmgqCp1d92TNOoi6PHQfPzwjdvUNKLhEKdffoETv/vtTKMqoHTtBna861HTqC5jYuGwqqhqUhOxoqoyFg5f09i48VBwS0vL2V/+8peNjz32WIVhJCbH/eIXv8j60z/902FVVXnHO94x8sMf/nDukVE3mBt6qyuEUICngD+b61gp5dPA0wBbtmwxBf9N5oWh69S/9VrSmjsnl6La1Zc4Y+mSnpdPcd3aiSIggLaTx8ivrCY1K/uG7UtKSX/LRc7ve41YaGZlrysjg7q77iU9L/8G7M5kIbHY7boeiynjniqAoevCYrcvWEHRfffdFxgZGdF6enq0rq4uS1tbm23v3r01ALFYTBQXF0effPLJJe1dLbZh7QKmJoGKx9bGSQXWAK+OFZDkA88JIR40C5hMFoL20ycIjo5OLgioXeIFS5ej6rbtDLQ2T3iE0pCce+MVtj74yA2RO4wEg9Tve3VGCxMkxqWVrd9E5cbblly42mQm88mBtp895dz3sx8V2ZwO3epw6tFQUI0EQ+rt7/3jrmstYBrn+PHjdsMwyMvLi3/xi1/M+/jHP979xS9+sXf8+aKiorUXLlyw1tTURBfi9RaDxf5LPAxUCyEqhBBW4FHgufEnpZQeKWW2lLJcSlkOHABMo2qyIIT9fi4eSw6VFtWuJi037wbt6NqxWG3U3n530pq3v4+Oc2eu6z6klHRfqOet//fjWY1qSlYWWx96D9W37TCN6k1E6ep1wdvf+8dd9pTUWGB42GJPSY0thFEdz7HW1tbWPfroo5Xf/OY3WzVN49lnn818z3veMzr12Pvvv3/k/2/v3uOjKu99j39+M5N7ICFAQgjhGi6iCCJe6v1edytaj1pFbbu32qu2ex97safaHu3ubu1u96nt2ZfWbe0Ft6XqtiqtPVoV652LIiIIMYAKIQkhJIEkkGRmfuePZyVMhoQQnGStSX7v12teJGtWVr6smcxvrfU863l+85vfFPW1rSAY1He8qkZF5BbgKdztNver6gYR+S6wRlWfOPwWjDl6boSlnh2WZqZRh6W+FE+dTvG06T0KWtXqVymeOn1IxrA90NLCxhdX0LD90BMcCYWYfuJJTJ2/kFDoQzW7mYCafOzxbak6O+0Si8Ve7235jh071icvu++++3ak8ncPhkE/lFTVJ4Enk5Z9p491zxnsPGZkaNixnbqtVT2WVSxKrw5LhzP7tLNoqN5OrKMTgFhnJ++8/DwLLvr4oF3mVlWqN22gcuXL3b830ejxxRx79vnkF40dlN9vTLqwazRm2HEdlpJGWBpfTNmcuX38RPrJzstn5smnsemlgx2zdr//Hru2baFkekXKf1/b3mY2vvAcjTurD3kuFAkz48RTmDJvgU1rZwxWWM0w9MHb62hrajy4oKvD0jD70J90zHHUvLuZ5rrufh1seuUFisomDXiy9r5oPM4HG96iavVrPS6rdyksncixZ51Hrs+z0hgTJMPrk8aMeAdaWtia3GFp9ty07rDUFxFh7pnn9jhg6Ghr491Vr6Zk+61Njaxe/ughg2uAmyFmzulnseiSy62oGpPEzljNsFK58iVinQfb/yJZWVScnP4dlvqSXzSWqQtOZFvCwUT1OxsorZjFmNKjG4hB43Hef2stVa+v6h6fOFFRWTlzzzqXnFGjjzq3McOZnbGaYWPPzh09JgYHd99nZnaOT4mGxrQFJ5Jb2POsceOLzxPvpSj2Z1/DblY9/gjvrnr1kKIaycxk7lnnsfBjl1pRNeYwrLCaYSEej7Hp5Z4jLI0aN55JaTjC0kCFIxHmnnluj2VtTY1se/PIbwePx2JseX0lK//we/bW7zrk+XFTpvKRq66lbM7ctB1cwwTb0qVLC0XkxLVr12YDbN68OTM7O3vhnDlz5s6ePXvuCSecMGfdunVZ/W0nCKywmmHhg/XraG1s7LFsOHZY6suY0jLKjul5ELFt7eu0JAza35e99btY+YeH2Pr6ajTec7TQjOws5p13EQsu+jjZeYN/j6wJvgNbmnL3LNtUvusX6yr2LNtUfmBLU24qtrts2bKihQsXtvz2t7/tHvyhvLy8fdOmTRs3b9688dprr9191113labidw02a2M1ae9A66EdlibOPmbEjU078+SPUP/+Njra3L37Go+z8YXnOOnSK3o9y4xFo2x9fRXvvfUG9DL6dsmMCuacdhaZOSmJc4mZAAAgAElEQVT53DQB1/C7Tf1OGxdr6YhE69pyCYkSEo02HMg+UNVUGCnJbQvnZx52xpmxS+b0OWRic3NzaPXq1fnPPPPM5ksvvXTmT37yk53J6+zduzdcWFgY+EnOwQqrGQYqX3v5kA5LM4dxh6W+ZGRlM/sjZ7L+2ae6lzXX1VL9zgYmzT2ux7pNtTVseOHZnuMoezJzc5lz+tmUTJsx6JlNeok1tmcREpVwyB2KhUWVOLHG9qz+CuvhPPjgg4XnnHNO8/HHH98+ZsyY6IsvvphbXFwc3b59e9acOXPmtra2hg4cOBB65ZVXNqXsPzOIRsZ1MjNs7dlZTd2Wd3ssq1h0yog9yyqZXsG4KVN7LKtc9TIHWlsAiHZ2sumVF1i9/L97LaqlM2dz2pXXWlE1vdKOWJiQ9Ly+ERLVjtiHGr/yoYceKlqyZEkjwBVXXLFn6dKlRXDwUvD27dvf/v73v7/9hhtu6PesOgjsjNWkrV47LI0dx6RjjuvjJ4Y/EWHO6Wfz6s7q7rP4WEcnm195kUlzj+OdF1ewf+/eQ34uKz+fuWecw7jJU4c4sUknkhmOaTQeIpxQXOMqkhk+6ku0dXV14ddee23U5s2bc2655RZisZiIiN566609etEtWbKk6Stf+crUo08/dKywmrS1/e31tCZ1zplzxjkjpsNSX3LyR1Fx0qlsfuVFWpubaKqpZuuba8j68xMUlpaRlzSgw6RjjmXmKacTycz0KbEJgsO1gXY5sKUpd+9f3i+TrHBMssIxbY+FtT0WHn3hlOrsGYVHNTD/0qVLx1x++eV7Hnzwwe7ff9JJJ83etm1bjzfkX/7yl1Hl5eXtR/M7hpoVVpOW2tta2fLGyh7LRmKHpb6Uz53H5ldfom5rFeFImIzMLKKdHdRtraJkegV5BYXkjB7NMWeey9iy8v43aAzgFc/qttW1Y6PN7VmRgqz23LMm1R5tUQV4+OGHi77+9a/XJi677LLLGn/wgx+UdrWxqioZGRn685//vN/iHwRWWE1aSp5hJZKZOSI7LPVFQiGiHe2EI5Hu+VDDkQwAmmqrOeaMs5mx6FQiGRl+xjRpKHtGYduHKaTJVq5cWZm87I477th1xx13HHpDdZqwwmrSyvoVT7PqsUfY11BPJDOTseVTGVdWzoxFp47YDkt9advbTEFJCS0NDd3LMnPzyMrJZfZHzvQxmTHDmxVWkzbWr3iaFx64n1g0TigSId4Zpa6qkuz8fMrnjtwOS30pGF/C/n17EYT2/W1k5+WTkZ1D7ugCv6MZM6yN7F4eJi3Eop3UbnmXl5ctJdbRSSgkhCREKBIhFAqxZ8f2Ed9hqTfHnnM+HW1tZOXlM37yNDJzcuncv59jzznf72jGDGt2xmoCKR6Psad6B7VVlex6byuxzk4OtLYQivR8y0Zycrrv0TQ9lVbM5rSrr2PD88/SXF9HwfgSFi2+nNKK2X5HM2ZYs8JqAkNVaa6rpWZLJXVbq+jcv7/H85HMTOKdUYi4s1MJCaFQiGybaaVPpRWzrZAaM8SssBrftexpoGZLJbVVlRzYt6/P9caWT6WuqhLicTJz3FRwsc5OTrzkE0MV1Rhj+mWF1fhi/7691G55l9qqSlr2NPS7fmZuLgsvXkxzfR0b//osrc1N5BUUcuIln2DeuRcNQWJjzGDZvn175Etf+lL52rVr8wsKCqIZGRl666231hYVFcWWLFkyo6ysrCMejzNu3Ljoww8/vLWsrOyoxyUeClZYzZDp2N9G3dYqaqoqaa6r7Xf9SGYmxdNmMKFiFkWlZd0dlE6+9IrBjmqM6cO2bdty165dO7a5uTmroKCg/YQTTmiYNm3aUd/XGo/HWbx4ccW1117bsHz58m0AlZWVmQ8//HBhUVHR/kWLFrWsWLGiCuDmm28u+/GPf1zc2+w3QTJiCmtN1eYenTiOPed8a3saAtGODurf30ZNVSV7qj84ZL7PZKFImPGTpzGhYhbjyqcQCn+osb2NMUfokUce6XeA+9bW1siuXbtyQ6GQhkIh3bNnT/aWLVsKi4uL2/Ly8g57FnnllVf2OmrS8uXLR2VkZOg3vvGN+q5ls2bN6rj99tt3/fGPfxzVtSwej7Nv375wRUXFgYH8v/wwIgprTdVmnvvVvUQ7OsgtKKR5Vy0vLVvKGdd8yorrIIjHYuze/j61Wyqpf38b8ejhx+eWkFBUVs6EGbMonjrdxqw1JqAaGxuzQqGQhsNhBej6t7GxMau/wtqX9evX5xx//PF9nvGuWbMmf86cOXObmpoiOTk5sXvuuWfH0aUfOiOisG54/llUlXi0k5YGd1AUi0ZZ8et7OWnxFRSWTqSwZAIZWdk+J01fqkpjzU5qvR690fb+x8ouKJlAacUsSqZX2KhJxqSBzs7OcCQSiScuC4VC2tnZmbJLS5/61Kcmr1q1Kj8jI0PvvvvuHYmXgm+//fYJt9xyy6QHH3zwg1T9vsEwIgprc30d8Whnj2WhcJh9exp4b90bsO4NEBhVNI4xE8sonDCRMRNK7cO+H6rKvobd1Ho9ettbW/v9mbwxYyitmE3JjJk2ApAxaSYjIyMWi8VCXWeqAPF4XDIyMo562rh58+btf/zxx8d0fb906dIPampqIosWLTomed0rrrii6aqrrgr8ZMEjorDmjymiceeO7kHIAeKxKFnZOQdXUtjXsJt9Dbv5YP06wBWBMRMmMqa0jMLSiWTn5Q919EBqa26idsu71FRV0tbU2O/62fn5TJgxiwkVs8gvGouIDEFKY8xA9NUGmmjbtm25K1asKMvMzIxlZWXF2tvbwx0dHeFzzz23+mg7MC1evHjft7/9bfnhD384/rbbbqsHaGlp6XUotRUrVuRPmTIl8FPHjYjCevwFF9O8qw5EiMdi7N/bRCwaY9zkssP+XGtjI62Njex4ZwMAOaMLGFM60Z3Rlk4kZ9ToEVMk2ttau2+P2Vvf/6QTGdlZlEyfyYSKWRSWlI6Y/WTMcOYVz+rEXsGnn3567YfpFRwKhVi+fPmWm2++ufxnP/vZhKKiomhubm7szjvv3AEH21hVlVGjRsXuv//+91L1/xksonr4XppBtGjRIl2zZs2Afia5V/DMU08nKyeXxtqdNO6sPmTC7CORlZ/vzmgnTGTMxDJyCwqHTQGprnyH9c8+zZ7qDxAJkVNQQN7owsP+TDgjg/FTp1E6YxZFk8oJhaxHrzFBIiKvq+qixGXr1q17b/78+bv9ypSu1q1bN27+/PlTe3tuRJyxQt9Du02YMROAzgMHXJGt2UlT7U727t4F/RxztLe0UFvl2hcBMnJyvEvH7vJxEC97qiqdB/ZzoLWV9rZW2ltbaG9rO/h1ayu7q7dT/c4GwpEwoXCEeCzK3t313RNkJ5KQMLZ8CqUzZjFuyjSb39MYM+KNmMLan4zsbIqnTqd46nQAOjvaaa6t6S62e+t3ofH4YbfRuX8/u7ZtYde2LQBEsrIonFDa3U47aty4QTuLU1WinR20dxfMxMLZ6oqnV0T7+3/sfm8r4Ui4u026e4LsmuruwjqmdCITKmZRMq2CjGzrTW2MMV2ssPYhIzOLcZOnMm7yVMBNXdZUV0uTV2ibd9X2e39mtL2d3e+/x+733wPcpdKC4gmMmeguH48eX0I40v9LEItGveLoFczWVtr3d319sHDGOjv73daRaD+wn4zMrB7LQuEIsViMmaecxoQZs8jOt45cxhjTGyusRygcyWBsWTljy8oBNwjC3vpdNNZU01i7k6bamn4LW6yzkz3V29lTvR0ACYeRkNBUU0PH/jZyRo9m4uy55BUUJpx1ttB5YGg7wWVl5xDt7CAjO4dIRgZZeaMIZ2QwqmgsU+cvHNIsxphBF4/H4xIKhdKvw41P4vG4AH1e+rPCepRC4TCFE0opnFDKNEDjcfY17KaxZmd3se1vkISWPQ3Uba3qbstsbdpD3dYtvbZlplIkM5OsvDyycvPdv3l5ZOW4f7Pz8misPYM1y/+brNw8snLzaG9rpaOtzSbINmZ4eru+vn7u+PHjm6249i8ej0t9fX0B8HZf6wx6YRWRi4GfAmHgPlW9O+n5W4GbgChQD9ygqv3eTxU0Egoxenwxo8cXM+X4BagqLY0NNNW4S8eNtTvpaOvZI72pprrftsyBCEXCrhjm5ZOVm9tdOLNz88jMdUUzMzev3w5GBcUTyMrNsQmyjRkBotHoTbW1tffV1tYeB/R6/6jpIQ68HY1Gb+prhUG93UZEwkAlcCGwA1gNLFHVjQnrnAusVNU2EfkicI6qXn247R7N7TZ+U1Xampu8zlDVNNbs5J2XnicjM6tHz2FVpbOjnekLDvaIl5CQmZPbfQaZlX+wcGbnecvy8ogkbcsYYxL1druNSb3BPmM9GahS1a0AIrIMuAzoLqyquiJh/deA6wc5ky9EhLzCMeQVjmHSnGMBN4LR3t31EI8Ti0YJRSLEo1GKRhcw/6KPkZ3nzjgzs3O6p0wzxhgTbIP9aV0GbE/4foe3rC83An8e1EQBMv/CvyEcCpMzuoCxkyaTO7qAzOwcTrn8KoqnTmf0+GKycvOsqBpjTBoJzCe2iFwPLAJ+1MfznxORNSKypr6+vrdV0k5pxWxOu/o6ckaNZt+e3eSMGs1pV19nbZnGGJPGBvtScDVQnvD9JG9ZDyJyAXA7cLaq9tqVVlXvBe4F18aa+qj+6GtEKGOMMelpsM9YVwMzRWSaiGQC1wBPJK4gIicAvwAuVdX+R3c3xhhjAmxQC6uqRoFbgKeAd4CHVHWDiHxXRC71VvsRkA88LCJvisgTfWzOGGOMCbxBv49VVZ8Enkxa9p2Ery8Y7AzGGGPMUAlM5yVjjDFmOLDCaowxxqSQFVZjjDEmhaywGmOMMSlkhdUYY4xJISusxhhjTApZYTXGGGNSyAqrMcYYk0JWWI0xxpgUssJqjDHGpJAVVmOMMSaFrLAaY4wxKWSF1RhjjEkhK6zGGGNMCllhNcYYY1LICqsxxhiTQlZYjTHGmBSywmqMMcakkBVWY4wxJoWssBpjjDEpZIXVGGOMSSErrMYYY0wKWWE1xhhjUsgKqzHGGJNCVliNMcaYFLLCaowxxqSQFVZjjDEmhaywGmOMMSlkhdUYY4xJISusxhhjTApZYTXGGGNSyAqrMcYYk0JWWI0xxpgUssJqjDHGpJAVVmOMMSaFBr2wisjFIrJZRKpE5Ju9PJ8lIr/3nl8pIlMHO5MxxhgzWAa1sIpIGPg34G+AucASEZmbtNqNQKOqVgA/AX44mJmMMcaYwTTYZ6wnA1WqulVVO4BlwGVJ61wG/Mb7+hHgfBGRQc5ljDHGDIrIIG+/DNie8P0O4JS+1lHVqIg0A2OB3YkricjngM9537aIyOajzDQuedsBYbkGxnINjOUauKBm+zC5pqQyiOndYBfWlFHVe4F7P+x2RGSNqi5KQaSUslwDY7kGxnINXFCzBTWXOWiwLwVXA+UJ30/ylvW6johEgAKgYZBzGWOMMYNisAvramCmiEwTkUzgGuCJpHWeAD7jfX0l8Jyq6iDnMsYYYwbFoF4K9tpMbwGeAsLA/aq6QUS+C6xR1SeAXwJLRaQK2IMrvoPpQ19OHiSWa2As18BYroELarag5jIesZNDY4wxJnVs5CVjjDEmhaywGmOMMSlkhdUYY4xJISusxhhjTApZYTXDhg2FaYwJghFfWEUkV0QO2Q9+f0hbroHruv85CFm6BHV/BTjXZBEp6mW5r59VAc4VyNdxpBvRhVVEJuLusb1RRI4VkXzxqKp6z1uu4OcqFZEnReQqEclLKLAh79/xPuUK6v4Kcq7HgJtE5BIRmS0i+QCqGheRyZbrkFyBex1NGo0VPEg+DUwDTgKuAzYDK4CXRKQNeEJEzlDVA5Yr0LluxE3ukAF8X0ReAB5Q1RXe8/eJyFXeDEtDKaj7K6i5rgVG4T6XlgBNwGYReRM36PxzIjJNVfdbLiC4r+OIN6IHiBCR64AdqvpX76jzcuBCIIqbYadJVRdbrsDn+gdgnaquEJFZwN8Bi4EWoBPYq6of9yFXUPdXUHN9FGhT1RdFpAQ4FzgdV9DmAQ2qmjzt5EjOFcjX0VhhzQMyVbUxafkkoAq4SlWXW67A5yoE8lS1Omn5HGAjsFhV/+RDrqDur6DmysB9JnUkLS/ATS15vTcMquUiuK+jGeFtrEAbUOj94SSqxU0G4Neb0nINgKo2AXtFJJz0VBXwoh9F1RPI/UVwc40G5oubsCNRG/C0H8XLE9RcQX0dR7wRe8YqIv8EzMJNUVeCO7N5VFVfF5FcoFhV37Ncgc91M3A+rohm4NqZnlPVShEZB5Sr6lofcgV1fwU11y+BTNxkHccA63GTdjzvPV/oHUBZLoL7OhpnRBZWEbkU+Kr3ACjCtZlMBpap6lOWKy1yfQK4FfhnIAsoBGbiPgTvVdV3fcoV1P0V1FwfB76B6yTU6eX6H8BHgCdV9T8sV49cgXwdzUEjtVfwAtwlwjUA3iXEN4CLgVtFpKHrOcsV6FynAn9U1T96ubJxvSQ/CfxSRG5S1UofcgV1fwU112zgnYQ28l243t2nAF8TkUpVfdZydQvq62g8I7WN9VfAWSJyl4iUqmpMVXer6gNADXCC5UqLXL8DrheRL4vIOFU9oKrvqOpdwPvAQp9yBXV/BTXXfwIFInKPiJR3LVTVlcBeXM9by3VQUF9H4xmRl4IBRORk4IvARNyb8RVgE/AgcJaqbrVcaZHrPNwZagauh+YGXDvYc8CZqrrFp1xB3V9BzTUX+BbuYKgOeNLL913gPL/aCwOcK5Cvo3FGbGEF8Hr5zQPmA5cAzcDDqvqk5UqrXDO9XNOAM4B84Feq+qDPuYK6vwKZC0DcsIHn4NoyK3Ed0V7yNRTBzBXk13GkG9GFtYuInOnd/C0aoB1iuQbGcg1MkHKJG35SVDUmImerG/TAch2BIL2OxhmpbayJ48hOB74GBwdx95PlGhiv4wYiMg24CQKTK6j7K5C5cDFiIlIBfK9rgc+ZIKC5Avw6GkZYYRWRsEj3rA9db8IvAju7nrdcaZErlJArnpCrzedcQd1fQc3V2wws1wArvectV4Kgvo7mUCPyUnDS5Z0KoEVVa0UkpKrx/n7ecgUy1+nANlXdGbBcQd1fgcmVyOt9e0BV6y1Xn1kC/zqOdCOmsIrI3wO5wH+q6u6k53JwfzRDvjMs14BzfRrIBh7SpBFvxA3tFrX9lRa5bgfycJ3M3k16Lgdo96NIBDhXIF9H07uRVFj34oa9m4brlv4L4L9UtVNErsLti4csV+Bz7QLexs3esQlYhhskolNEPob7gHnOh1xB3V9BzdUMvAUUA9XAQ8Bj3pnX54Gwqv675erOFcjX0fRBVYf9A5gD/Nr7OgP4HLAG1z3990A9cIrlCnyuBcDTuDOKmcA/AH8B3gR+jJsb82TbX4HPNRX4kfd1BDeX6FPAVuAB3AhHlivgr6M9+n6MiDNWr01iDlCjCVMsiRsC7z7gVFWtsFyBzxXG3be3TVWbEzKVA3cBJ6rqbB9yBXV/BTWXAFNw85juS1ieAfwbcL6qzrBc3b8/kK+j6duIGCtYXZvIRkmY9klERFUPiMjbuCNSyxX8XDHc2WnisgPAuyJSh7ts50euoO6voOZS4D0RGZ20vFNENuAuwVqug78/kK+j6duwL6zi7m+8Dtdmsk1EdgPrcG/UOO6DeqXlCnyuEuATwHTcZbBaYIt6vYCB1bjLwkOdK6j7K6i55gF/h7ucv9drO3wFeNk7SOoams9yEdzX0RzesL8ULCIv4trlYrh7v8Z4T/1FVYf8g9hyHXWuvwIv43pGjvKyVeN6B2/wMVdQ91dQc70E/AHXHn4Ad6BUCqxT1V9arkNyBfJ1NP3wu5F3MB+4nqMbkpbNBD6LO/q8EQhZrsDnKgY2Ji07EfhH4APgcnt/pUWuMcDbSctKgI8CL+BGEIpYrmC/jvY4gtfO7wCD+p9zl3WW4hr45yQ9Nwd4DNd93nIFO1cJ8Diug1J50nOnepn9KBRB3V9BzRUC7gEexXU0S3xuEvAskGG5gv062qP/x0i4FDwed8QZxs3RuQ13n9rHgGtU9RzLlRa5ZgNfwl3+fQc3jNtW3Gwj16nqeT7lCur+CmquXOA23P2Y9cBm4FXgQmCxqp5ruXrkCuTraA5vWBdWr+ecihuo+kxcu0kOboqlV4F/VdW1littcp0KXIybg7LZy7gT+KG6yaf9yhXU/RWoXAn58nCX8ufi7h39BO6KxG/V3/byQOUK+uto+jasC2syr4ddgaq+KSK5qtrmdyawXAMlbnzUUmAjENOkoQ39EuD9FdRck4FMVa0SkYiqRv3OBIHOFcjX0Rxq2M5u492C0fV1hvflEqAIIAhvSst1RFmk69+E+/gWA22q2uB3UfVyZXnfBm1/ZQclV6KE99dNwHEAQSheQcrVx/s+UK+j6duIOWP13qg1wHxVrfM7TxcvVy0uV61PGUbjBhdvF2+GDC9XNXBCAPdXDbDAr/3VmzR4fx3vZy5JmnnFy7UTt792+ZUrWcBzBe59b3o3LAeIEJFrgae15ywQxcBXVbUu+Y98CHPdhht7dL26UYTAdan/gvo77dPtwB5cW2XX7y8B7vB5f50IzAK2JrShTgG+7ef+EpExuE4unbg2rypc55LbfN5f03H7KwvIB14DmoC/9zMXdI8e1DUsZRx3L/ItqrpLRMIJfw9DRtxUcMcBe4E1qtoekFzHALOBelV92Vs8HviKz58T5ggNuzNWcVMorQJOU9V93ofgPNwf8zuq2tDVKcCHXO/ijjh3i8hJuI4SE4HXVPVJP/5gvMuF23EfwKuA76rqZu9SelhVO4cyT0KuHOAZ3FF6BfBV3HRxXfe0rvRxf90PFOJ6j84BngdeUtXlQ5klKVcOrqNNHFjPwcEE3sNNgbbTp/d9Jq5X61Oq+nrC8q6OORl+vMe81/ER3AFIC+7A8u/VG4vXx4OjbOBPuL/H2bh9tw83HvYmr311yF9HMzDDsY31KuAFr6jOA34C/DvweeBOEcnx6U15CfCGV1RPBH4EHIv7g75CREp9Ogr9JLBMVWfiLhl+Q0RmqGrcr6LquR54X1WvBO4G/jfwRdyHzY0iUujT/voU7oD0Y7gPvddwHamuE5Gv+5Cny6eBDlW9GPgn4Je4+xxHAbeJSJ5P7/sbcLex3CMiL4nIt0RkSkKWLye0BQ+l64C96m5X+SzuQOSyhOevTmhzHUrX4AbbvwL4Lu59fytwAnCLiIy2ohp8w7GwfhV3hgPuHsfNqnoc8B2gDDjLp1yvATUiUgichhuK78vAz4FW4As+5fpb3A3wAD8D2oFHReSTPuXpMh7oahO8FFilqpcA/4K7/PoZn3KNxbXBoar1uMvA7wD/C1gkImU+5aoEGkVkrKo2qepmVf0TbuCDicDlPuWaD3wLOBd3MDkTWC4i/y0i9wNXqxuLd6h9HG9saVWtwc1v+jkRyRaR84HP+HRgeT3QdeXjY7hL1Ffg9l0WcIUPmcwADcc21luAr4lII1CA+8NGVbeJSAfu7MIPO4AG3GWePcAmryv/AREZS9KsLUPBu0z3O1V9DEBV3we+JCJX4M7ASlT1//p06elB4McishY3bOEHXsZ6r8NkyxDn6bLMy3UX7kBtBq7tfpt35nWmt85QW4n7UF4mIs8Df8U1MdR6vZZzfcgE8AMg1+td+zjwuNe2eQLwMO4qxJDyOgL9K25+VQBU9RURWe/lWYBPMyUBXwe67pn9Ke5zA+9Kl0+RzEANuzbWRCJyNm52iqh3pvgKbsiy/T5mOgW4CHdJsRr3x50JXOtnrmQicjnwTeBsn84outoNJ+EGIP827uywCDgHONPHW1pm48aRjQKve+29pbib9uf6eSuEiFyAuyIyBTegQDtu/10VpFs0RCSCG91onh+5vD4E2araltDeW4Qb1nAOMNXH9333gWxCtmLcwdLCIH1OmN4Nq8LqXYa7HtdhYyWwU1U7vD+ii4ELVPVWn3JdB2zBTfnUJQd31vOyJkysPIS5JuLujdvm5epxX6j4dHO8t7+uxhXSt3Gdqxbg2qmjuMvCT/mU65O4gSm24trCWrwzoFOBS1T19qHOlZRRcJd+i7xFk3Edq5r9S+UkFYwpwEWq+p8+x+raZ2HvAPxbwExV/bsA5Aqrasw7CLkAOFdVb/M7l+nfsCmsXi/be3BnpYtwvSM3A4+q6jNeR4RMVW31MddJuFs0NgCPq+qKoczSS66f4qZiW4Q7q9kM/EFVn/E51z24s7+uXJuABxNuPfAz12terg5c4X9U/Z2GbTKuDfotXG/p3f38yJBIyvW2qu5JeM63Xq0Judbjbnvb08s6Qz6qUdL+2qCqDQnPdRVYX3pQm4EbToX1X4BGVf2e9/1U4Epc55xVqnpDQHJNS8i1BrjRp7PCw+2v1cBN6s89fL3lugo3CfWrwBdVtSMAuabhOpL8LW5/fd6nXL/F9SP4I+5sfivutrJVXtv9ElX914Dk2qCqa7xLrter6s8Clmss7pL5zwOWawxuwP3/GOpc5ugMp17B1cBxIjLTO7J7T1V/7PUIjojIGQHJtU1Vf6SqxwICnBKQXIn7K4y7tBmUXD9S1blABu6sPwi5tiXtL79y5QH/B9ezuwX34XydiHwR+D2uM1VQcn3Ky/UQcHpAc/kyS1I/uR7B9ao2aWI4nbFmAHfi7kd7FneZLqqqNSKyGfiUqq6yXJZruOTy+g5MBpr14MAGU3GXqqfiOnydrapD2uPccg2PXObopX1hTeoQkYu72fujuFtb9uCGLKtV1essl+Uajrn6eP5M4BFVLRnCWJZrgIKay3w4w6Gwdl22PBs3yszvVPUtcaMujcddwqvvrZOC5bJcwyDXObh7sx9V1ecSni8DZicus1yWywyN4dDG+hngh0AjrgfpYyKyFbgQN24k9oEAAAWrSURBVGrJ5qH+0LNclmsIczV4j1+IyHYRuUvc6EvVPn0YW67hkct8GKqa1g/cbDGXJy07EfgN8GXLZblGUK6FuAkCbrFclsse/j3S+oxVRAR4Dm/Ywi7qZtH4BnCVuAHvLZflGgm53sCNWfxJy2W5jH/SurCqO7y7FzhWRJ4Tkc96bRbgxkYtwY2SY7ksl+WyXJbLDIm07rwkIifgZrJpAibgbtafC7wE7AfqVHXIp/KyXJbLclmudM1lPry0nd1GRBYC/4wbpWQ/UKmq54vIeNy4shtwk2RbLstluSyX5TJDJp0vBd8APKluYufPAzNE5Cp1c2S+Blyo/pyOWy7LZbksV7rmMimQzoV1IW5ge1R1F/BfwI3ec1/2nrdclstyWS7LZYZUWhZWr4H/m7ib8wFQ1T8ArSLyBeB84NeWy3JZLstlucxQS/fOS13TKYVUNS4iM4E/48bc9K2buuWyXJbLcqVrLvPhpW3nJQD1pjXz3pRhVX1XRJYBdZbLclkuy2W5jB/S+oy1N+JmikBV435nSWS5BsZyDYzlGhjLZQbTsCusxhhjjJ/SsvOSMcYYE1RWWI0xxpgUssJqAklExorIm96jVkSqE77PHMB2bhCRCSnIs1BELh7A+kXerRMD/T0hEfnmEa57k4jc088654nIqQPNYYw5elZYTSCpaoOqLlDVBcDPgZ90fa+qHQPY1A24cVg/rIXAERdWoAgYUGEVEcH11D+iwnqEzsNNpG2MGSJWWE3aEZHPiMgq7+z1372zvIiILBWR9SLytoh8RUSuxo27+vveznRF5H+KyEYReUtEHvCW5YvIr73trxWRxSKSA3wHuM7bzpVJ25knIqu9594SkenA3cBsb9ndIjJa3Awmb3jrXOL9bIWX4b9w48P+Ahjl/dxve/m/3yQilSKyioSCKSKXichKL/PTIlIsIjOAm4Cve9s7TURKRORREVnj/R+t6BqTahqASWHtYY/DPYA7ga95Xx8HPAZEvO/vBa4FTgH+nPAzhd6/LwEL+thuDZCZtP4/A9d4X48BKoFsXIG6p4/t/Adwtfd1lrd+BfBmwjoZwGjv62LgXe/rCiAOLPK+jwBNffyeScD7wFggEzem7D0JWbt6+X8B+KH39feAf0jYxu+BU72vpwJv+/362sMew+2R1gNEmBHpAuAkYI27ckoOsB14CneG+DPgT8DTR7CtDcADIvI4rlgDXAT8TUI7ZzYwuZ/tvALcISJTgEdVtcrLlkiAu0XkDFwhLReRcd5zW1R1zRHkPRV4VlUbAETkoYRsk4GHvPbkLNwBQW8uwO2nru/HiEiOqu4/gt9vjDkCdinYpBsB7teD7a2zVfUfvWJzPPAicDPukmp/Poprvz0JWCVuDFcBPpGw/cmq2leRAkBVlwKXA+3A/xORs3pZ7dNAAbBQXbvxblzRBmg9gqz9+TdcO/Q84EsJ204mwMkJ/78yK6rGpJYVVpNungE+2XW25/UenixuHktR1Ydx7aFds4PsA0Ylb8QropNU9TngG8A4IBd35vvlhPVOONx2vHWmq2qVqv4U+COuwCevXwDsUtWoiFwIlPW2LVWNetvs7WrSa8B5Xo/jTCCxrbcAqPY6QH0mYXlyjmdwBx5d2Rf0lsMYc/SssJq0oqrrgbuAZ0TkLdwl3xKgHHhBRN4EfgV8y/uRXwH39dJ5KQI86G3jDeDHqrrP23ae1wlqA659F+A5YL7XOahH5yXgWhHZ4P3uWcADqloHvO5t525gKXCaiKwHrgHePcx/85fAW8mdl1R1B67N9DXcmfnGhKfvBP4ArKbnWLOP4w5E1orIabiierrXgWoj8NnD5DDGHAUb0tAYY4xJITtjNcYYY1LICqsxxhiTQlZYjTHGmBSywmqMMcakkBVWY4wxJoWssBpjjDEpZIXVGGOMSSErrMYYY0wK/X+pppOvBTYfbgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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qVjlZhJOqINa1lfa2Rlo1j8TMDO6pU3i5/Nk/XEqckRGckRFKTz8DQqC1t9XHZI2+PtR1tO78UglnYgJ3YhJ3YrwupH6lemZXgQIwAUwKcM78KoCKIUw8I4KCgioFilBwdYOI59EQ2VALklFqrlNlXspArU0Nkv2FqgRLzcWq1CI06xGTYjbgBlDE3P5aEQJfSGzfxvJsLK8KMQPXipFyIihCQyCpaC5CNWgpGqhKBEUXCOPcSugLSTFWpVCzQguxCo7unfU9hmKQNtMkjSQpI1Vf727bzaNHHw0sTqOBolukYBf47Z2/Hc4gcgUydjLH4WfGyE9XSDVHufaODjo2zg0wSCnxfYnn+PiuxHN9PNvBK2bwSjm8YhavlMcr5/HKBfxqCc+y8Hx1gVj6voLnx/H8ZK1NxZdL/3bL0y1MWv0owkXBI1dVeH5gN7faT9FxsS5MyIq56kU1FU3gug6e9LFlkAbh46Mr8766JynPFDk5U+SkKjBTUTpueSMd7Q2ki0X8oSHsgQG8mczZTyYl7tg47tg45eeeB0BraQks2ZrQqumzjQAujW9ZdcEMrM9x3IlJ/GLx7N0BSswJqXVGdwWgIdRIsCgaLdIhI0vEfI2o1HHxsYTHGyLX0X/dDavu+9n65voOlmthOTa2Z2F5VlCw3LNwPLt+rAl0VxLMKBqaqaCi4OFj4NMkIxjq2X/Gtu5SiNUENF6hGLWQypylHdWiNM0TyrSZXiCcKTOFqS7v5u9L9S2Y6/JXr//Vy0dQh/bAy1+E7Clo6IOdD7/urBspA/Fzbb++dh0Pz6m1OcHrqeEix16cQNUEiioYyVoMHpxhw5YUEdPHtyp41SrStcCtwux63m91ISpw4ePXObcTRbgEiQkamuZhKBUO524JRfUy5KofUz354mF+/MMfY2o6qlApWCVKToXmaJpY9ByuWk1BT5i093TQtaWXllgMRkawBwawB07hTk6uuv9qY2Pdii2/9hq5r3wFd3oarbmZhl/5d6Tuumuh23ZiAi+XW9U5KswJaXleu1BVRCQGWgyEAX7gXtU1g7ZoE+2xZsyTDuPWOEesWvUW32Sr2UtHpIPotU2r6ocn/bpYWt5C4ax6FlKuPNM/Ma5T8i0GlWnK2MQw6PWbiSsmxfY5u1sKKEes+jio2yCIJGKkzEAc54tl0kiSMlNX5jimlMHN3CmDXQK7PLc9ux7fDwe+EYzF6XGQTuCKvOlh2HAT6DEw4gvXmsnSfvy15VwW4Sy+5+M6i4TQ8etr117c5tWO9fEcr95+xl3O98F3QXrgBevRgSputYrqFYN9QsETJpoq6WyaWcerIUCLBNdeM+dtB+tT+6bQnOm6dyiiVWg0Jykld/Lu/3jPys8SjqleFK56UYVAWPc+s4dcKUc6nubmO3bR0dnByKHTjJ4aYqqUxZNnd/8JTUGNG7R2t7FhUw8dHR0Yno99agDn1CmsgQHc8YX5sGejcuJEkHep6wjDqKWiOPW8y9ViURNSoKiqKNEIIhpFiUSCP06pIS2JdAIh04RKa7SR9lgzjWaqPs5oDeSQjo/QFISuggLS9hGGQmRrI/i1Un2+RHo+tmtjOVUsN3DT2p5dF07HX+xkPn/aRpNUhYWYPx4qwcQgv1MimgyM5jixlhSpWJqUmSKhJ66MMUzPqQljKQhasYuBMC4QzCWE03fP/rmDz4BjgTYvYt21QTeh946l3yOUpcXWiAXCrEeDbSMRbOvx4LUWWbEYDx/J8Mw3jge1g1WBY3k4lkf3tkZiKbNmQQaC6PvL/D1JSRCJ5taW+duLXy+xb4kErFNjzWh+MRjSQAAS6UtcJUFfx/SKvtvSiODBZjnhVI16CUhFU1A1BVUTtbXCyVen8EoFdHsS4VXQTR21uZtoaxtvevjalfciFNWLwutCVM+G9CTV0QJjh08zOjjMZGkG5xw3K6EpKHGdps4WNmzsprOzk0QigV+pYA8OYg+cwh4YwBkdgWVuCvnvfQ/pOAvyYGeLUqQeeGBFfXdUhUxDA1OmSU5RUKJRRDSK0HT8iotfdvErbr0PilBoiTTQHmumOdKAujhgRxWgCXJHxhkSY8woWdKk6FBbkfc0MNNUYsaaIVvNkrEy5Kzc3MOIBCFFLaeyVu+1tii1dkUq9f0LjznzPUIK4mqMlJYiocVpfE2lUMyDJtAVDUtxcKVLX9dmtnzwthVdr3Vj1sWaGYDUBrjuXdCypSaOlaUFc75QruHDxwKO/jAQxPliJ2Vw/mvuW9tzzYrxfAE2gte2jJMtRMgWDLJZlWP7LVzLQXXzwQOFquGpCTTToLNbnEUk57Wd4yH4fBgdj+D6KqqYE1xPKmiKR2f7XJyCIiSq4qEqwVpRfFTDQI1EUSMx1GgcNZpAjSdRYmnUWBJVV1H1hWI5uyi1NkVdukLT2Mkcz3/7JEZUw4iq2BUPu+Jy67s3LmndL0coqheHq35M9VwIVRDtTrGx+wb6vW3YYyUmDg8zenqYieI01SXGS6Tr4+UsJnPDTB0f5bW4Trqtkc6+Ljo7O0lv3YoQAt+ycAYHsU8FImsPD4MX/MH61eqZOa+1ohRnoAi05ha0tjZkcxMzhsGE4zBdno0alKiOj1928aYdpDX3GQJBU6SBjlgzLZFGtJrl5ksfy7NxTI9Kk0exwSITL3Ewc5DDEy9wx8Q2mu1GThnH+MeWR4lOttJcbD7LhaxVzQl6xEpueaZq0mA20BRposFsoDHSWF8vTi+xrskz8th+TjvDTMgMDSLFJn0jG964bQVnWiVSBmNldnmeCJYWulbtYrB/8gic+HGQ+6qoMH0Mjj0OG26GROva9201mIkzLVXPCdrXGunXrkmRalYnW0yQK8XJlhKU7YXj0XY2sAjrxYp9B8UqY1cTEL0Qi3B5VMVHUzxU1UNTfDTVQ1U8NHVuu7l8isOl2zGUKoZi4fo6jjTY3fA9OrbfiJpIoyabELEmiDXD7DraGKS6rBMdG9Pc+u6NC9zlO9/WsypBDbl4vC4s1X2v/CPf3PNZhoVLV7SVB7f/72y/4X1nfY90feyxEtPHRhk5NcxkeYaSUznre4QeWLDx5hSdvRvo7OykqampXjJN2jb20DD2wACjn/oUfrm8oCyidF1EJELr7/w2elsbWns7WmsrorGRielphoeHmZiYwPd9pC/xqy6yZo1Kd7aMeVBDNqFFaTCTpIwYEh/Hs3F8F0fazEQKTCSzzKSKVEx7QerI3qGnsUrjGPXMGYmNj2km2ZXaHKS/CKVWQEGZExNRa1fm2hVVI2020BBtpjHaMiegkQYazUaiWnRVVXPmplhbRSED369ZhKVFwlgTRbs0ZzHOiqVTDkRiJZyPi3WtULSlXbRGrS17OrCgzRToEajmwCrA9e8NLGpnvgVduy7LBt0sjZRQdQyyxTi5coJsMUHFOXuBlJVahBBUkdLmC2FtO1h7NUEM9i3Ynt2nBmJ6xs+sbllHAxesHoND32Es28jh7C3k7UZS0RLXth2kY4MK7/nMqq7L5UhoqV4crnpR3bf/K/zn5/8U1/eIAo4EVxG8teseNm55IKiTqtRmnlCCKZpm22Zfa76KNuVTPpVjYnCMqUqWvH32yFthKChxg0hDjM6eDXR0dNDa2opaE9GZR7/G5H/9c4Smg2EEky/bNq2f+ARND/0inucxNTXF8PAwY2Nj2K6NY9k4ZQu/7OBXvPq0X7OLoajENZOEHqlbpACu5jGTLJFJFckkS3jaEoLhOVCa4smRZ4hKf8G0XxJJRcDd0Q1nvC2KSpOi0yB0GhWjttZpFDppoQepMhDcxGaT19XauNLstqrXxpzM2jHGwv1arW3yKBz5VyiMQKIdNr8VGnoXWpJ1kawJpXv2B6ELZi1crEI9w2V65pjmvPXsMapx7nHM1Ub/zo7vOpUzA5+cMtIqU85XyU17ZDOQyypULbEqd2xlOsuM3UVEK2EqFTw0XF9nR+pxOq7vDazHmlWpiGXynwG0aG1cNxY8NNS3o4v2LT5umWs3tAd+8l8gkgIzGTyAVPOrLrJwuRKK6sXhqhfVP/nSO9hXHmb+rdVGYiLYldoI8VZItAU38RWgegrNuSQtmSR6RqPkWFRqT/diXgF0UYs8UFCQhkBGBUpSp6G9kZb2Ftpa25j42pfgS9/CyFWw0lGqD70d/y1vYmJ0gsxkBsuykJaHUgXDVtG8M4NuDEUjoUdI6pEFaUKlqEUmVWQmVaQQqy5dyEDKwHopjgdJ6Ej2loawhIKxYKZNSdrz+UDjjTQoBo3zhNMUFykQqDi5sKqM5wRW1aV2sy6wVEVgPUo3sA53vH/JMcZAGOeJ5ErE8RIhpaSUtclOlMlNlMlOVLCrS8QczEbTzl88F3wHIV2SMZuGRJmGeIHU4FeYzsc5nHtDYBGaOa5N76GjuQA7/+0iEYwuLZZaJPCarDVXcQpSKKoXh6teVD/697eSdysUxCLLC8ndsa65A810IK6xpsCFuQJUV6E5n6BpJoGeUSk5FmXXOutcJI7mUjUcslqRCWeCiBvB8AxcXGzVpjHaQMpPYjoapq2hyDNvHJqiktQiJPQIZm0sx1ck2USJTKrETKqIbZwl2Mq1EMUJEuUMSc8lITQSQiMpNCam9vMt1SONoEEGUcUFIfltx2B7910rui7rwsV2s6pGTfQSc9ahkZgTydntmRPw3P8MxtUiDWBf2daN9CWFTJXcRIXseJncZAXHXl1QkKIIks0RGtpiNLTFSLdGUfV5v+Or3CK8XAlF9eJw1QcqdZnNTDAJ+MGcqkgcIM4isbJywTKjBsEHibbA2jiLAeFpPhNNeSaa8miuQlMuSXMmgZ5VKTsWJdeqTfk1h+5q6K4Gtk/E60JFRUHUatN6REoaSePM/FlVKCT1mpAqOkIIqobDaCpLJlUkmygjFYmCQtyI02oEKSUJY3Y2kyjJ/CjJ8UMkMpPERQzFWKJAfup6tk28zDfjMYZNnS40ftVR2X77h4NC3p4VWIiuXdt2giR4z65Zj1Zt36L9FxqtaRUDEVtwUfSg/ayIeUIYXzjmWBfJRZajEV954En79ZDsXGjd3PE7V4w4+L6kMF2tWaFlchMVXHd1M4WriiDVGg1EtD1GsiWCqp7FiuzeHQjoFXrNQkLOxlVvqe7b/xX+8sW/IKIYRBWdglum6FX4RaOL3lgrDj62lLW1j42PM/taj2InO3GS7diqiuM5OL5zzpxW3VFpziVpyiYwchpl26LoVvHmBb9kKkVUqaCgQG1yOh8fT/g0RoPoTEUIElqEpB4lqgVjvU4avBYV0WYSaYiTNJMk9ARJI1jH9fjC4J/8CAw+C0MvBmONZyPeCr23g6LDgW+trQvM984U33OKszW3/cqXAotGNYLrpaiBezGShls/Ms/FOs+qNOKBuzCciL6O5/kUpqp1Ac1NVvC8VYqoppCui2iUZFME5WwiGnJZEFqqF4er3lLdfsP7+F3gm/v+P4atGXrMJh7c/gjbr3lXMEZ3+rlAPJaj7EB5KLDSeu6CzptwFRXHd+oFDhzPwfbtufXsPs/Bqdq0jHtoYx7OdJWMVSRnF8lWSrVx17kC9UIGk1B3JzppjzbRHGnEjJrEOtPENjSgd8RRjBW4pp0qjOwNXKbZwbMfq+iwYWfgQm3aNDe2t+lNK7m8K0epBeNwnhMOtG8PXYbnwcjRDK89MUx2vIyiKcQbDCKJ1U1fqOsq6bYo6bZASJNNkVqBhJCQkMVc9ZbqiiiMwennYfjFIHDnbKhmIELdt0Lz5lUFmPgVF3u4iDNc4Bs/fYyMzDObCKOioCBoEineedf96B0x9I44auMKb2BSBuN7g8/C6MvnTo1o6IWe26FrVxD4cSVwFQeRrBVW2SE3GVigQ4cznHptGkUNatn6nsR3Ja19CWKp5QPzdFOtjYdGSbfFSDSYoYheBYSW6sUhFNX5+D5MHQ4EduzVc5eBizUH4tr9BoifpTDCEhz4wtM8eXoPwvNRfYGr+qCo3N2zm+t/5c6Vf1A1D0PPw+BzUJo4+7F6PBChntsh3XX2Y0Mue6QvKWYt8jURzU1WqJbnqjONHsviOj6qNuea9VwfTVfo3NJQbzMjGg3tscASbY8RSxmryh8OuTIIRfXicNW7f1cFf06bAAAgAElEQVSFokDbtmCxy4F7eOj5oPzcUpSng7zJI/9acw/fBp03rSg9Z/Obb8T7js0R6zR5r0yLGmOr2cPmN9947n76PkwcCNy7EwfOXaig5VrovQ06dqxr5ZeQ9cV1PPJT1bqI5qfOHlRkVz00Y+FYp6IKPMenY2MqiMxtixFN6qGIhoSsEaGluhIK44G4Dr2wpu7hVVcIKk4GY8CnnwPrHHO9RhqCoKOe24I0oZArjmopcOUGIlqmmDl7utZiZi1VM6phRDXMaPAMHU+bqyrEHnJ1EFqqF4dQVFfDat3D0SbouTUQ2FW6h+t4Doy+Elil08fOfqxQoePGIOio5dow6vUKYtaVm5uskJsok5+qUC2f4/e1BIoiSDZFSLdGcR2PQ8+MYcb1CyrEHnJ1EIrqxSF0/66G1bqHKzNw5HvB0rwlENcNO1dWvSl7Ogg6Gt5z7lJ7iY7AKu2+JYiMDbnscR2P/GSV3FRNRKereKvMD4UgqCjdEiXdGkTnJpoW5oim22JhIfaQkIvI68ZSlVKu37jRat3DnTcFbtnF7mG7HEQgDz4H+aFzf07XruBzGvsv2zJ3IcFvzyq5gRU6FeSHlrKrc+XOEksZgYC2RMPx0JBVEVqqF4fXhaiOncjy1FeP4Xs+rb0ptr2xc32e1lfrHnYsKE/Vihpogbv4XOOfjRsDq3TDzSuuVxxycamX+quPh1awKufpym0OXLnp1hiplghGJHQuhZwfoaheHNb1L1QI8QDwGUAF/kZK+f8s2t8L/D3QUDvmD6SUj61lH8ZO5njqq0cpFxwUVTByJMPU6QJ3vW8LnZsb1/JUq3MPL1UgfvrE0gXijUQwNttzGyQ71rbPIRfM2Mkcr/10iOmREpquEEubRBKrj7I2TJV0a5RUTUSTTWZYqSgk5Apj3URVCKEC/wO4DxgCXhBCfEtKeWDeYf8H8BUp5V8LIa4HHgP617Ifh54exSp7c7l6Aiolh5996Shv+uC1tPen1iex3YhB/xuDZSn38Myx2hRnteo2s+uZYzVRFYE4994ObTcElmzIZcfggWl+/tVjOI6HogqsikthxjpngQWAeM2Vm2oNis6HrtyQkCuf9bxT3wock1KeABBCfAl4EJgvqhKYzSFJAyNr3YnseBmhBAWHZlFUQaXocPCZUQYPzLBpZwvNXYn1u6El22Hbu+Had865h48+fmYlI1UP6t1e+07oeUMw80nIZYnreAy+NsNz3z6B48w9tKmaAHxyE5UFoqoqgmRzdJ4lGkU3L9K0eSEhIReN9RTVLuD0vNdDwG2Ljvm/gB8IIX4HiANvW+qDhBAfAT4C0Nvbu6pONHbEMeM6juVRylr4vsT3JEYkuKGVchb7nhgm3RJl085WGtrPszbtSpjvHh7eCzPHgwmg3Wow9Zweg8Y+2Pr29etDyAUhfcnIsSwDr05hWx5WxcV3RylO7sNzsqh6A5HUdpAbaO1J1sZDoyQaQ1duSMjrgUv9V/5B4O+klN3ALwD/IIQ4o09Sys9JKW+RUt7S2rq6CamvvaMDx/IwohqtvQnMqIbvSdJtC63E3FSFlx4f5NWfnKYwU72Ar7RCdv1K4P5t6IWu3YE1iwwmaQ657JBSMj1c5IXvnuTIC+PYVm2mIn+M4tQTuPY0UtoIstiFp2jqLHLjPV30bGsi1RINBTUk5HXCelqqw0DPvNfdtbb5/DrwAICU8hkhRARoAc5RxHbldGxME9/VxOP/epKI5WM2mNz69i46TYPxk/kz0hqmR0pMj5Ro70vRv6OFWGp1M3qsmHBOySuGYqbKsT0TZMbLZ+zz7APBtHYEs8C4dhVFjeE7B4DQ4xCyNrw8mOFLL5zmxGSR/pY4D9/ay87ecHjocmQ9RfUF4BohxEYCMf0A8PCiYwaBtwJ/J4TYBkSAybXsxMuDGT7z8iCZlI+uCnzf4scvn+Jj927hnnf0c+rVKaaGz5zoevxUnonBAhu2pOnb3lIv8bamdO8ORfQyxio7nHxlirETuSVzSlVVQTcKRJIuTkXB93wUVUE3bYoz58gzDglZBsfzmShYjOUqjOUs9pya4QcHxhEIdFUghODTPzzCx+/bGgrrZci6iaqU0hVC/DbwfYJ0mf8lpdwvhPi/gRellN8Cfg/4vBDiPxAELf2aXOPE2Uf3DmO7PsZsIIkqqNg+f/mjY7y2rcDOnga29nWQPZojN7mwcpGUkuGjWcaO5+je1kTP9U3oK5nPNOSKxnN8Bg/OcPrAzJITeAugY1Oa/h3NDO+X2GVvgUfDcx3scoXM2AiNHRsuYs9DriSklEyXbMZyVcbzVcbyVcZzVaZK9oLAyhcGZhCI+j1MCEhGdB7dO3zBorpnz542TdP+BriRSz8ceKXgA6+5rvuh3bt3n+FVXdc8jVrO6WOL2j41b/sA8Mb17MOJqSK26y+I7NVVQclyyVUcnjgyyRNAd0OE6zYniE5YWAVnwWd4vuTU/mlGjmTpvbGJrmsbF5SCC7k6kL5k7ESOk69MYVWXLtbQ2B5j8642kk0Rhg6+RjSVJj8VOFcUVcP3XDzXo6W3k1d+8F1uffDfEEs3LPlZIa8fipY7J565QEAn8lVs79w2RMlyiepzD/Nl26OvWeP0zJnDEatF07S/6ejo2Nba2ppRFOXKqgR0ifB9X0xOTl4/Njb2N8B7Fu+/6pMfN7UkiGoqluczVbSxXR/Hk8TNhV99KFtlKFtFQXJN0qA55xNHLMhhdRyP4y9NMnQoQ//2Fjo3p8PJm68SZkZLHN87QTFrLbk/njLYvKuNpg1xhBBUigWOPPdz4ukG2jdtITs6jOs6aLpBS28X8XQDTtXipe99mzc8+EsYkStkIviQC8J2fSYKs+JpBdZnvkphmYe0lRA3NSzHJ6ILIrpK3FApVl16mtYkU+HGUFBXh6IosrW1NTc2NrbkPJ1Xvag+tKuLT//wCKmIzoZ0hLG8xWiuwjVtiSWP9xEcdh2ISlJFjw1FaDb0QIRr+mlVXA4/P8bpgzNsvKmF1t5kmLR/hVLKWhx/aYLpkdKS+w1TZeNNLXRubqg/QEkpOfjkT/DswKMRTzeQamnljl/6ICNHDnJizwv195dzOV7+/nfZ/c73ompX/Z/b6wbfD1y38y3P8XyV6UWu2/OlMabTkY7QnoqwuzfNv+wdpilmkIzqFKsuharDh+/eeOEnAiUU1NVTu2ZLuiuv+r/ynb2NfPy+rTy6d5jTM2V6m2L8h7ddw7YNKfaP5Nl7KsOJqdKZfwiKIJ/SyCckkekqDTMeLTGDlriJoQfXslyw2f/UCMmmCJt2ttLYEQvFdR15eTBT/3fsaYrx0K6u8x5TsisuJ1+dYvRYdskgJEUR9GxroveGJjR94Tj6yJFDTJ8eXNB2za13EE2m2LTrVir5PKNHD9f35cbH2P/E42y/9/7w93EFUqg6Z1ie4/kqzgpct+ciZqh0pCK0pyN0pIKlLWUSWfSb29KWXPDb//DdG6+aICVVVXdfc801FSklqqrKz3zmM4P33Xff0k+558FDDz3U/653vSv3yCOPZN7//vf3feITnxjfvXv3uuVNXvWiCoGwLvUD3NXbyK7eRrJlm5dOZ3npVIbJor3wIEVQbTUZb5Tkpm2GhrOkTI2WhElT3EBRBIWZKq/8+DSN7TE27Wwl1RK6+i4UKSUVx6NQeyrfcyrDPz57Ck1VMFXBkfECf/avh/iDB67l5r6VT8LueT5DBzOc2j+97FRr7f0pNu1sJRI/s35vtVTkyLNPLWhr7NxA9/XbARBCcP0991ItFcmMzGWQjR8/RjSZ4ppb71xxX0MuLp4v+cmhcb66Z4jTM2Wihkp3Q5SYufo6zovRFEFb0pwTz5oVmopoK3rQWu4edrF55vhU7MsvnG4eyVXNDemI9f439EzfsbnlggZ3TdP0Dx06dADg0UcfTf3RH/1R93333Xf4XO87H7785S+fWo/Pnc/rQlTPRUPM4C3XtvHmra0MZSrsHczw6lCOsu3Vj5GaoNJuUm3SsSdt8lMlTk2XaIwbtCRMUhGdzHiZPd8/RWtPko03tRBPh7PILMb3JSXbrYllIJiFqku+tp7f5vpzlsALAzNYzlwUNwTjV3/wtdf4wK099DfH6W+J090YRV8iiExKyfjJPCdfmVx28u+G1hibdy3/UCSl5NBTT+Bac+OuiqZy/T33LrgxKqrKTfe9g+e/+SjlbKbePvDyXqLJFN3blhyKCbmISCmZKtoMZcoMZSoMZSq8OpThpcEcuqqgq4JC1WM4U2V7V4qWZGTFn90U1wPrsyaeHekILXET5TKOv/jYP7/Ud65jpouWdni8ENMUITVFyMHpUuSpY1MN17Yny80J86yDxv/9gzevSMxyuZyaTqfd2rbywAMPbMnlcqrruuJTn/rUyC//8i9n8/m88p73vGfT6Oio4fu++MQnPjHy4Q9/OPPkk0/GPv7xj/eUy2WlsbHR/ad/+qeBvr6+BVGnt95667V/8Rd/cfqee+4px2Kxm3/913994gc/+EE6Eon43/nOd4719PS4IyMj2iOPPNI3PDxsAHz6058efPvb375iyzkU1XkIIehpitHTFOOd2zs5NFbgpdNZDo/lmc2skLpCeUOEarNBdMJiqmAzVbQxVIWWRCCwk6cLTJ0u0LE5Tf/2liUtnqsN1/MpWu6yAjm7Llou/nl4zRZHQEIQxZ2vOhwZL3JkPMg11hRBd2OU/pY4G1vi9DbFqM5YHNs7sWylrFjSYNPOVlp6zl7/efz4USZPnVzQtuWWO5aM7tXNCDc/8C6e/+a/4FTmUrUO/fwJIokkLT3nvIeFrBFSSvIVl9N1AS0znK1QdRZ6Ko5OlNBVpf7gZmjBb+HkdHlJUY0bat3i7EjPuW5N7epMuzudqZiaIqSuBmOwuiok+JzOVMxzierZsCxLue666663LEtMTU3pjz322BGAWCzmf/e73z3W1NTkj46Oarfddtt1Dz/8cPZrX/taqqOjw/npT396DGB6elq1LEt87GMf6/3ud797bMOGDe7nP//5xt///d/v+upXvzqw3HkrlYpyxx13FD/72c8O/+Zv/mb3Zz/72dY///M/H/2N3/iNno9//OPj999/f/Ho0aPG/ffff82JEyf2r/T7hKK6DJqqcGNXmhu70pQsl1eGsrw0mGUoE9wgfVOh1BNFrXhEJywoeYzkqozkqiQMjeaEgXPEZ/xknq6tjfTe0HTFzIU5f+xyQ0OEt9/QQV9zfIFA5heJ5nyrfj2YjYCcvdEBS0Zxu75kYLrMwHSZn706TmzCIm1JkhGdZEQjGdHQapasbqj0bW+m65qGc5YRtMplDj39xIK2dHsHvTfuWPY9sVSam+9/Fy9+52v4bnB9pC959fHv8Yb3PESyuWVV1yBkZZQsd4F4DmUqK4q+Xe7BrWy7dDVE6EhHa65bk/ZUhIS5Mtft1ULF9lRTEwueRDRFyIrtXdBTxHz37+OPPx5/5JFHNh45cmS/7/vid3/3d7ufffbZhKIoTExMGENDQ9quXbsqn/zkJ3s++tGPdj344IO5Bx54oPjCCy9Ejh49Gr333nu3Avi+T2trq3O28+q6Lj/wgQ/kAHbv3l16/PHHUwA///nPU0ePHq27q4rFoprL5ZR0Or30eNEiroy7/CUmbmrcubmFOze3MJGvBuOvg1lyFQcvqlLsi6EVXaITFmrVp2i7FGdcBmfKNMQMpgsWw0cz9N3QTM91Taj6pc1xlVJSsr15IunWrEyHAyM5vvfaOLMac3yyxI8PTa7aBbYWmJpSF8LGmMZPDk2SjGrEdJWpko3leGxsPjOtQLiSyKSFmQn+pkpAyfYYywf7o7pK8+YUW29uIt6RWFFa1OGnf4ZTnXP7ClXlhje9FaGc/d8y3dbO9nvv55UfPsZsRJTnOLz0vW9z63v/DZH40lHoISuj6niM1IRzVkgz5bPeS5clbmp4vqQpphMzVGKGhuv7dKQi/Pa916xxz688oobq2a6nBBZqgOtLETXUNXuiftvb3lbKZDLa6Oio9uijj6anp6e1ffv2HTRNU3Z1dW2vVCrKjh07rL179x549NFH0//pP/2nrscffzz/vve9L7tly5bKyy+/fGil59I0TSq1v19N03BdV0Bwf9y7d+/BWCx2XpFooaiukrZUhPtv6OC+be2cmCqxdzDDgZE8VkKjEFfRCy7RCRvF9pFApmyTKducnCpxcrxIxyuT3HhLOxtWYB2tFsfzF7lcF7pdC1WHguVSrC7vgn1hYAbHk8zOa3AuF9j5ENXVulim6hbknCU5u704AvLt13fULeibexr5xV0b6GqMMTBVZmC6xMBEkfJQmciUjVjmCzopjXybyaji8torI/AKpKIaG2tjshtb4rQlzQUWyPiJY4yfOLbgc7bsvpV4w8oCR9r6N7H19rs48sxcgJNVKgU5rO9+CM1Yp/rSVxmu5zOaq9bFcyhTYbJonXcKS1RX6WqM0t0Ypashyju3d/C5n50gGdFJRDSKVZey7fNLu7vX9otchqxkzPOZ41Ox//bDI11JU/MSpuYVLVctWK76e/dtHb7QYKVZXnrppYjv+7S3t7u5XE5taWlxTNOU3/72t5MjIyMGwMDAgN7W1ub+1m/91kxjY6P3t3/7ty1/8id/MjYzM6M9/vjj8be97W0ly7LEvn37zFtuuWXVUb533XVX/s/+7M/a/viP/3gc4Omnn47eeeedlXO9b5ZQVM8TRRFsaUuwpS2B5Xpz6TlKiXxSw8i6RCYtFDf4i3d9yXjBYrxgcWQ4T1tLlDfc2cWma5vOainNtyqLVZf8fIGsuvWctXzVxVommnU1LOcCK1lnd58JAQlTI2lqi0RSXyCeiYi2ZCDRSlguArI1YdKvaBwfsskbkkKzXr8+FSd4iPaiKuV2Ey92pqcqX3F5ZSjHK0PBBPIxQ6W/OUZfc5yepMrAUz9dcHyqtY2+HTevqu+9N95EJZ/j9P599bbi9DSv/uh73Hz/u85p8b7e8H3JRMGqi+dwtsJorsISVSNXhK4KNjQEAtrdGKO7MUpz3DjDfZswtas2deVCuWNzS/n3YHh+9O9H7tk0dqGCOjumCsH97q//+q8HNE3jQx/60Mw73vGOLVu3br1+x44d5Y0bN1YB9uzZE/3DP/zDbkVR0DRN/tVf/dWpSCQiv/SlLx3/2Mc+1lsoFFTP88RHP/rR8fMR1c997nOnP/ShD/Vu3br1es/zxG233Va48847B8/9zgCxxqV2151bbrlFvvjii5e6G8uyID0nH7ggI1M2YomcNrfkIKo+vpC4SZ327Y20b2qoW5b5mpCeT2DP+bI4ylYI8KUkFdF5aFfXGWJZ3za1SxLdmJuscHzvBLmppR8kVVPF6E8wpUtOzZQZyVZWdT3NEy9iZk4HDwwRnWTU4C3vf5imttVNQQggfZ+Xf/gYU6cGFrR3b7uB6+568+tqfG4+UkpmSvYCF+5ItrKiEn5LoQjoSEXobpoT0PZk5LKOvr0YCCH2SClvmd/2yiuvDNx0001Tl6pPVzKvvPJKy0033dS/uD20VNeYJdNzTmXxRytEpu36uJpbdNAKLlKIoFDTjMPoz8Y5OFYgvmH9x9miukoiopGKaHOCEdHY2ZPmqy8OkY7qNMb0eq7o5TYjRqVoc+KlSSYGC0vu13SFvhubz6jTbLkep2fKnJwqMzBV4nSmvGwSv5odRZs+jQfkKi65iosd62Pvz8fobszT3xyjvyVOvuLwnVdHz1mUQigK2++9nz3f+Tr5ybk63EMH9xNNpem/adeFXZQleHkww7/sGeJ0pkJ3Y5T37uxie3caKYOHpWABJHi111IGQufVtn0p8X2QBMcGx8xt+z71983/zAXHLNgPxyYKPHFkipFMGU1V6GmMnvfwQmvSrFmgUbobYnQ2RM7bGxIScqGElupFwPV8Do0V2Ht8hlOvTWHMOHgTVRQf5LyHZyHB0sC5pWlJN+W5UGqzV8xZkBpJMxgfSpgrd8GuZeWitWTsZI6DPx9lYiCP70tSrRFiqYW5wEIIuq5poG9784qirV3PZzhbCSKGp0oMTJeCVAvXJrb/Rwh7znvkx1JUtr0F5rlqpwpV9o3kSZkaTXED2/Op2B7v2N5JX3NsgZDMipFbrVB47vv41blKXhLQb7gTtbVnnijNipZEwiKxCkTKmyeCc+cI1hP5Ci8P5dGVIO/S8SSO51+SoLP5TBWq7BvO1/NBV9Ovhphed+F21dy5i8feQ5YmtFTXltBSvYQsSM+5rYc9R6Z46vMHziiPJ4GoI4kMlHHjKpU2Ey+qLmtVzrYlIzoJUyNmqGviQrxcqrfMZ+jQDD//l2M4tgcCfE8yeapIax91YW3pSrB5V9uqJpbXVIW+5jh9zXHetLUV35eMF6o8/4MfMKL7FHyB7UoQAqt/9wJBhSCAS1cUPEm9Gpft+nzrlRHe0L98pSelczfRg0+AN2+ses+TVK69Cz/RvIorszzHJoO+rTTv8mJxcrq8onzQhKnWhDNWd+UmzNfnLev0wdfY96Pvkx0fpbFjAzvvfyedW6691N0KWYLX5y/0EhI3Ne7Z3sGz7SdxCjaKK1EdCQKEJxG6yqa2RHDTcRTaNyTZsrOVROOluwleSsp5m9MHZ9j7/VM4toc6Oy+uJgCf3ESF9r40W3a30dB+4bN2KIpAz08Qy5xmS1sCicRyffS+6yl2bGRgqsR0aa6U5fkGdvnRFNUttxE58jR1c9X3iR59lvK2NyEjFz4EcL59W2+W6ldEF/hS8qatLXUrtCGmv67GmV3bppzPUc5lKeezlHPB9sSpkwwf3I+qqSiqRk6M8fSX/4k73/9vQ2G9DAlF9RJx+709PPn1Y8iICjFQSh6KJ+noTtAYn7O0MiMlXhgp0dabpH/H66f0YX6qwuCBGaZOF5AEMwNpxkIr0TBVNENl9zv61uzm69o2B578Sf21QNDc2sLtD9yLogZCkKs4nJoucXKqxIGRHJmyg66evSjFUnipNqz+mzFP7p3fAaJHn6a87c2gXViqzfyCGbOXx/WCoDNTU1AVgSICl7kiZrepbYv6tloL8Jk9Zv4+RQQPImLevsWfufB4wXCmTMlyiZlB2lTCULE9n6a4yQM3dl7Qd77c8VynLpZzAhqs7fLSQbRTAydQNRVVCyqzCUXBiMXY/9MfhaJ6GRKK6iXinjuC3Ldnf3waJ2+jN5nctLuNFkUnO3nmH9fEYIHJwQLtG1P0b28hmrz6chullMyMlhjcP0N2YuE1MCIqruOjagJFESQaTTRDJZY8My3iQjj63M+xisW5BgE3vOmtdUEFSEd1dnQ3sKO7gb6mGP/1B0dQBKgCyo5Hxfb4pd3dXNOerAvJrGAJIVDniYwQvUzujzN54OUgYE0EMwwmvCNsffMvoGn6AkGaFay64IngDcoiMRQCXhvK8Zc/OhoMG0Tmpgy71EFnXQ0RPv3DIwvyQQtVlw/fvemS9Wkt8VyXSj63UDSzGcr5HFZp9ZOvWNUKujH3MO06NmYsTm5yfC27HbJGhKJ6Cbnnju66uM4ipSQzVubkK5PkpxemWElg7GSe8YECnZtS9F0ldYWlL5k4VWDwwPSyk4Sn26JMD5eIpQxSLREcy8euuFz79o4168fMyBBDBxeW+OzbcTPptuXPsbO3kf/49rmpBa9tT606sEt2vpnXNIexo0fmGgvTFPY9y/Z7337eDw27+5v4vXnTHl4ueZc7exv59WtVnnrsOzjZKVINrbzxHQ9c8n6tBt/zqBTygWjmcpRzmbqIVktFlpxP8DwxI1Fc18WMxdAMAyMSxSqXSLe2r91JLiGzU7+5ritUVZUf+MAHpj/1qU+Nf+Mb30h98pOf7AYYHBw029ranEgk4m/btq389a9/feASd3tZQlG9zBBC0NQZp7EjxvRwiYFXJylkFgqNlJKR4znGTubp3JKm74ZmzNiVJ66e4zN6IsfpgzNUS8uXlks0mFz/xg1I3+fws+PkpyukmqPsfFsPHRvTa9IX13E48LMfL2iLpdNs3n3bOd97oYFdQghuuOetVItFsqMj9fbx40eJplJc84Y7zvuzL8egs8PPPsWxr36Bdt9DMTT8wgCHv/g/yL+wlVRLK4qqoWoaiqahajqKqgava+2z+5TatqrqKLXxxvn7VXXumPq6dsxyDyqjxw6z/6c/Ijc5Tqq1jS233P7/t3fn8XGW9f7/X59ZMlmbNGmbtmm6QJd0b6G0CKJFQDkqIAeQzR1QVMTzwwOcox4P4vkq7sqRI3IQD5aDCIgI2KMii+xdsHRJ9z1J9+zJZJuZz++P+552kqZN0sxk7iSf5+Mxj2Tumcy8c08yn7mv67qvi7zCIprdI86W+NfGhqQWTgDxCVl5I8gaUUBOQQHZI/LJzi9g6pJ38fZzTxPKziGUnUNbuJn2cJhFl1ye3AC9sevVbNYsK6K+IkR+aRsLP17NlPOStvRbVVVV4KqrrjqtoaHB/+Mf/3jfFVdcsRE6ry6TjF8jlayoepSIMGpCLkUlORypaGLX2sM0N3Re6zUWU6q21nFgez3jB9Gk/e2tEfZtraNyS60zmvcERhZnUzqrkMJxOUffBMeedvyKMMmwY/VbtDQ0HNsgMOu9F+APDMz+9Pn9LLjog6x85knCdXVHt+9e8zbZefmUOBPODGrh+jq2rniDd/70LBqLHu0jjH+tqawgK3cE0H1rRTKJW6gTC21TbQ0V5euOThtZu6+KnW+vovi0qeR0sxLRqT0xZOWNIDv/WNGMf83KG9HtzFpFEyaSmZN7tNjnjy5m0SWXJ7c/9ckbel42qflIgEPl2fiCii+o1OzOZMeLBYyZHSZn1MlHv135y14t/VZSUhJ58MEHd59zzjmzfvjDH+7zDcKZxrz/DjzMiQijJ+YxakIuh/Y0snv9EcKNnYtrNKZUbK5h3/Y6JswYSenMQoIh75271/sQ1hIAACAASURBVNrUQcWmGvbvqCd6gvnmBBhVmsfEWYUDtth73YH97N2wttO20tnzGDl2/IA8f1wwM5OFF19y3HJxm157iczcXIomTBzQPMnS0drKzjWrqChfh8b0uD5CAJ8/QFtrr6dX7TeNRolEo53Wxq3aXE4sGiHa4XyAcz5QKXX7q/pWVAUyc/PIHlFAdn7+sa9u4Uzsn++tcVNnpH9QUu2eEL6g4s9wjtHjX2v3hHosqn0wa9as9mg0SlVVVaC0tDS9Q9VPgRXVQUJ8QvGUEYyZlMeBXQ3sXn/kuCbTaCTGnvJqqrbUUjqzkAkzRxLwwInxTbWt7N1Yw6E9jZxoshGfTxh7Wj6lMwv7dJ5pf0UjEcpfeaFTU17WiBFM7UeTa384y8V9iNXP/b7TcnFr//p/znJxhYNnubhYNEpF+Xp2rlnVqXiFMrOIdLQfbdpVVaLtbYQyB+ZD1In0tdiHcnOPO9qMF86BauEYUB3NfgJZnT8N+4JKR3P632Q8ZAi+8kOb+IRxp+dTPDmP/Tvq2bOhmraWzh/mIpEYu9YfoXJLLRNnFVIyfeSALzenqtQfamFPeTU1+0884jEQ9FEyfSQTZowkI2vg/xx3vL2iU3MrwMzzzicQTF8fdf6YscxZehHrXvjTseXi2jtY86fnWHzZlZ5fLk5VObRrB9tWvtG5Sd1VMK6E6sq95BePJXdkEW3hZtrCzZx9xTWMmXwasUiUaKSDaCRCLBIhGnW/RiLOkWQk4twneuw+8ds73SfqPE53P9udxGJ/9HcB8opGMX7GTHLyC8jKjx995ne637AQzIkSbfMdPUIFiHUIwZykLqa8cePGDL/fT0lJyaA7SgUrqoOWz+8Uo7Gn57NvWx17N1TT3tb5b7ujPcqOdw5TsamGiXOKGD+toNM8uKmgMeVIZRN7N1YfN3o5USgrQGlZIeOm5aftaLr+0EH2rFvTaduEmbMpKilNS55ExadNZXrTu9n6VsJycU1NvPOn51h06RVpLfonU3/oIFvfeo26A/u7vT0zL4+573s/MVU2/i2FfYQnoarEotGEQtxBLBpl//atvP3Hp8kIhQjl5hGLRuhoaR0ekyz0ps9z16vZvPQfJWSMiBLKjdLW5Ke9wc/5X6/q72CluH379gVuuummSZ/+9KcPDcb+VLCiOuj5/T5KywoZf3oBVVtr2bux5rjBP+1tUba/fYiKjTVMmlPEuNPzk76WazQa4+DOBio21RzX55soe0QGE2cVUTw5L+kZ+iIWjVL+t792avYN5eYybcm5acvU1cS58wk31lOZsFxcY/UR1r/wJxa8/0OeWi6upbGBbSvf5OCObd3e7s8IctqCRZTOmX+0aXT8tPQUKhE5Okgp8aNJXtEo8oqKOg0Imn3pBUO/oPbWlPPC8PWqTqN/z731QH8Lanzpt/gpNVdffXX1v//7vw/ak3CtqA4R/qCPibOLGD+9gMpNtVRsqiHSZX3VtpYIW1cdZO/GGibPKWLsafknXcu1NyLtUaq21VG5uZb21hO31uSPymLi7EKKSnI9MfXczr+vorm2ttO2Weed76kFw0WEsnedR2tjA0f2HjuQOLJ3D5vffJWyc96T9n0ZaW9n1zur2bN+LRo9vhVQfELJzDmcfsZZZGT1fxrJVPPEgCAvm3JeOFlHpXHRaPTtnu6zcuXKLcl8zlSyojrEBIJ+Js8bRcmMkVRsqqFySy3RLsW1tbmDzSsOsHdjDZPmFlE8aUSfi2tbuIPKzbXs21Z3XPFONKokl9JZhRSM8c4basORw+xe2/n/ePyMmYwq7fmsgoEmPh9zL7iY1c/8jsbqY4uJVJavJzsvn0nzFqQll8ZiVG4uZ8fbKzuNVE40atJkpi0+h9yRJ15YwJihxorqEBUM+TltwWgmlI1kb3kN+7bWEu2yOne4sZ1Nb+xn74ZqpswfzajSno8im+vbqNhUw8GdDcROMJJXRCiePIKJswrJKfDWXMXxZl9N2BcZ2dlMP9s7zb5dBYJBFlz8YVb+4clOUyhuXfEambm5FJ82dcCyqCrVFXvYuuL1447043KLiph+9rs90TdtzECzojrEZWQGmHrmGEpnusV1W91xxbC5oZ0Nr1aRNzLE5HmjKSrJOa641h9uoWJjNUcqm044kYw/4GP81AImlI307PSJu9f+nabq6k7bZr57KcGQt1cByszJZeHFH2bVM78j2u6eSqWw4eXnCeXkUlCcvOkaT6Sx+ghb33qdmqqKbm/PyM5m6lnvYvy0GZ7q7zVmIFlRHSZC2UGmnVVM6axC9myoZv+O+uPOGW2sbWP93yoRgbZwhPaWCBmZAbLygkSjJ56TLSPkp2TGSEpmjCSY4d1T1hprjrDz76s6bRs7dTpjJg+OidzzCkcx/8IPsuZPzxw90o5Forzzlz+y+LIryR6RnCkbu2oLN7N91Vvs27qp26n5fIEAk+cvZNK8Mzw7KtmYgWJFdZjJzAkyY8lYJs4qZPf6Ixzc1dDpfTLc0MbhPU34AkIww0dHe4xYRBk9KffoYuBxWblBJs4qpPi0/JSfqtNfGoux8W8vorFj/b/BrCzKznlPGlP1XdGEUma++/xO8xR3tLSw5k/PsvjSKwlmJu+IO9LRwZ51a9izbg3Rjm7mZhYYP30mpy9a4vlzZ40ZKFZUh6msvAxmnjOeibOL2L3uCIf2NgJQf6gFX0DwB3zEYriLgjuLgceLat7ITCbOLmR0aV6/Rw8PlD3r1tBw+FCnbTPPfW9Si9BAKSmbRbixnt1rjg22CtfV8c7zyznzg5ed0jR4iVSV/ds2s33VWydcqqywZALTlpzLiFGj+/Vcxgw1VlSHuZz8ELPPK2FSbSu71h5hz4bq4xYD9/mF9tYohWNzmDi7kILi7LSfytEXzXW1bH97ZadtY6acPqADfJJt6qKzaWlo6HReaN3+fZS/8gJzll50yq9Pzb5Ktr75WqeRxomyC0Yyfck5jJo4eVD9DRjvys7OXhgOhzvNwnLbbbeNf+SRR0YVFhZGOjo65I477tj/uc99riZdGfvCiqoBIHdkJnOXTuDAznpq9jURiTiNwgJkhAIUT85m/gWDbzSnxmKU/+2FTudQBjNDlJ373jSm6j8RYc7SC2kLN3daLu7Atq1k5eUzdVHPS9Ylaq6rZeuK1zmyZ3e3twczMzn9zMWUzJyNz+fdfnOTWiv3r8z+/fbfF+1v2h8alzuu7fKpl1cvHrc4Jcux3XzzzQfvvvvug+vXrw+9613vmvWpT32qNhQKJXnBveRLaVEVkYuBnwJ+4EFVvaeb+3wUuAtnCMRaVb0ulZnMyc09fwIrn92FzycgzkT3kY4Yc5dO6PmHPWhv+TrqDx7otG3GOe8hlO2d82ZP1YmWi9v191Vk5Y2gZMbMHh+jvbWFnW+vpHLThk6nGcWJ38+kOfOZvPDM4yabN0PHHa/c0eNJ2rUttYFtdduy/T6/BiSgFU0VmW/ue7NgWsG08MiskSedp/d77/ler5Z+687cuXPbMjMzY0eOHPEPhvmAU1ZURcQP3AdcBFQCq0TkGVXdmHCfacC/Aueqaq2IjElVHtM7Y6fks/iSKWx58wAN1S3kjMxkxrvGJm0x8IEUrq9j+6o3O20bNWkyY0+fnqZEyXd0ubinn6Cj9dhcy5tefZHMnFyKJnTfuhCNRKgoX8euNauJtHc/rWTx6VOZeta7Ujaq2AwulU2VIb/Pr0FfUAGC4nytbKoM9VRU++O1117LnjRpUutgKKiQ2iPVxcB2Vd0JICKPAZcBGxPucxNwn6rWAqjqoeMexQy4sVPyB2URTaSqbHzlxU4rkgQyMpj57qVDri8we0Q+Cz7wYVY/9/ujzdzOcnHLWXzpleQWFh29r6pycOd2tq18g9bGxm4fL794LNPPfveAnPtqBo+WSIs/5A91mj4tIAFtibSkpD/g/vvvL3700UdH7d69O/TYY49tT8VzpEIqz4MoARLPEq90tyWaDkwXkddF5C23udiYfqvcuJ7ahL5GgOnvOm/InvpRUDyWuedf1Gmbs1zcs7SFnRG8dQcPsOqZ37H+hT93W1CzRoxg3oUXc9alV1hBNcfJCmRFIxrp9Ik0ohHJCmQldem3uJtvvvng9u3byx9++OEdX/jCFyaHw+FB8Wk43QOVAsA0YCkwAXhFROaqaqcFLkXks8BnASZOnDjQGc0gE18xJVFR6UTGTy9LU6KBUXzaVKYtOYdtK944uq26qpIn/+Pf6GhvhZhSMK6EnPyCTj8XyMhgysJFTJwzv9+n45jBqTd9niv3r8z+2Ts/K8kN5kZzgjnR5o5mf1NHk/+WBbdUpWqwEsD1119f/9BDDzXfd999Rbfffnv3w9I9JJVHqlVAYofOBHdbokrgGVXtUNVdwFacItuJqj6gqotUddHo0XZenDkxp9n3pU6TFfgzgsw87/wh1+zbnUnzFjJh5mwAmuvrOLhzOy2N9cQiUSId7RzcuZ3meuczq/iE0tlzOfeajzN5/hlWUM1JLR63OHzLgluq8kP5HYfDh4P5ofyOZBTU1tZWX3Fx8bz45a677iruep+77rpr/3333Tc22s1KSF6TyiPVVcA0EZmCU0yvAbqO7H0auBb4lYiMwmkO3pnCTGaI27dl03Fz005ffC5ZuXlpSjSwRISyc99LS1MTVZvL8Qf8+APO1IHxr3X7q5g8byHTlpxDTsHIdMY1g8zicYvDyT4qjcViPS79dt5554V37969IZnPmyopO1JV1QhwC/BnYBPwuKqWi8jdInKpe7c/A9UishF4CbhdVau7f0RjTq61qYktb73aadvI8SWUuEduw4X4fMy74ANEo1F8/s6fm0M5uWTmjWDBBz5kBdWYFEhpn6qqLgeWd9n2jYTvFbjNvRhzylSVja++dGwFF5yJ3me9533Dotm3q0BGBhNmzubAjm3EIhH8wSAjRo/BHwiSlTci3fGMGbLSPVDJmKTYv20L1RWdx1pMWzy8z7Gcd+HFNFYfIRjKJDMvj/ZwmPZwmEWXXJ7uaMYMWb1q/hWRL4pIQcL1kSLyhdTFMqb32sJhtrzZudk3v3gspbPnpSmRN4ybOoNzr/4YOQUjaaqpJitvBOdcfT3jps5IdzRjhqzeHqnepKr3xa+4sx/dBPxXamIl17aVb7Lx1RdpbWykYOx45l7wAcZPszeWoUBV2fz6y0Ta2o5u8wX8zH7vBcOy2bercVNnWBE1ZgD1tqj6RUTcPtD4FIQZqYuVPPu3b+HNJx+lvSWMzx+gsfowe9avYfL8MyiecjpZI/LJzssna0Q+WSNGkJWbh/i8vTaoOebgzu0c2tV5wPjpZy6xQTjGmLTobVH9E/BbEfmFe/1z7jbPK3/5BcQnnU8riHSwb/NGhOOPZMQnZOaOIHvECLfQ5pMdL7h5+QSCwYH+FcwJtLeE2fz63zptGzF6DJPmLkhTImNMX/n9/jOnTZvWEo1GpbS0tO3xxx/fNWrUqOiWLVsy5s+fP2fy5MlHJ7V+5513NmVmZnp6pZreFtU7cQrp593rzwMPpiRRktUfPoh2eQl8/gBtrS3d3l9jSktDPS0N9XSeZdGRkZ1NVt4It9A6BTc73/k+GMq0JscBtPmNVztNIi9+t9nXWhqMSYnmFSuy6373u6KOfftDwfHj2gquuKI6Z8mSfp23GgqFYps3b94I8I//+I+Tv//974/+7ne/ewCgtLS0LX7bYNGroqqqMRH5JfAazhJtW1TV+1NbAPmji+loaYHMTCLt7UQ62om2tRHKzDqlx4uPoOy6nBg4M/fEm5KPHt2632fm5NqbfRId2r2z0wLdAKedsajT5PHGmN6p+so/97j0W6SmJtC2dWu2BAJKIKAdFRWZza+/URCaPj0cKCw86QoyJT/8Qa+Wfjv77LOb161bd2pvzh7Rq6IqIkuBh4HdOOtWl4rIJ1X1ldRFS47ZSy/gjd/+LxnZ2YSyc2gLN9Pa1Mj893+IvMIiwu5RabihnpbGBtrDp/6hK9reQWP1ERqrj5+eUvx+svLyyMrLP9q03FxXy5517xCuryV/zFhmL73ABpX0QkdrK5tee7nTtryiUUyef0Z6AhkzDHRUVoYkEFAJOku+4X7tqKwM9VRUeyMSifDSSy/l3XDDDUffQCsqKkJlZWWzAM4666ymZcuW7e3v86Rab5t/fwi8X1W3AIjIdOA3wJmpCpYs46bO4Jyrr6f85ReoP3yQ/NHFLLrk8hMWr0hHx7Eie7TgNtDSWE9rU2O3Czn3hkajhOvqCNfVUc2xeVn9AT8+f4C6g/vZtfZtJs87g6IJpQQzMwmGMsnIzCIYChHMzCKYGb+eSTAzE39geJ5mvOXNVzt9+BGfMHvphfh8NnetMakSC4f9kpnZaek3AgGNhcP9+sdra2vzlZWVzTp48GDw9NNPb/3IRz7SEL9tyDb/AsF4QQVQ1a0iMmhG7PTltIJAMEhe0SjyikYdd1ssFqW1sTHh6LYh4Si3vtPanT2p21/VaV5WET8ai1G1aQPHdQKfgC8QIMMtvvGi6xTeTIKhLOdr/OLexx8IDNp+3/3bt/D2c09TtXUTocysoyuuTFm4qNvXyxiTPL7s7Ki2t/viR6gARCLiy87uV1dgvE+1sbHRt3Tp0mn33HPPmK9//euDdm3t3hbV1SLyIPCIe/16YHVqInmXz+cnO7+A7C5LZ4FzvmR7S7j7gttQR0drW6f7t7W2EMwIdX78kwyg6k4sEqG1qYnWpqZe/4z4/UeLbeJRb/xrRmYWdYcOsOvvq2iqrSGvaDTTzz6H0ROnEIvF0FiUWNT9GouhsRixWNT5GnW/dt0evx7t/n4nva+7veHIYao2lyM+P8GM0NEVVybPP4MpCxb1+vc3xhyvN32ezStWZB++994SX05u1JebG401NfljzU3+0bfeWtXfwUoAeXl5sXvvvXfvVVddNfXOO+8c8kX188AXgVvd668ySCZ+GCgiQig7h1B2DiPHjj/u9o72Nrc5uYFwQz11B/fT0ui0ckQjHaAQi0ZOeQBVb2k0SltzM23Nzd3e3rVZuuHwQfZuWEvxaVOPW4dzIB3auR0Rwe8uTxY/wo9GOmzJMmMGQM6SJWFuvbUqcfRv0Q2fOZCMghp37rnntpSVlbU88MADhRdeeGHvjxY8pMei6k708JCqXg/8KPWRhqZgRojgqDGMGDUGgMzc3KMDqDKys2lraqK1uZEFH7iEkWPH0dHWSntrKx1trXS0ttLR2uJcP7qt5ZT7d0+ma7N04nJh6Syq3R3Z548Ze/SDiTEm9XKWLAkns4gChMPhNYnXX3zxxe3x77dt21aezOcaCD0WVVWNisgkEclQ1faBCDUcdDeA6qzLruh136+qEulodwtuQtHtWoTbjm1rb21Fe1jkNxnN0qkQyswi0tHuFHmBrLx8Qjm5w3rCfGOM9/S2+Xcn8LqIPAMcbTdUVTty7Yf+zMsqIs7Rb0YIellYVJVoJEJHa8vRI9721hY6WtvoaHOKcM3+Stqam/H5nYFTiBBp7yC3oJARY4rx+Xz4/H7E50N8Pnw+H+Lzu1/d635/568+Hz7pfD3+M/HHij9O/Puuz3Fozy5W/eEJQtm5ZOTk0NHSQns4zOxL//GU9p8xxqRCb4vqDvfiA/JSF8ekkogQCAYJBE+8pmZB8djjzuttD4fTvrrJ5HkLCWVn9/rUKGOMSYfezqj0zfj3IjJWVY+fTsgMCX09r3egs3khhzHGnMipzB6wHLCpa4YwK17GGHNqTmUy2sE5c4AxxhiTYqdSVP876SmMMcYMSyJy5k033TQhfv0b3/hG8W233TYe4Lbbbhs/ZsyYeWVlZbOmTJky+/rrr58Y7eEMhnTrVVEVkbNFJA9AVf9LREaIyJLURjPGGOMllVtqsp9/qLz0qR+8PfX5h8pLK7fUZPf3MTMyMnT58uUj9+/f32135M0333xw8+bNG7dv316+efPmrOXLl3t6sGxv+1R/Tud+1KZuthljjBmE/vLL8h6XfmtpbA9UVzVl+/yiPr9ow5GWzIpNNQVFJbnhrLyMk65S8/4bZp9wGkS/36+f+MQnDn/7298u/s///M+qE92vra1N2trafEVFRf1eESeVetv8K6rHZnlX1RinNsjJGGPMINRwpCXk84v6Az4VEfwBX7y4hnr+6ZO7/fbbDz311FOF1dXVx805ev/99xeXlZXNGjt27PwpU6a0nnPOOemdiaYHvS2qO0XkVhEJupcv40wIYYwxZhjoaI/6fX7pNDeqzy/a0R7t9+TbhYWFsauuuqr6nnvuGdP1tnjz7+HDh9eGw2HfAw88MLK/z5dKvS2qNwPnAFVAJbAE+GyqQhljjPGWYIY/Gotqp7M/YlGVYIY/KSOH/vVf//Xgo48+Oqq5ubnbuhQKhfT9739/wyuvvDL4+1RV9RBwTYqzGGOMSYOT9XnGVW6pyV75zK6SYFYgmpHpj7a3Rv0dLRH/4kunVE2YUdjvSfaLi4ujl1xySe2jjz466tprr63uenssFuONN97IXbBgQVIn9E+2XhVVEfkVcNySKKr6maQnMsYY4zkTZhSGuZSqTa/vL2qsaQ3lFWa2Lbyo9EAyCmrc1772tQMPP/zw6MRt999/f/Hjjz9eFIlEZObMmeHbb7/d02ut9naw0XMJ32cClwP7kh/HGGOMV02YURhOZhGFzku/lZaWRlpaWo5e/9GPfrTvRz/60aCqNb1t/v1d4nUR+Q3wWkoSGWOMMYPUqcyoBDANOG6UljHGGDOc9bZPtZFjfaoKHATuSFUoY4wxZjDqbfNvnogU4hyhZsY3pyyVMcYYMwj19kj1RuDLwATgHeBs4E3gfamLZowxxgwuve1T/TJwFrBHVc8HFgJ1KUtljDHGDEK9LaqtqtoKICIhVd0M2CrWxhhj+sXv959ZVlY2a8aMGbNmzZo18/nnn89JvP3uu+8eEwqFzuhuXmAv6m1RrRSRAuBp4HkR+QPQ4wwcInKxiGwRke0i8i8nud8VIqIisqiXeYwxxgywveXrspf/7Aelv/33O6cu/9kPSveWr+v30m+hUCi2efPmjVu2bNn4rW99q+qrX/3qhMTbn3zyycI5c+Y0P/LIIwX9fa6B0NuBSpe7394lIi8B+cCfTvYzIuIH7gMuwpkveJWIPKOqG7vcLw+neXlFH7MbY4xJgj/e+70el34LN9QHjuzdk+3z+9Xn92vdoQOZe9a9UzBq4qRw9oj8ky7H9qFb7+jxIAygvr7en59/7LHKy8tD4XDY/9Of/nTPt7/97XFf/vKXj5u+0Gv6vHybqv6tl3ddDGxX1Z0AIvIYcBmwscv9vgV8F7i9r1mMMcYMjPpDB0M+v1/9gYACxL/WHzoY6qmonkxbW5uvrKxsVltbmxw5ciS4fPnyrfHbfv3rX4+8/PLLay6++OKmm266KbOioiJQWlo6JNZTPRUlQEXC9Up321EicgZQqqp/PNkDichnRWS1iKw+fPhw8pMaY4w5qY7WVr/P7++y9JtfO1pb+9XXGW/+3bVrV/nvf//7bZ/+9KenxGIxAJ566qmiT3ziEzV+v58PfvCDtcuWLfP0sm+QxoXGRcQH/Aj4VE/3VdUHgAcAFi1aZOfHGmPMAAtmZkajHR2++BEqQCwalWBmZlKWfgO48MILm2trawP79+8PVFVVBffs2RO6+OKLpwN0dHTIhAkT2r/61a96+sgqlUW1CihNuD7B3RaXB8wBXhYRgLHAMyJyqaquTmEuY4wxCXrT57m3fF326799pCSUnRXNyMqOtreE/W3hFv+5V3+sauLseUmZZH/NmjWZsViM4uLiyHe+853ir3zlK/u+853vHIjfXlJSMnfr1q0Z06dPb0/G86VCKpt/VwHTRGSKiGTgrMf6TPxGVa1X1VGqOllVJwNvAVZQjTHGgybOnhc+9+qPVWXm5nU019QEM3PzOpJRUON9qmVlZbOuueaa037+85/vDgQCPP3004Uf/ehHO82H8A//8A+1Dz/8cGH/fpPUStmRqqpGROQW4M+AH3hIVctF5G5gtao+c/JHMMYY4yUTZ88LJ+uoNC4ajb7d3fbKysr1Xbc9+OCDlcl87lRIaZ+qqi4HlnfZ9o0T3HdpKrMYY4wxqZbK5l9jjDFmWLGiaowxxiSJFVVjjDEmSayoGmOMMUliRdUYY4xJEiuqxhhj0mrZsmUFInLmmjVrMgG2bNmSkZmZeUZ8SbiFCxeWrV27NpTunL1hRdUYY0yvtO6oy655bHPpoV+snVrz2ObS1h11/V76DeCxxx4rPOOMM5p+/etfH53YobS0tC2+JNx111135Jvf/Oa4ZDxXqqVt7l9jjDHeUP2bzT0u/RZtag9EDoaz8YniE41Ut2a2bq8rCBRnh/25GSddOabo2rITToNYX1/vW7VqVe5f//rXLZdeeum0H//4x/u63qehocFfUFCQtDmGU8mKqjHGmB5Fa9tC+ETF73Mm1PeLKjGitW2hnorqyTz66KMFS5curZ83b17byJEjI6+++mr2mDFjIhUVFaGysrJZzc3NvtbWVt8bb7yxOWm/TApZ868xxpgeaXvUj086rxLmE9X2aL+Wfnv88ccLr7322lqAK664ombZsmWFcKz5t6KiYsO3v/3tis985jM9Hk17gR2pGmOM6ZFk+KMaifnwJxTWmIpk+E+5WfbgwYP+t956K2/Lli1Zt9xyC9FoVEREb7vttkOJ97v22mvrbr311smnnn7gWFE1xphh7mR9nnGtO+qyG57fUyIhf1RC/qi2Rf3aFvWPuGhSVebpBac0yf6yZctGXn755TWPPvro0ec/66yzZuzatSsj8X7PP/98XmlpadupPMdAs6JqjDGmR27hrAqvOlAUqW8LBfJDbdnvmXDgVAsqwBNPPFF4++23H0jcdtlll9V+5zvfGRfvU1VVgsGg3n///T0Wfi8QVe35Xh6yaNEiMfVDkgAAHDpJREFUXb3allw1xpi+EJG3VXVR4ra1a9funj9//pF0ZRrM1q5dO2r+/PmTu263gUrGGGNMklhRNcYYY5LEiqoxxhiTJFZUjTHGmCSxomqMMcYkiRVVY4wxJknsPFVjjDFpU1FREfjCF75QumbNmtz8/PxIMBjU22677UBhYWH02muvPb2kpKQ9FosxatSoyBNPPLGzpKTklOcZHgh2pGqMMaZXdu3alf3UU0+V/upXv5r61FNPle7atatfS7/FYjEuueSSqeedd15TZWXl+vLy8k2PP/74zoqKigyARYsWNW3evHnj1q1bNy5cuLD5Bz/4wZjk/CapY0eqxhgzzD355JM9Tlbf3NwcOHToULbP51Ofz6c1NTWZO3bsKBgzZkw4JyfnpEePV155ZbezIT377LN5wWBQ77jjjsPxbdOnT2//2te+dui5557Li2+LxWI0Njb6p06d2tqX3ysdrKgaY4zpUW1tbcjn86nf71eA+Nfa2tpQT0X1RNavX581b968E05zuHr16tyysrJZdXV1gaysrOhPfvKTylNLP3Cs+dcYY0yPOjo6/D6fr9O8tj6fTzs6Ovq19Fuij3/84xNnzJgxa86cOTPhWPPvgQMH1l133XXVt9xyy4RkPVeqWFE1xhjTo2AwGI3FYpK4LRaLSTAYPOWl3+bOnduybt26o/2yy5Yt2/vyyy9vra2tPa4V9YorrqhbsWJFXtftXmPNv8YYM8ydqM8z0a5du7JfeumlkoyMjGgoFIq2tbX529vb/eeff37VlClTTmmlmksuuaTx3/7t3+S73/3u6DvvvPMwQFNTU7cHey+99FLupEmTPL/8mxVVY4wxPXILZ9WaNWuK6uvrQ/n5+W3nnnvugVMtqAA+n49nn312xxe/+MXSe++9d2xhYWEkOzs7etddd1XCsT5VVSUvLy/60EMP7U7W75MqVlSNMcb0ypQpU8L9KaLdmTRpUsdzzz23s7vbGhsb30nmcw0E61M1xhhjksSKqjHGGJMkVlSNMcaYJLGiaowxw1es62kypmfuPot1d5sVVWOMGb42HD58ON8Ka+/FYjE5fPhwPrChu9tTOvpXRC4Gfgr4gQdV9Z4ut98G3AhEgMPAZ1S1x/OljDHG9F8kErnxwIEDDx44cGAOdpDVWzFgQyQSubG7G1NWVEXED9wHXARUAqtE5BlV3ZhwtzXAIlUNi8jnge8BV6cqkzHGmGPOPPPMQ8Cl6c4xlKTyk8liYLuq7lTVduAx4LLEO6jqS6oaP+fpLcDz8zoaY4wxJ5LKoloCVCRcr3S3ncgNwP+lMI8xxhiTUp6YUUlEPgYsAt57gts/C3wWYOLEiQOYzBhjjOm9VB6pVgGlCdcnuNs6EZELga8Bl6pqt5Mlq+oDqrpIVReNHj06JWGNMcaY/kplUV0FTBORKSKSAVwDPJN4BxFZCPwCp6AeSmEWY4wxJuVSVlRVNQLcAvwZ2AQ8rqrlInK3iMRHm30fyAWeEJF3ROSZEzycMcYY43kp7VNV1eXA8i7bvpHw/YWpfH5jjDFmINnJvsYYY0ySWFE1xhhjksSKqjHGGJMkVlSNMcaYJLGiaowxxiSJFVVjjDEmSayoGmOMMUliRdUYY4xJEiuqxhhjTJJYUTXGGGOSxIqqMcYYkyRWVI0xxpgksaJqjDHGJIkVVWOMMSZJrKgaY4wxSWJF1RhjjEkSK6rGGGNMklhRNcYYY5LEiqoxxhiTJFZUjTHGmCSxomqMMcYkiRVVY4wxJkmsqBpjjDFJYkXVGGOMSRIrqsYYY0ySWFE1xhhjksSKqjHGGJMkVlSNMcaYJLGiaowxxiSJFVVjjDEmSayoGmOMMUliRdUYY4xJEiuqxhhjTJJYUTXGGGOSxIqqMcYYkyQpLaoicrGIbBGR7SLyL93cHhKR37q3rxCRyanMY4wxxqRSyoqqiPiB+4B/AGYB14rIrC53uwGoVdWpwI+B76YqjzHGGJNqqTxSXQxsV9WdqtoOPAZc1uU+lwEPu98/CVwgIpLCTMYYY0zKBFL42CVARcL1SmDJie6jqhERqQeKgCOJdxKRzwKfda82iciWU8w0qutje4Tl6hvL1XdezWa5+qY/uSYlM4jpXiqLatKo6gPAA/19HBFZraqLkhApqSxX31iuvvNqNsvVN17NZY5JZfNvFVCacH2Cu63b+4hIAMgHqlOYyRhjjEmZVBbVVcA0EZkiIhnANcAzXe7zDPBJ9/srgRdVVVOYyRhjjEmZlDX/un2ktwB/BvzAQ6paLiJ3A6tV9Rngl8AyEdkO1OAU3lTqdxNyiliuvrFcfefVbJarb7yay7jEDgyNMcaY5LAZlYwxxpgksaJqjDHGJIkVVWOMMSZJrKgaY4wxSWJF1QwJNr2lMcYLhnVRFZFsETluH6T7Ddpy9V38/GYvZInz6v7ycK6JIlLYzfa0vk95OJcnX8fhbtgWVREZj3MO7Q0iMltEcsWlqurebrm8n2uciCwXkatEJCehuPrcr6PTlMur+8vLuZ4GbhSRD4vIDBHJBVDVmIhMtFzH5fLc62gGydy/KfIJYApwFnA9sAV4CXhNRMLAMyLyblVttVyeznUDzkINQeDbIvIK8IiqvuTe/qCIXOWulDSQvLq/vJrrOiAP5z3pWqAO2CIi7+BMIP+iiExR1RbLBXj3dRz2hu3kDyJyPVCpqn9zP21eDlwERHBWyqlT1Ussl+dz/ROwVlVfEpHpwKeBS4AmoANoUNUPpSGXV/eXV3N9AAir6qsiUgycD5yLU8zmAtWq2nXpyOGcy5OvoxneRTUHyFDV2i7bJwDbgatU9VnL5flcBUCOqlZ12V4GbAQuUdU/piGXV/eXV3MFcd6P2rtsz8dZHvJj7tSmlgvvvo5mGPepAmGgwP2nSXQAZ2L/dP1BWq4+UNU6oEFE/F1u2g68mo6C6vLk/sK7uUYA88VZfCNRGPhLOgqXy6u5vPo6DnvD8khVRP4fMB1nmblinCOap1T1bRHJBsao6m7L5flcXwQuwCmgQZx+pRdVdauIjAJKVXVNGnJ5dX95NdcvgQychTdmAutxFuB42b29wP3wZLnw7utoHMOuqIrIpcBX3AtAIU4fyUTgMVX9s+UaFLk+AtwGfA8IAQXANJw3wAdUdVuacnl1f3k114eAO3AGBHW4uf4ReBewXFV/brk65fLk62iOGY6jfxfgNAuuBnCbDf8OXAzcJiLV8dssl6dznQ08p6rPubkycUZDfhT4pYjcqKpb05DLq/vLq7lmAJsS+sQP4YziXgL8s4hsVdUXLNdRXn0djWs49qn+CniPiHxTRMapalRVj6jqI8B+YKHlGhS5fgN8TES+JCKjVLVVVTep6jeBPcAZacrl1f3l1Vz/DeSLyE9EpDS+UVVXAA04I2wt1zFefR2Na9g1/wKIyGLg88B4nD/EN4DNwKPAe1R1p+UaFLneh3NkGsQZiVmO0+/1InCequ5IUy6v7i+v5poFfBXng9BBYLmb727gfenqH/RwLk++jsYxLIsqgDuaby4wH/gwUA88oarLLdegyjXNzTUFeDeQC/xKVR9Ncy6v7i9P5gIQZyrApTh9l1txBp29ltZQeDOXl1/H4W7YFtU4ETnPPbFb1EM7w3L1jeXqGy/lEmdKSVHVqIi8V50JDSxXL3jpdTSO4dinmjgv7GnAP8OxCdnTyXL1jTtIAxGZAtwInsnl1f3lyVw4MaIiMhX4j/iGNGcCj+by8OtoGEZFVUT8IkdXb4j/AX4e2Be/3XINily+hFyxhFzhNOfy6v7yaq7uVlK5Bljh3m65Enj1dTTHG3bNv12adKYCTap6QER8qhrr6ectlydznQvsUtV9Hsvl1f3lmVyJ3FG2rap62HKdMIvnX8fhblgUVRH5MpAN/LeqHulyWxbOP8yA7wjL1edcnwAygce1y0w24kzXFrH9NShyfQ3IwRlQtq3LbVlAWzoKhIdzefJ1NN0bLkW1AWcquyk4Q89/AfyvqnaIyFU4++Fxy+X5XIeADTircGwGHsOZAKJDRD6I8+byYhpyeXV/eTVXPbAOGANUAY8DT7tHXJ8D/Kr6X5braC5Pvo7mBFR1SF+AMuB/3O+DwGeB1ThD0H8LHAaWWC7P51oA/AXnSGIa8E/A88A7wA9w1rZcbPvL87kmA993vw/grAX6Z2An8AjOzEWWy+Ovo11OfBnyR6puH0QZsF8TlkkSZ1q7B4GzVXWq5fJ8Lj/OeXm7VLU+IVMp8E3gTFWdkYZcXt1fXs0lwCScdUgbE7YHgfuAC1T1dMt19Pk9+TqaExvyc/+q0weyURKWbhIRUdVWEdmA80nUcnk/VxTnqDRxWyuwTUQO4jTVpSOXV/eXV3MpsFtERnTZ3iEi5TjNrpbr2PN78nU0Jzaki6o45y9ej9NHsktEjgBrcf5IYzhv0issl+dzFQMfAU7Dafo6AOxQd7QvsAqnKXigc3l1f3k111zg0zhN+A1uX+EbwOvuB6T4dHuWC+++jubkhnTzr4i8itMPF8U5t2uke9Pzqjrgb8KW65Rz/Q14HWcEZJ6brQpnFHB5GnN5dX95NddrwO9x+r9bcT4kjQPWquovLddxuTz5OpoepLtTN1UXnBGi5V22TQNuwvnUeQPgs1yezzUG2Nhl25nAt4C9wOX29zUoco0ENnTZVgx8AHgFZ2aggOXy9utol168dukOkLJfzGnKWYbTmV/W5bYy4GmcIfKWy9u5ioE/4AxGKu1y29lu5nQUCa/uL6/m8gE/AZ7CGVSWeNsE4AUgaLm8/TrapefLUG/+HY3zSdOPs8bmLpzz0D4IXKOqSy3XoMg1A/gCTpPvJpyp2XbirBpyvaq+L025vLq/vJorG7gT53zLw8AW4E3gIuASVT3fcnXK5cnX0ZzckC2q7gg5FWfS6fNw+kmycJZJehP4maqusVyDJtfZwMU4a0jWuxn3Ad9VZ+HodOXy6v7yVK6EfDk4zfezcM4N/QhOS8SvNb39457K5fXX0ZzYkC2qXbkj6fJV9R0RyVbVcLozgeXqK3HmOx0HbASi2mW6wnTx8P7yaq6JQIaqbheRgKpG0p0JPJ3Lk6+jOd6QXKXGPc0i/n3Q/fZaoBDAC3+QlqtXWST+NeE8vUuAsKpWp7ugurlC7lWv7a9Mr+RKlPD3dSMwB8ALhctLuU7wd++p19Gc2LA4UnX/SPcD81X1YLrzxLm5DuDkOpCmDCNwJgpvE3elCzdXFbDQg/trP7AgXfurO4Pg72teOnNJlxVU3Fz7cPbXoXTl6srjuTz3d2+6N+QmfxCR64C/aOfVHMYAX1HVg13/wQcw1504c4muV2d2IHCGzd+s6V266WtADU7fZPz5i4Gvp3l/nQlMB3Ym9JlOAv4tnftLREbiDGjpwOnj2o4zkOTONO+v03D2VwjIBd4C6oAvpzMXHJ0VKD7VZAznXONbVPWQiPgT/h8GjDjLuc0BGoDVqtrmkVwzgRnAYVV93d08Grg1ze8TppeG1JGqOMsgrQTOUdVG9w1wLs4/8iZVrY4PAEhDrm04nzSPiMhZOIMixgNvqerydPyzuE2EFThvviuBu1V1i9t87lfVjoHMk5ArC/grzqfzqcBXcJZ8i5+zuiKN++shoABnlGgZ8DLwmqo+O5BZuuTKwhlUEwPWc2yigN04y5jtS9PffQbO6NU/q+rbCdvjg3CC6fgbc1/HJ3E+fDThfKj8srpz66bxg1Em8Eec/8cZOPuuEWd+681uf+qAv46mb4Zan+pVwCtuQZ0L/Bj4L+BzwF0ikpWmP8gPA393C+qZwPeB2Tj/zFeIyLg0ffr8KPCYqk7DaSa8Q0ROV9VYugqq62PAHlW9ErgH+Hfg8zhvNDeISEGa9tfHcT6IfhDnDe8tnEFT14vI7WnIE/cJoF1VLwb+H/BLnPMY84A7RSQnTX/3n8E5VeUnIvKaiHxVRCYlZPlSQt/vQLoeaFDnlJSbcD6EXJZw+9UJfawD6RqcifOvAO7G+bu/DVgI3CIiI6yget9QK6pfwTmyAeccxi2qOgf4BlACvCdNud4C9otIAXAOzvR6XwLuB5qBm9OU61M4J7cD3Au0AU+JyEfTlCduNBDvA7wUWKmqHwZ+iNPk+sk05SrC6XNDVQ/jNP1uAv4VWCQiJWnKtRWoFZEiVa1T1S2q+kecSQ3GA5enKdd84KvA+TgfJKcBz4rI70TkIeBqdebWHWgfwp0rWlX346xP+lkRyRSRC4BPpulD5ceAeIvHB3Gapa/A2Xch4Io0ZDJ9NNT6VG8B/llEaoF8nH9qVHWXiLTjHFWkQyVQjdO0UwNsdofrt4pIEV1WXxkIbtPcb1T1aQBV3QN8QUSuwDnyKlbV/0xTc9OjwA9EZA3OVIR73YyH3YGRTQOcJ+4xN9c3cT6knY7TV7/LPeI6z73PQFuB84b8mIi8DPwNp1vhgDs6OTsNmQC+A2S7o2j/APzB7ctcCDyB0/owoNxBPz/DWR8VAFV9Q0TWu3kWkKYVj4Dbgfg5sT/Fed/AbeFKUyTTV0OqTzWRiLwXZ5WJiHuE+AbONGQtacy0BHg/TjNiFc4/dgZwXTpzdSUilwP/Arw3TUcS8X7CCTiTif8bzlFhIbAUOC+Np63MwJkXNgK87fbvjsM5IX9WOk93EJELcVpCJuFMFtCGs/+u8tJpGCISwJm1aG46crljBjJVNZzQv1uIM1VhGTA5jX/3Rz/EJmQbg/NB6QwvvU+Y7g2Zouo2vX0MZ3DGCmCfqra7/0AXAxeq6m1pynU9sANn2aa4LJyjndc1YVHkAcw1Hufct11urk7nfUqaTnx399fVOEV0A85AqgU4/dIRnKbgP6cp10dxJp3YidP31eQe+ZwNfFhVvzbQubpkFJzm3kJ300ScQVT16Uvl6FIsJgHvV9X/TnOs+D7zux++vwpMU9VPeyCXX1Wj7geQC4HzVfXOdOcyPRsSRdUdTfsTnKPRRTijILcAT6nqX91BBxmq2pzGXGfhnIZRDvxBVV8ayCzd5PopznJqi3COZrYAv1fVv6Y5109wjvriuTYDjyacXpDOXG+5udpxiv5Tmt6l1Cbi9DmvwxkVfaSHHxkQXXJtUNWahNvSNno1Idd6nFPbarq5z4DPVtRlf5WranXCbfHimpaR0qbvhkpR/SFQq6r/4V6fDFyJMxBnpap+xiO5piTkWg3ckKajwZPtr1XAjZqec/S6y3UVzgLSbwKfV9V2D+SagjNo5FM4++tzacr1a5xxA8/hHMXvxDl1bKXbV3+tqv7MI7nKVXW128z6MVW912O5inCaye/3WK6ROJPn/3ygc5lTM1RG/1YBc0RkmvuJbreq/sAd+RsQkXd7JNcuVf2+qs4GBFjikVyJ+8uP05zplVzfV9VZQBDnaN8LuXZ12V/pypUD/AhnBHcTzhvz9SLyeeC3OAOnvJLr426ux4FzPZorLasd9ZDrSZzR02aQGCpHqkHgLpzzzV7AaZqLqOp+EdkCfFxVV1ouyzVUcrljBSYC9Xps0oLJOM3Tk3EGd71XVQd0ZLnlGhq5zKkb1EW1y+CHbJwTuT+Ac/pKDc40ZAdU9XrLZbmGYq4T3H4e8KSqFg9gLMvVR17NZfpnsBfVeFPle3Fmj/mNqq4TZzal0TjNdoe7G5BguSzXEMi1FOfc66dU9cWE20uAGYnbLJflMgNjsPepfhL4LlCLM1L0aRHZCVyEMxvJloF+w7NclmsAc1W7l1+ISIWIfFOcWZWq0vRGbLmGRi7TH6o6aC84q75c3mXbmcDDwJcsl+UaRrnOwJns/xbLZbnskr7LoD1SFREBXsSdijBOndUw7gCuEmfyestluYZDrr/jzEH8UctluUz6DNqiqs7HugeA2SLyoojc5PZRgDPXaTHO7DeWy3JZLstlucyAGLQDlURkIc6KNHXAWJwT8WcBrwEtwEFVHfDluCyX5bJclmuw5jL9NyhXqRGRM4Dv4cw+0gJsVdULRGQ0zjyx5TgLXFsuy2W5LJflMgNmsDb/fgZYrs6izJ8DTheRq9RZ4/It4CJNzyG45bJclstyDdZcJgkGa1E9A2eSelT1EPC/wA3ubV9yb7dclstyWS7LZQbUoCuqbmf+v+CceA+Aqv4eaBaRm4ELgP+xXJbLclkuy2UG2mAeqBRfEsmnqjERmQb8H84cmmkbim65LJflslyDNZfpv0E5UAlA3aXJ3D9Iv6puE5HHgIOWy3JZLstluUw6DNoj1e6Is+IDqhpLd5ZElqtvLFffWK6+sVwmlYZUUTXGGGPSadANVDLGGGO8yoqqMcYYkyRWVI0niUiRiLzjXg6ISFXC9Yw+PM5nRGRsEvKcISIX9+H+he4pEn19Hp+I/Esv73ujiPykh/u8T0TO7msOY8ypsaJqPElVq1V1gaouAO4Hfhy/rqrtfXioz+DMrdpfZwC9LqpAIdCnoioigjMiv1dFtZfeh7MQtjFmAFhRNYOOiHxSRFa6R63/5R7dBURkmYisF5ENInKriFyNM5fqb7s7whWR/09ENorIOhF5xN2WKyL/4z7+GhG5RESygG8A17uPc2WXx5krIqvc29aJyGnAPcAMd9s9IjJCnNVI/u7e58Puz051M/wvzpyvvwDy3J/7dTe/+40islVEVpJQLEXkMhFZ4Wb+i4iMEZHTgRuB293HO0dEikXkKRFZ7f6OVnCNSSb1wKKudrHLyS7AXcA/u9/PAZ4GAu71B4DrgCXA/yX8TIH79TVgwQkedz+Q0eX+3wOucb8fCWwFMnGK009O8Dg/B652vw+5958KvJNwnyAwwv1+DLDN/X4qEAMWudcDQN0JnmcCsAcoAjJw5on9SULW+Gj+m4Hvut//B/BPCY/xW+Bs9/vJwIZ0v752sctQugzayR/MsHUhcBaw2mktJQuoAP6Mc2R4L/BH4C+9eKxy4BER+QNOoQZ4P/APCf2amcDEHh7nDeDrIjIJeEpVt7vZEglwj4i8G6eIlorIKPe2Haq6uhd5zwZeUNVqABF5PCHbROBxt/84hPNhoDsX4uyn+PWRIpKlqi29eH5jTA+s+dcMNgI8pMf6V2eo6rfcQjMPeBX4Ik4zak8+gNNfexawUpx5WQX4SMLjT1TVExUoAFR1GXA50Ab8SUTe083dPgHkA2eo0098BKdgAzT3ImtP7sPpd54LfCHhsbsSYHHC71diBdWY5LGiagabvwIfjR/luaOEJ4qzFqWo6hM4/Z/xlT4agbyuD+IW0Amq+iJwBzAKyMY54v1Swv0Wnuxx3PucpqrbVfWnwHM4xb3r/fOBQ6oaEZGLgJLuHktVI+5jdteK9BbwPndkcQaQ2LebD1S5g50+mbC9a46/4nzoiGdf0F0OY8ypsaJqBhVVXQ98E/iriKzDaeYtBkqBV0TkHeBXwFfdH/kV8GA3A5UCwKPuY/wd+IGqNrqPneMOeCrH6c8FeBGY7w4E6jRQCbhORMrd554OPKKqB4G33ce5B1gGnCMi64FrgG0n+TV/CazrOlBJVStx+kjfwjki35hw813A74FVdJ4/9g84H0LWiMg5OAX1XHew1EbgppPkMMb0kU1TaIwxxiSJHakaY4wxSWJF1RhjjEkSK6rGGGNMklhRNcYYY5LEiqoxxhiTJFZUjTHGmCSxomqMMcYkiRVVY4wxJkn+fwofJtg3QSXIAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "importlib.reload(pipeline)\n",
    "\n",
    "best_models_per_metric = {}\n",
    "\n",
    "for metric in metrics_to_display:\n",
    "    #For each model, find the set of parameters that work the best in each train/test set\n",
    "    best_models = pipeline.get_best_models_of_each_type_for_each_train_test_set(models_to_run,results,'test_set_start_date', metric)\n",
    "    pipeline.plot_models_in_time(models_to_run, best_models, metric)\n",
    "    best_models_per_metric[metric]=best_models\n",
    "   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}