modelling.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Modelling"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Import libraries and modules"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from utils.classification_report import plot_feature_importance, plot_confusion_matrix, plot_ROC_curve\n",
    "\n",
    "from tqdm import tqdm\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.compose import ColumnTransformer\n",
    "from sklearn.impute import SimpleImputer\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.preprocessing import OrdinalEncoder\n",
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "from sklearn.metrics import classification_report, confusion_matrix, ConfusionMatrixDisplay\n",
    "\n",
    "from sklearn.metrics import classification_report"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load pre-processed data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# load data\n",
    "df = pd.read_pickle('data/output/entities_processed_and_cleaned.pkl')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## RandomForest\n",
    "\n",
    "As a first approach, we will use a Random Forest model, a powerful ensemble learning method that operates by constructing multiple decision trees during training and outputting the class that is the mode of the classes or mean prediction of the individual trees.\n",
    "\n",
    "For preprocessing, categorical variables will be handled using OrdinalEncoder, which converts each string value to a whole number.\n",
    "For numerical variables, we will standardize them to have a mean of 0 and a standard deviation of 1.\n",
    "This preprocessing will ensure that our data is in the right format and ready for modeling.\n",
    "\n",
    "Later on, we'll use a tokenizer for text data, which breaks down text into individual words or terms, making it understandable for machine learning models. Following tokenization, we'll apply an embedding. This transforms the tokenized words into vectors of real numbers, capturing semantic relationships between words. This numerical representation allows machine learning models to perform computations on the text data, enhancing their ability to understand and generate predictions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# define X and y for the model, encode y as binary\n",
    "X = df.drop(columns=['surname_household'])\n",
    "y = df['surname_household'].apply(lambda x: 0 if pd.isna(x) else 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((20059, 11), (5015, 11), (20059,), (5015,))"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# split the data into training and testing sets\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
    "\n",
    "# check the shape of the training and testing sets\n",
    "X_train.shape, X_test.shape, y_train.shape, y_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(surname_household\n",
       " 0    0.773269\n",
       " 1    0.226731\n",
       " Name: proportion, dtype: float64,\n",
       " surname_household\n",
       " 0    0.783051\n",
       " 1    0.216949\n",
       " Name: proportion, dtype: float64)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check the distribution of the target variable in the training and testing sets\n",
    "y_train.value_counts(normalize=True), y_test.value_counts(normalize=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00      3927\n",
      "           1       0.98      0.99      0.99      1088\n",
      "\n",
      "    accuracy                           0.99      5015\n",
      "   macro avg       0.99      0.99      0.99      5015\n",
      "weighted avg       0.99      0.99      0.99      5015\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# define a pipeline for the Random Forest model\n",
    "\n",
    "# define the preprocessing steps for the model\n",
    "numeric_features = X_train.select_dtypes(include=['int64', 'float64']).columns\n",
    "categorical_features = X_train.select_dtypes(include=['object']).columns\n",
    "\n",
    "numeric_transformer = Pipeline(steps=[\n",
    "    ('imputer', SimpleImputer(strategy='mean')) # mean imputation for missing values\n",
    "])\n",
    "\n",
    "categorical_transformer = Pipeline(steps=[\n",
    "    ('imputer', SimpleImputer(strategy='constant', fill_value='missing')), # constant imputation for missing values\n",
    "    # ordinal encoding for categorical variables\n",
    "    ('ordinal', OrdinalEncoder(handle_unknown='use_encoded_value',\n",
    "                               unknown_value=-1))\n",
    "])\n",
    "\n",
    "preprocessor = ColumnTransformer(\n",
    "    transformers=[\n",
    "        ('num', numeric_transformer, numeric_features),\n",
    "        ('cat', categorical_transformer, categorical_features)\n",
    "    ]\n",
    ")\n",
    "\n",
    "# define the model\n",
    "model = Pipeline(steps=[\n",
    "    ('preprocessor', preprocessor),\n",
    "    ('classifier', RandomForestClassifier())\n",
    "])\n",
    "\n",
    "# fit the model on the training data\n",
    "model.fit(X_train, y_train)\n",
    "\n",
    "# make predictions on the testing data\n",
    "y_pred = model.predict(X_test)\n",
    "\n",
    "# evaluate the model\n",
    "print(classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model summary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-1 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-1 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-1 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-1 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>Pipeline(steps=[(&#x27;preprocessor&#x27;,\n",
       "                 ColumnTransformer(transformers=[(&#x27;num&#x27;,\n",
       "                                                  Pipeline(steps=[(&#x27;imputer&#x27;,\n",
       "                                                                   SimpleImputer())]),\n",
       "                                                  Index([&#x27;age&#x27;, &#x27;birth_date&#x27;], dtype=&#x27;object&#x27;)),\n",
       "                                                 (&#x27;cat&#x27;,\n",
       "                                                  Pipeline(steps=[(&#x27;imputer&#x27;,\n",
       "                                                                   SimpleImputer(fill_value=&#x27;missing&#x27;,\n",
       "                                                                                 strategy=&#x27;constant&#x27;)),\n",
       "                                                                  (&#x27;ordinal&#x27;,\n",
       "                                                                   OrdinalEncoder(handle_unknown=&#x27;use_encoded_value&#x27;,\n",
       "                                                                                  unknown_value=-1))]),\n",
       "                                                  Index([&#x27;surname&#x27;, &#x27;firstname&#x27;, &#x27;occupation&#x27;, &#x27;civil_status&#x27;, &#x27;nationality&#x27;,\n",
       "       &#x27;link&#x27;, &#x27;lob&#x27;, &#x27;employer&#x27;, &#x27;observation&#x27;],\n",
       "      dtype=&#x27;object&#x27;))])),\n",
       "                (&#x27;classifier&#x27;, RandomForestClassifier())])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" ><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;Pipeline<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.pipeline.Pipeline.html\">?<span>Documentation for Pipeline</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>Pipeline(steps=[(&#x27;preprocessor&#x27;,\n",
       "                 ColumnTransformer(transformers=[(&#x27;num&#x27;,\n",
       "                                                  Pipeline(steps=[(&#x27;imputer&#x27;,\n",
       "                                                                   SimpleImputer())]),\n",
       "                                                  Index([&#x27;age&#x27;, &#x27;birth_date&#x27;], dtype=&#x27;object&#x27;)),\n",
       "                                                 (&#x27;cat&#x27;,\n",
       "                                                  Pipeline(steps=[(&#x27;imputer&#x27;,\n",
       "                                                                   SimpleImputer(fill_value=&#x27;missing&#x27;,\n",
       "                                                                                 strategy=&#x27;constant&#x27;)),\n",
       "                                                                  (&#x27;ordinal&#x27;,\n",
       "                                                                   OrdinalEncoder(handle_unknown=&#x27;use_encoded_value&#x27;,\n",
       "                                                                                  unknown_value=-1))]),\n",
       "                                                  Index([&#x27;surname&#x27;, &#x27;firstname&#x27;, &#x27;occupation&#x27;, &#x27;civil_status&#x27;, &#x27;nationality&#x27;,\n",
       "       &#x27;link&#x27;, &#x27;lob&#x27;, &#x27;employer&#x27;, &#x27;observation&#x27;],\n",
       "      dtype=&#x27;object&#x27;))])),\n",
       "                (&#x27;classifier&#x27;, RandomForestClassifier())])</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;preprocessor: ColumnTransformer<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.compose.ColumnTransformer.html\">?<span>Documentation for preprocessor: ColumnTransformer</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>ColumnTransformer(transformers=[(&#x27;num&#x27;,\n",
       "                                 Pipeline(steps=[(&#x27;imputer&#x27;, SimpleImputer())]),\n",
       "                                 Index([&#x27;age&#x27;, &#x27;birth_date&#x27;], dtype=&#x27;object&#x27;)),\n",
       "                                (&#x27;cat&#x27;,\n",
       "                                 Pipeline(steps=[(&#x27;imputer&#x27;,\n",
       "                                                  SimpleImputer(fill_value=&#x27;missing&#x27;,\n",
       "                                                                strategy=&#x27;constant&#x27;)),\n",
       "                                                 (&#x27;ordinal&#x27;,\n",
       "                                                  OrdinalEncoder(handle_unknown=&#x27;use_encoded_value&#x27;,\n",
       "                                                                 unknown_value=-1))]),\n",
       "                                 Index([&#x27;surname&#x27;, &#x27;firstname&#x27;, &#x27;occupation&#x27;, &#x27;civil_status&#x27;, &#x27;nationality&#x27;,\n",
       "       &#x27;link&#x27;, &#x27;lob&#x27;, &#x27;employer&#x27;, &#x27;observation&#x27;],\n",
       "      dtype=&#x27;object&#x27;))])</pre></div> </div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">num</label><div class=\"sk-toggleable__content fitted\"><pre>Index([&#x27;age&#x27;, &#x27;birth_date&#x27;], dtype=&#x27;object&#x27;)</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;SimpleImputer<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.impute.SimpleImputer.html\">?<span>Documentation for SimpleImputer</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>SimpleImputer()</pre></div> </div></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" ><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">cat</label><div class=\"sk-toggleable__content fitted\"><pre>Index([&#x27;surname&#x27;, &#x27;firstname&#x27;, &#x27;occupation&#x27;, &#x27;civil_status&#x27;, &#x27;nationality&#x27;,\n",
       "       &#x27;link&#x27;, &#x27;lob&#x27;, &#x27;employer&#x27;, &#x27;observation&#x27;],\n",
       "      dtype=&#x27;object&#x27;)</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" ><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;SimpleImputer<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.impute.SimpleImputer.html\">?<span>Documentation for SimpleImputer</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>SimpleImputer(fill_value=&#x27;missing&#x27;, strategy=&#x27;constant&#x27;)</pre></div> </div></div><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-7\" type=\"checkbox\" ><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;OrdinalEncoder<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.preprocessing.OrdinalEncoder.html\">?<span>Documentation for OrdinalEncoder</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>OrdinalEncoder(handle_unknown=&#x27;use_encoded_value&#x27;, unknown_value=-1)</pre></div> </div></div></div></div></div></div></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-8\" type=\"checkbox\" ><label for=\"sk-estimator-id-8\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;RandomForestClassifier<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.ensemble.RandomForestClassifier.html\">?<span>Documentation for RandomForestClassifier</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>RandomForestClassifier()</pre></div> </div></div></div></div></div></div>"
      ],
      "text/plain": [
       "Pipeline(steps=[('preprocessor',\n",
       "                 ColumnTransformer(transformers=[('num',\n",
       "                                                  Pipeline(steps=[('imputer',\n",
       "                                                                   SimpleImputer())]),\n",
       "                                                  Index(['age', 'birth_date'], dtype='object')),\n",
       "                                                 ('cat',\n",
       "                                                  Pipeline(steps=[('imputer',\n",
       "                                                                   SimpleImputer(fill_value='missing',\n",
       "                                                                                 strategy='constant')),\n",
       "                                                                  ('ordinal',\n",
       "                                                                   OrdinalEncoder(handle_unknown='use_encoded_value',\n",
       "                                                                                  unknown_value=-1))]),\n",
       "                                                  Index(['surname', 'firstname', 'occupation', 'civil_status', 'nationality',\n",
       "       'link', 'lob', 'employer', 'observation'],\n",
       "      dtype='object'))])),\n",
       "                ('classifier', RandomForestClassifier())])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Confusion matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_confusion_matrix(model, y_pred=y_pred, y_test=y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ROC curve\n",
    "plot_ROC_curve(model, X_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Wow this model seems to perform very well. Let's look at the feature importance (and see that we are cheating in some way :) )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Feature importance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_feature_importance(model, X_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Removing the `surname` column\n",
    "\n",
    "The reason for removing the `surname` column is that it contains information that could potentially lead to \"cheating\" in the prediction process. Since people who are not part of a household have their name written in this column, using it as a feature could make the prediction task too easy and not reflect the true nature of the problem you are trying to solve.\n",
    "\n",
    "By removing the `surname` column, we ensure that the prediction model focuses on other relevant features and avoids any bias or unfair advantage that could arise from using the latter column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# now we make the same process but without the 'surname' column\n",
    "X = df.drop(columns=['surname', 'surname_household'])\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.96      0.94      0.95      3927\n",
      "           1       0.81      0.87      0.84      1088\n",
      "\n",
      "    accuracy                           0.93      5015\n",
      "   macro avg       0.89      0.91      0.90      5015\n",
      "weighted avg       0.93      0.93      0.93      5015\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# define the preprocessing steps for the model\n",
    "numeric_features = X_train.select_dtypes(include=['int64', 'float64']).columns\n",
    "categorical_features = X_train.select_dtypes(include=['object']).columns\n",
    "\n",
    "numeric_transformer = Pipeline(steps=[\n",
    "    ('imputer', SimpleImputer(strategy='mean'))\n",
    "])\n",
    "\n",
    "categorical_transformer = Pipeline(steps=[\n",
    "    ('imputer', SimpleImputer(strategy='constant', fill_value='missing')),\n",
    "    ('ordinal', OrdinalEncoder(handle_unknown='use_encoded_value',\n",
    "                               unknown_value=-1))\n",
    "])\n",
    "\n",
    "preprocessor = ColumnTransformer(\n",
    "    transformers=[\n",
    "        ('num', numeric_transformer, numeric_features),\n",
    "        ('cat', categorical_transformer, categorical_features)\n",
    "    ]\n",
    ")\n",
    "\n",
    "# define the model\n",
    "model = Pipeline(steps=[\n",
    "    ('preprocessor', preprocessor),\n",
    "    ('classifier', RandomForestClassifier())\n",
    "])\n",
    "\n",
    "# fit the model on the training data\n",
    "model.fit(X_train, y_train)\n",
    "\n",
    "# make predictions on the testing data\n",
    "y_pred = model.predict(X_test)\n",
    "\n",
    "print(classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot confusion matrix\n",
    "plot_confusion_matrix(model, y_pred=y_pred, y_test=y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ROC curve\n",
    "plot_ROC_curve(model, X_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As expected, removing the 'surname' column from the model has a negative impact on the model performance.\n",
    "However, has discussed earlier, it is more realistic to build a model without the 'surname' column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'memory': None,\n",
       " 'steps': [('preprocessor',\n",
       "   ColumnTransformer(transformers=[('num',\n",
       "                                    Pipeline(steps=[('imputer', SimpleImputer())]),\n",
       "                                    Index(['age', 'birth_date'], dtype='object')),\n",
       "                                   ('cat',\n",
       "                                    Pipeline(steps=[('imputer',\n",
       "                                                     SimpleImputer(fill_value='missing',\n",
       "                                                                   strategy='constant')),\n",
       "                                                    ('ordinal',\n",
       "                                                     OrdinalEncoder(handle_unknown='use_encoded_value',\n",
       "                                                                    unknown_value=-1))]),\n",
       "                                    Index(['firstname', 'occupation', 'civil_status', 'nationality', 'link', 'lob',\n",
       "          'employer', 'observation'],\n",
       "         dtype='object'))])),\n",
       "  ('classifier', RandomForestClassifier())],\n",
       " 'verbose': False,\n",
       " 'preprocessor': ColumnTransformer(transformers=[('num',\n",
       "                                  Pipeline(steps=[('imputer', SimpleImputer())]),\n",
       "                                  Index(['age', 'birth_date'], dtype='object')),\n",
       "                                 ('cat',\n",
       "                                  Pipeline(steps=[('imputer',\n",
       "                                                   SimpleImputer(fill_value='missing',\n",
       "                                                                 strategy='constant')),\n",
       "                                                  ('ordinal',\n",
       "                                                   OrdinalEncoder(handle_unknown='use_encoded_value',\n",
       "                                                                  unknown_value=-1))]),\n",
       "                                  Index(['firstname', 'occupation', 'civil_status', 'nationality', 'link', 'lob',\n",
       "        'employer', 'observation'],\n",
       "       dtype='object'))]),\n",
       " 'classifier': RandomForestClassifier(),\n",
       " 'preprocessor__n_jobs': None,\n",
       " 'preprocessor__remainder': 'drop',\n",
       " 'preprocessor__sparse_threshold': 0.3,\n",
       " 'preprocessor__transformer_weights': None,\n",
       " 'preprocessor__transformers': [('num',\n",
       "   Pipeline(steps=[('imputer', SimpleImputer())]),\n",
       "   Index(['age', 'birth_date'], dtype='object')),\n",
       "  ('cat',\n",
       "   Pipeline(steps=[('imputer',\n",
       "                    SimpleImputer(fill_value='missing', strategy='constant')),\n",
       "                   ('ordinal',\n",
       "                    OrdinalEncoder(handle_unknown='use_encoded_value',\n",
       "                                   unknown_value=-1))]),\n",
       "   Index(['firstname', 'occupation', 'civil_status', 'nationality', 'link', 'lob',\n",
       "          'employer', 'observation'],\n",
       "         dtype='object'))],\n",
       " 'preprocessor__verbose': False,\n",
       " 'preprocessor__verbose_feature_names_out': True,\n",
       " 'preprocessor__num': Pipeline(steps=[('imputer', SimpleImputer())]),\n",
       " 'preprocessor__cat': Pipeline(steps=[('imputer',\n",
       "                  SimpleImputer(fill_value='missing', strategy='constant')),\n",
       "                 ('ordinal',\n",
       "                  OrdinalEncoder(handle_unknown='use_encoded_value',\n",
       "                                 unknown_value=-1))]),\n",
       " 'preprocessor__num__memory': None,\n",
       " 'preprocessor__num__steps': [('imputer', SimpleImputer())],\n",
       " 'preprocessor__num__verbose': False,\n",
       " 'preprocessor__num__imputer': SimpleImputer(),\n",
       " 'preprocessor__num__imputer__add_indicator': False,\n",
       " 'preprocessor__num__imputer__copy': True,\n",
       " 'preprocessor__num__imputer__fill_value': None,\n",
       " 'preprocessor__num__imputer__keep_empty_features': False,\n",
       " 'preprocessor__num__imputer__missing_values': nan,\n",
       " 'preprocessor__num__imputer__strategy': 'mean',\n",
       " 'preprocessor__cat__memory': None,\n",
       " 'preprocessor__cat__steps': [('imputer',\n",
       "   SimpleImputer(fill_value='missing', strategy='constant')),\n",
       "  ('ordinal',\n",
       "   OrdinalEncoder(handle_unknown='use_encoded_value', unknown_value=-1))],\n",
       " 'preprocessor__cat__verbose': False,\n",
       " 'preprocessor__cat__imputer': SimpleImputer(fill_value='missing', strategy='constant'),\n",
       " 'preprocessor__cat__ordinal': OrdinalEncoder(handle_unknown='use_encoded_value', unknown_value=-1),\n",
       " 'preprocessor__cat__imputer__add_indicator': False,\n",
       " 'preprocessor__cat__imputer__copy': True,\n",
       " 'preprocessor__cat__imputer__fill_value': 'missing',\n",
       " 'preprocessor__cat__imputer__keep_empty_features': False,\n",
       " 'preprocessor__cat__imputer__missing_values': nan,\n",
       " 'preprocessor__cat__imputer__strategy': 'constant',\n",
       " 'preprocessor__cat__ordinal__categories': 'auto',\n",
       " 'preprocessor__cat__ordinal__dtype': numpy.float64,\n",
       " 'preprocessor__cat__ordinal__encoded_missing_value': nan,\n",
       " 'preprocessor__cat__ordinal__handle_unknown': 'use_encoded_value',\n",
       " 'preprocessor__cat__ordinal__max_categories': None,\n",
       " 'preprocessor__cat__ordinal__min_frequency': None,\n",
       " 'preprocessor__cat__ordinal__unknown_value': -1,\n",
       " 'classifier__bootstrap': True,\n",
       " 'classifier__ccp_alpha': 0.0,\n",
       " 'classifier__class_weight': None,\n",
       " 'classifier__criterion': 'gini',\n",
       " 'classifier__max_depth': None,\n",
       " 'classifier__max_features': 'sqrt',\n",
       " 'classifier__max_leaf_nodes': None,\n",
       " 'classifier__max_samples': None,\n",
       " 'classifier__min_impurity_decrease': 0.0,\n",
       " 'classifier__min_samples_leaf': 1,\n",
       " 'classifier__min_samples_split': 2,\n",
       " 'classifier__min_weight_fraction_leaf': 0.0,\n",
       " 'classifier__monotonic_cst': None,\n",
       " 'classifier__n_estimators': 100,\n",
       " 'classifier__n_jobs': None,\n",
       " 'classifier__oob_score': False,\n",
       " 'classifier__random_state': None,\n",
       " 'classifier__verbose': 0,\n",
       " 'classifier__warm_start': False}"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.get_params()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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bF154wfGYjSVLlpj27dubiIgIx2fdd999JjU19Ypzpjwe4/Hhhx869btU9z9/F5ceV/LHH3/kGNPK+fLDDz+YDh06GH9/f1OmTBlzyy23mA0bNlj67EumTp1qKlasaNzc3Jzm1urvMK/5T0hIyPGIlhMnTpjhw4ebihUrGi8vL1OpUiXTv39/c/z4cUcfq+cYAJQ0mzEl8I11AMB1z2azadiwYTkudwVyw/kCAKUT34EEAAAAAFhCgAQAAAAAWEKABAAAAABYwncgAQAAAACWsAIJAAAAALCEAAkAAAAAsMTD1QWg+Nntdv3+++8KCAiQzWZzdTkAAAAAXMQYo9OnTysyMlJubvlfTyRAXgd+//13RUVFuboMAAAAAKXE4cOHValSpXzvR4C8DgQEBEj66yQJDAx0cTUAAAAAXCUjI0NRUVGOjJBfBMjrwKXLVgMDAwmQAAAAAAr81TZuogMAAAAAsIQVSLhE/OgFri4BAAAAKBHbn+/n6hKKDCuQAAAAAABLCJAAAAAAAEsIkAAAAAAASwiQAAAAAABLCJAAAAAAAEsIkAAAAAAASwiQAAAAAABLCJAAAAAAAEsIkAAAAAAASwiQxax169YaOXKkJCkmJkYzZ860vO+aNWtks9mUlpZWLLUBAAAAQH54uLqA68nWrVvl5+fn6jIAAAAAoEAIkCUoPDzc1SUAAAAAQIFxCWsJ+uclrDabTW+99Za6d++uMmXKKDY2Vp9//nme+589e1adOnVSixYtuKwVAAAAQIkjQLrY5MmT1aNHD/3444+67bbb1KdPH508eTJHv7S0NN16662y2+1asWKFgoOD8xwzKytLGRkZTi8AAAAAKCwCpIsNGDBAvXr1UrVq1fTMM88oMzNTW7Zscepz5MgRJSQkqEKFClq6dKnKlClz2TGnTZumoKAgxysqKqo4DwEAAADAdYIA6WL16tVz/Ozn56fAwEAdO3bMqc+tt96qatWq6YMPPpCXl9cVxxw3bpzS09Mdr8OHDxd53QAAAACuPwRIF/P09HR6b7PZZLfbndr+9a9/6bvvvtO+ffssjent7a3AwECnFwAAAAAUFndhvQpMnz5d/v7+atu2rdasWaNatWq5uiQAAAAA1yEC5FXihRdeUHZ2ttq0aaM1a9aoZs2ari4JAAAAwHWGAHkVeemll5xCZPXq1V1dEgAAAIDriM0YY1xdBIpXRkaGgoKClJ6eXmq+Dxk/eoGrSwAAAABKxPbn+7m6BIfCZgNuogMAAAAAsIQACQAAAACwhAAJAAAAALCEAAkAAAAAsIQACQAAAACwhAAJAAAAALCEAAkAAAAAsIQACQAAAACwxMPVBeD6VJoepgoAAADAGlYgAQAAAACWECABAAAAAJYQIAEAAAAAlhAgAQAAAACWECABAAAAAJYQIAEAAAAAlhAgAQAAAACW8BxIuETKlLquLuGaVnnCbleXAAAAgGsQK5AAAAAAAEsIkAAAAAAASwiQAAAAAABLCJAAAAAAAEsIkAAAAAAASwiQAAAAAABLCJAAAAAAAEsIkAAAAAAASwiQAAAAAABLCJAAAAAAAEuumgB56NAh2Ww27dy50/I+NptNn376aYH3BwAAAAD8n6smQEZFRSk1NVV16tSxvE9qaqo6depUpHUUJoj+PdACAAAAwNXGw9UFWOXu7q7y5cvna5/89gcAAAAA5K3UrUDa7XY999xzqlatmry9vVW5cmU9/fTTTit/drtdlSpV0muvvea0744dO+Tm5qZff/1VUsFX/E6dOqU+ffooPDxcvr6+io2N1dy5cyVJVapUkSQ1bNhQNptNrVu3liRt3bpVt956q8LCwhQUFKSEhAT98MMPjjFjYmIkSd27d5fNZnO8HzBggLp16+b0+SNHjnSMK0lLlixR3bp15evrq9DQULVr105nzpzJs/6srCxlZGQ4vQAAAACgsEpdgBw3bpymT5+u8ePHa9++fXrvvfdUrlw5pz5ubm7q1auX3nvvPaf2hQsXqkWLFoqOji5UDZc+++uvv1ZiYqJee+01hYWFSZK2bNkiSVq5cqVSU1P18ccfS5JOnz6t/v37a926ddq0aZNiY2N122236fTp05L+CpiSNHfuXKWmpjreX0lqaqp69eqlQYMGKTExUWvWrNEdd9whY0ye+0ybNk1BQUGOV1RUVIHnAgAAAAAuKVWXsJ4+fVqzZs3Sq6++qv79+0uSbrjhBrVs2VKHDh1y6tunTx/NmDFDKSkpqly5sux2uxYtWqQnn3yy0HWkpKSoYcOGaty4saT/Wz2UpPDwcElSaGio0yWybdq0cRrjzTffVHBwsNauXavOnTs79gsODs7XpbWpqam6ePGi7rjjDkcwrlu37mX3GTdunEaNGuV4n5GRQYgEAAAAUGilagUyMTFRWVlZatu27RX7NmjQQHFxcY5VyLVr1+rYsWO6++67C13HAw88oEWLFqlBgwYaM2aMNmzYcMV9jh49qiFDhig2NlZBQUEKDAxUZmamUlJSClVL/fr11bZtW9WtW1d333235syZo1OnTl12H29vbwUGBjq9AAAAAKCwSlWA9PX1zVf/Pn36OALke++9p44dOyo0NLTQdXTq1Em//vqrHnnkEf3+++9q27atHnvsscvu079/f+3cuVOzZs3Shg0btHPnToWGhur8+fOX3c/NzS3H5agXLlxw/Ozu7q4VK1bo66+/Vq1atfTKK6+oRo0aOnjwYMEPEAAAAAAKoFQFyNjYWPn6+mrVqlWW+vfu3Vt79uzR9u3btWTJEvXp06fIagkPD1f//v317rvvaubMmXrzzTclSV5eXpKk7Oxsp/7r16/XiBEjdNttt6l27dry9vbW8ePHnfp4enrm2C88PFypqalObf98RIjNZlOLFi00efJk7dixQ15eXvrkk0+K4jABAAAAwLJS9R1IHx8fjR07VmPGjJGXl5datGihP/74Q3v37s31staYmBg1b95cgwcPVnZ2tm6//fYiqWPChAmKj49X7dq1lZWVpS+++EJxcXGSpIiICPn6+mrZsmWqVKmSfHx8FBQUpNjYWL3zzjtq3LixMjIyNHr06BwrqjExMVq1apVatGghb29vhYSEqE2bNnr++ee1YMECNWvWTO+++6727Nmjhg0bSpI2b96sVatWqX379oqIiNDmzZv1xx9/OOoBAAAAgJJSqlYgpb/ugProo49qwoQJiouLU8+ePXXs2LE8+/fp00e7du1S9+7d830JbF68vLw0btw41atXTzfffLPc3d21aNEiSZKHh4defvllvfHGG4qMjFTXrl0lSf/973916tQpNWrUSH379tWIESMUERHhNO6MGTO0YsUKRUVFOQJihw4dNH78eI0ZM0Y33nijTp8+rX79+jn2CQwM1HfffafbbrtN1atX15NPPqkZM2aoU6dORXKsAAAAAGCVzVzueRC4JmRkZCgoKEjp6eml5oY6KVMufydZFE7lCbtdXQIAAABKocJmg1K3AgkAAAAAKJ2uywB5//33y9/fP9fX/fff7+ryAAAAAKBUKlU30SkpU6ZMyfOxHKXlEk8AAAAAKG2uywAZERGR4wY3AAAAAIDLuy4vYQUAAAAA5B8BEgAAAABgCQESAAAAAGAJARIAAAAAYMl1eRMduB4PugcAAACuPqxAAgAAAAAsIUACAAAAACwhQAIAAAAALCFAAgAAAAAsIUACAAAAACwhQAIAAAAALCFAAgAAAAAs4TmQcIkWr7RwdQklav1D611dAgAAAFBorEACAAAAACwhQAIAAAAALCFAAgAAAAAsIUACAAAAACwhQAIAAAAALCFAAgAAAAAsIUACAAAAACwhQAIAAAAALCFAAgAAAAAsIUACAAAAACwhQAIAAAAALCFAAgAAAAAsIUAWs2XLlqlly5YKDg5WaGioOnfurOTkZMf2DRs2qEGDBvLx8VHjxo316aefymazaefOnY4+e/bsUadOneTv769y5cqpb9++On78uAuOBgAAAMD1jABZzM6cOaNRo0Zp27ZtWrVqldzc3NS9e3fZ7XZlZGSoS5cuqlu3rn744QdNnTpVY8eOddo/LS1Nbdq0UcOGDbVt2zYtW7ZMR48eVY8ePfL8zKysLGVkZDi9AAAAAKCwPFxdwLXuzjvvdHr/9ttvKzw8XPv27dO6detks9k0Z84c+fj4qFatWvrtt980ZMgQR/9XX31VDRs21DPPPOM0RlRUlPbv36/q1avn+Mxp06Zp8uTJxXdQAAAAAK5LrEAWswMHDqhXr16qWrWqAgMDFRMTI0lKSUlRUlKS6tWrJx8fH0f/Jk2aOO2/a9curV69Wv7+/o5XzZo1JcnpUti/GzdunNLT0x2vw4cPF8/BAQAAALiusAJZzLp06aLo6GjNmTNHkZGRstvtqlOnjs6fP29p/8zMTHXp0kXPPvtsjm0VKlTIdR9vb295e3sXqm4AAAAA+CcCZDE6ceKEkpKSNGfOHLVq1UqStG7dOsf2GjVq6N1331VWVpYj8G3dutVpjEaNGumjjz5STEyMPDz4dQEAAABwHS5hLUYhISEKDQ3Vm2++qZ9//lnffvutRo0a5djeu3dv2e12DR06VImJiVq+fLleeOEFSZLNZpMkDRs2TCdPnlSvXr20detWJScna/ny5Ro4cKCys7NdclwAAAAArk8EyGLk5uamRYsWafv27apTp44eeeQRPf/8847tgYGBWrp0qXbu3KkGDRroiSee0IQJEyTJ8b3IyMhIrV+/XtnZ2Wrfvr3q1q2rkSNHKjg4WG5u/PoAAAAAlByuiSxm7dq10759+5zajDGOn5s3b65du3Y53i9cuFCenp6qXLmyoy02NlYff/xx8RcLAAAAAJdBgHSxBQsWqGrVqqpYsaJ27dqlsWPHqkePHvL19XV1aQAAAADghADpYkeOHNGECRN05MgRVahQQXfffbeefvppV5cFAAAAADkQIF1szJgxGjNmjKvLAAAAAIAr4i4sAAAAAABLCJAAAAAAAEsIkAAAAAAASwiQAAAAAABLCJAAAAAAAEu4CytcYv1D611dAgAAAIB8YgUSAAAAAGAJARIAAAAAYAkBEgAAAABgCQESAAAAAGAJARIAAAAAYAkBEgAAAABgCQESAAAAAGAJz4GES6y9OcHVJViS8N1aV5cAAAAAlBqsQAIAAAAALCFAAgAAAAAsIUACAAAAACwhQAIAAAAALCFAAgAAAAAsIUACAAAAACwhQAIAAAAALCFAAgAAAAAsIUACAAAAACwhQAIAAAAALCFAlqA1a9bIZrMpLS3N1aUAAAAAQL4RIItJ69atNXLkSKe25s2bKzU1VUFBQa4pCgAAAAAKwcPVBVxPvLy8VL58eVeXAQAAAAAFUipXILOysjRixAhFRETIx8dHLVu21NatWx3b9+7dq86dOyswMFABAQFq1aqVkpOTHdvffvtt1a5dW97e3qpQoYKGDx8uSTp06JBsNpt27tzp6JuWliabzaY1a9ZI+r/LTL/88kvVq1dPPj4+uummm7Rnzx7HPidOnFCvXr1UsWJFlSlTRnXr1tX777/v2D5gwACtXbtWs2bNks1mk81m06FDh3K9hPWjjz5y1BoTE6MZM2Y4zUVMTIyeeeYZDRo0SAEBAapcubLefPPNK85fRkaG0wsAAAAACqtUBsgxY8boo48+0vz58/XDDz+oWrVq6tChg06ePKnffvtNN998s7y9vfXtt99q+/btGjRokC5evChJeu211zRs2DANHTpUu3fv1ueff65q1arlu4bRo0drxowZ2rp1q8LDw9WlSxdduHBBknTu3DnFx8fryy+/1J49ezR06FD17dtXW7ZskSTNmjVLzZo105AhQ5SamqrU1FRFRUXl+Izt27erR48euueee7R7925NmjRJ48eP17x585z6zZgxQ40bN9aOHTv04IMP6oEHHlBSUlKetU+bNk1BQUGOV26fDQAAAAD5ZTPGGFcX8XdnzpxRSEiI5s2bp969e0uSLly4oJiYGI0cOVKnTp3SokWLlJSUJE9Pzxz7V6xYUQMHDtRTTz2VY9uhQ4dUpUoV7dixQw0aNJD01wpkSEiIVq9erdatW2vNmjW65ZZbtGjRIvXs2VOSdPLkSVWqVEnz5s1Tjx49cq27c+fOqlmzpl544QVJf30HskGDBpo5c6ajz6WxT506peDgYPXp00d//PGHvvnmG0efMWPG6Msvv9TevXsl/bUC2apVK73zzjuSJGOMypcvr8mTJ+v+++/PtZasrCxlZWU53mdkZCgqKkrp6ekKDAzMdZ+StvbmBFeXYEnCd2tdXQIAAABQZDIyMhQUFFTgbFDqvgOZnJysCxcuqEWLFo42T09PNWnSRImJiTpy5IhatWqVa3g8duyYfv/9d7Vt27bQdTRr1szxc9myZVWjRg0lJiZKkrKzs/XMM89o8eLF+u2333T+/HllZWWpTJky+fqMxMREde3a1amtRYsWmjlzprKzs+Xu7i5JqlevnmO7zWZT+fLldezYsTzH9fb2lre3d75qAQAAAIArKXUB8kp8fX0LtE2S3Nz+umL374uuly5LzY/nn39es2bN0syZM1W3bl35+flp5MiROn/+fL7HsuKfYdlms8lutxfLZwEAAABAXkrddyBvuOEGeXl5af369Y62CxcuaOvWrapVq5bq1aun77//PtfgFxAQoJiYGK1atSrXscPDwyVJqampjra/31Dn7zZt2uT4+dSpU9q/f7/i4uIkSevXr1fXrl3173//W/Xr11fVqlW1f/9+p/29vLyUnZ192WONi4tzOs5LY1evXt2x+ggAAAAApUWpW4H08/PTAw88oNGjR6ts2bKqXLmynnvuOZ09e1aDBw+W3W7XK6+8onvuuUfjxo1TUFCQNm3apCZNmqhGjRqaNGmS7r//fkVERKhTp046ffq01q9fr4ceeki+vr666aabNH36dFWpUkXHjh3Tk08+mWsdU6ZMUWhoqMqVK6cnnnhCYWFh6tatmyQpNjZWS5Ys0YYNGxQSEqIXX3xRR48eVa1atRz7x8TEaPPmzTp06JD8/f1VtmzZHJ/x6KOP6sYbb9TUqVPVs2dPbdy4Ua+++qpmz55dLHMLAAAAAIVR6lYgJWn69Om688471bdvXzVq1Eg///yzli9frpCQEIWGhurbb79VZmamEhISFB8frzlz5jgu8+zfv79mzpyp2bNnq3bt2urcubMOHDjgGPvtt9/WxYsXFR8fr5EjR+Z6s51LNTz88MOKj4/XkSNHtHTpUnl5eUmSnnzySTVq1EgdOnRQ69atVb58eUe4vOSxxx6Tu7u7atWqpfDwcKWkpOT4jEaNGmnx4sVatGiR6tSpowkTJmjKlCkaMGBA0UwkAAAAABShUncXVlf7551SrwWFvdNSceAurAAAAEDJK2w2KJUrkAAAAACA0ocACQAAAACwpNTdRMfVWrduLa7qBQAAAICcWIEEAAAAAFhCgAQAAAAAWEKABAAAAABYQoAEAAAAAFhCgAQAAAAAWMJdWOESCd+tdXUJAAAAAPKJFUgAAAAAgCUESAAAAACAJQRIAAAAAIAlBEgAAAAAgCUESAAAAACAJQRIAAAAAIAlBEgAAAAAgCU8BxIu8eqjSwu1//AZXYqoEgAAAABWsQIJAAAAALCEAAkAAAAAsIQACQAAAACwhAAJAAAAALCEAAkAAAAAsIQACQAAAACwhAAJAAAAALCEAAkAAAAAsIQACQAAAACw5KoJkK1bt9bIkSPz3B4TE6OZM2cWy9gFYbPZ9OmnnxbpmAAAAADgSldNgLySrVu3aujQoZfts2bNGtlsNqWlpZVMUfkwadIkNWjQwNVlAAAAAECeChwg33nnHbVo0UKRkZH69ddfJUkzZ87UZ599VmTF5Ud4eLjKlCmT5/YLFy6UYDUAAAAAcO0pUIB87bXXNGrUKN12221KS0tTdna2JCk4OLjAl5FacfHiRQ0fPlxBQUEKCwvT+PHjZYyRlPMSVpvNptdee0233367/Pz8NGTIEN1yyy2SpJCQENlsNg0YMMDR3263a8yYMSpbtqzKly+vSZMmWa7rwIEDuvnmm+Xj46NatWppxYoVOfqMHTtW1atXV5kyZVS1alWNHz/eEWrnzZunyZMna9euXbLZbLLZbJo3b54kKS0tTffee6/Cw8MVGBioNm3aaNeuXfmbOAAAAAAoAh4F2emVV17RnDlz1K1bN02fPt3R3rhxYz322GNFVtw/zZ8/X4MHD9aWLVu0bds2DR06VJUrV9aQIUNy7T9p0iRNnz5dM2fOlLu7u26//XbdeeedSkpKUmBgoHx9fZ3GHjVqlDZv3qyNGzdqwIABatGihW699dbL1mS323XHHXeoXLly2rx5s9LT03P9PmVAQIDmzZunyMhI7d69W0OGDFFAQIDGjBmjnj17as+ePVq2bJlWrlwpSQoKCpIk3X333fL19dXXX3+toKAgvfHGG2rbtq3279+vsmXL5lpTVlaWsrKyHO8zMjIuewwAAAAAYEWBAuTBgwfVsGHDHO3e3t46c+ZMoYvKS1RUlF566SXZbDbVqFFDu3fv1ksvvZRngOzdu7cGDhzoVLckRUREKDg42KlvvXr1NHHiRElSbGysXn31Va1ateqKAXLlypX66aeftHz5ckVGRkqSnnnmGXXq1Mmp35NPPun4OSYmRo899pgWLVqkMWPGyNfXV/7+/vLw8FD58uUd/datW6ctW7bo2LFj8vb2liS98MIL+vTTT7VkyZI8v/M5bdo0TZ48+bJ1AwAAAEB+FegS1ipVqmjnzp052pctW6a4uLjC1pSnm266STabzfG+WbNmOnDggOMS2n9q3Lix5bHr1avn9L5ChQo6duzYFfdLTExUVFSUIzxequufPvjgA7Vo0ULly5eXv7+/nnzySaWkpFx27F27dikzM1OhoaHy9/d3vA4ePKjk5OQ89xs3bpzS09Mdr8OHD1/xOAAAAADgSgq0Ajlq1CgNGzZM586dkzFGW7Zs0fvvv69p06bprbfeKuoaC8zPz89yX09PT6f3NptNdru9SOrYuHGj+vTpo8mTJ6tDhw4KCgrSokWLNGPGjMvul5mZqQoVKmjNmjU5tv1zBfXvvL29HSuWAAAAAFBUChQg7733Xvn6+urJJ5/U2bNn1bt3b0VGRmrWrFm65557irpGh82bNzu937Rpk2JjY+Xu7m5pfy8vL0nKc8WyIOLi4nT48GGlpqaqQoUKjrr+bsOGDYqOjtYTTzzhaLt059q/1/bPuho1aqQjR47Iw8NDMTExRVYzAAAAABREvi9hvXjxohYsWKB27drpwIEDyszM1JEjR/S///1PgwcPLo4aHVJSUjRq1CglJSXp/fff1yuvvKKHH37Y8v7R0dGy2Wz64osv9McffygzM7PQNbVr107Vq1dX//79tWvXLn3//fdOQVH66zuVKSkpWrRokZKTk/Xyyy/rk08+ceoTExOjgwcPaufOnTp+/LiysrLUrl07NWvWTN26ddM333yjQ4cOacOGDXriiSe0bdu2QtcOAAAAAPmR7wDp4eGh+++/X+fOnZMklSlTRhEREUVeWG769eunP//8U02aNNGwYcP08MMP53kjmdxUrFhRkydP1uOPP65y5cpp+PDhha7Jzc1Nn3zyiaOue++9V08//bRTn9tvv12PPPKIhg8frgYNGmjDhg0aP368U58777xTHTt21C233KLw8HC9//77stls+uqrr3TzzTdr4MCBql69uu655x79+uuvKleuXKFrBwAAAID8sJlLD1LMh9atW2vkyJHq1q1bMZSEopaRkaGgoCClp6crMDDQ1eVIkl59dGmh9h8+o0sRVQIAAABcPwqbDQr0HcgHH3xQjz76qP73v/8pPj4+x81q/nlHUwAAAADA1a9AAfLSjXJGjBjhaLPZbDLGyGazFelNalxt4cKFuu+++3LdFh0drb1795ZwRQAAAADgGgUKkAcPHizqOkqt22+/XU2bNs112z8f/QEAAAAA17ICBcjo6OiirqPUCggIUEBAgKvLAAAAAACXK1CAXLBgwWW39+vXr0DFAAAAAABKrwIFyH8+e/HChQs6e/asvLy8VKZMGQIkAAAAAFyD8v0cSEk6deqU0yszM1NJSUlq2bKl3n///aKuEQAAAABQChQoQOYmNjZW06dPz7E6CQAAAAC4NhToEtY8B/Pw0O+//16UQ+IaNXxGF1eXAAAAACCfChQgP//8c6f3xhilpqbq1VdfVYsWLYqkMAAAAABA6VKgANmtWzen9zabTeHh4WrTpo1mzJhRFHUBAAAAAEqZAgVIu91e1HUAAAAAAEq5At1EZ8qUKTp79myO9j///FNTpkwpdFEAAAAAgNLHZowx+d3J3d1dqampioiIcGo/ceKEIiIilJ2dXWQFovAyMjIUFBSk9PR0BQYGurocAAAAAC5S2GxQoBVIY4xsNluO9l27dqls2bIFGRIAAAAAUMrl6zuQISEhstlsstlsql69ulOIzM7OVmZmpu6///4iLxIAAAAA4Hr5CpAzZ86UMUaDBg3S5MmTFRQU5Njm5eWlmJgYNWvWrMiLxLXn6X/fVeB9n3h3SRFWAgAAAMCqfAXI/v37S5KqVKmi5s2by9PTs1iKAgAAAACUPgV6jEdCQoLj53Pnzun8+fNO27lRCwAAAABcewp0E52zZ89q+PDhioiIkJ+fn0JCQpxeAAAAAIBrT4EC5OjRo/Xtt9/qtddek7e3t9566y1NnjxZkZGRWrBgQVHXCAAAAAAoBQp0CevSpUu1YMECtW7dWgMHDlSrVq1UrVo1RUdHa+HCherTp09R1wkAAAAAcLECrUCePHlSVatWlfTX9x1PnjwpSWrZsqW+++67oqsOAAAAAFBqFChAVq1aVQcPHpQk1axZU4sXL5b018pkcHBwkRUHAAAAACg9ChQgBw4cqF27dkmSHn/8cf3nP/+Rj4+PHnnkEY0ePbpICwQAAAAAlA4F+g7kI4884vi5Xbt2+umnn7R9+3ZVq1ZN9erVK7LiAAAAAAClR4EC5N+dO3dO0dHRio6OLop6AAAAAAClVIEuYc3OztbUqVNVsWJF+fv765dffpEkjR8/Xv/973+LtMDiEBMTo5kzZ5boZx46dEg2m007d+6UJK1Zs0Y2m01paWklWgcAAAAAFFSBAuTTTz+tefPm6bnnnpOXl5ejvU6dOnrrrbeKrLjCmjdvXq439dm6dauGDh1a8gX9TfPmzZWamqqgoCBJedcKAAAAAKVFgQLkggUL9Oabb6pPnz5yd3d3tNevX18//fRTkRVXXMLDw1WmTBmX1uDl5aXy5cvLZrO5tA4AAAAAsKpAAfK3335TtWrVcrTb7XZduHCh0EVd0rp1a40YMUJjxoxR2bJlVb58eU2aNMmx/cUXX1TdunXl5+enqKgoPfjgg8rMzJT01yWiAwcOVHp6umw2m2w2m2Pff17CmpKSoq5du8rf31+BgYHq0aOHjh496tg+adIkNWjQQO+8845iYmIUFBSke+65R6dPn3b0WbZsmVq2bKng4GCFhoaqc+fOSk5OzvPY/n4Ja161TpkyRXXq1Mmxb4MGDTR+/Pg8x87KylJGRobTCwAAAAAKq0ABslatWvr+++9ztC9ZskQNGzYsdFF/N3/+fPn5+Wnz5s167rnnNGXKFK1YsUKS5Obmppdffll79+7V/Pnz9e2332rMmDGS/rpEdObMmQoMDFRqaqpSU1P12GOP5Rjfbrera9euOnnypNauXasVK1bol19+Uc+ePZ36JScn69NPP9UXX3yhL774QmvXrtX06dMd28+cOaNRo0Zp27ZtWrVqldzc3NS9e3fZ7fYrHmNetQ4aNEiJiYnaunWro++OHTv0448/auDAgXmON23aNAUFBTleUVFRV6wBAAAAAK6kQHdhnTBhgvr376/ffvtNdrtdH3/8sZKSkrRgwQJ98cUXRVpgvXr1NHHiRElSbGysXn31Va1atUq33nqrRo4c6egXExOjp556Svfff79mz54tLy8vBQUFyWazqXz58nmOv2rVKu3evVsHDx50BK0FCxaodu3a2rp1q2688UZJfwXNefPmKSAgQJLUt29frVq1Sk8//bQk6c4773Qa9+2331Z4eLj27duX6yri3+VVq7+/vzp06KC5c+c66pg7d64SEhJUtWrVPMcbN26cRo0a5XifkZFBiAQAAABQaPlagfzll19kjFHXrl21dOlSrVy5Un5+fpowYYISExO1dOlS3XrrrUVa4D+fK1mhQgUdO3ZMkrRy5Uq1bdtWFStWVEBAgPr27asTJ07o7NmzlsdPTExUVFSUU8CqVauWgoODlZiY6GiLiYlxhMd/1iFJBw4cUK9evVS1alUFBgYqJiZG0l+XxxbGkCFD9P777+vcuXM6f/683nvvPQ0aNOiy+3h7eyswMNDpBQAAAACFla8VyNjYWKWmpioiIkKtWrVS2bJltXv3bpUrV6646pOnp6fTe5vNJrvdrkOHDqlz58564IEH9PTTT6ts2bJat26dBg8erPPnzxf5TXLyquOSLl26KDo6WnPmzFFkZKTsdrvq1Kmj8+fPF+pzu3TpIm9vb33yySfy8vLShQsXdNdddxVqTAAAAAAoiHwFSGOM0/uvv/5aZ86cKdKCrNq+fbvsdrtmzJghN7e/FlIXL17s1MfLy0vZ2dmXHScuLk6HDx/W4cOHHauQ+/btU1pammrVqmWplhMnTigpKUlz5sxRq1atJEnr1q3L1/HkVauHh4f69++vuXPnysvLS/fcc498fX3zNTYAAAAAFIUCfQfykn8GypJUrVo1XbhwQa+88oq6dOmi9evX6/XXX3fqExMTo8zMTK1atUr169dXmTJlcqxMtmvXTnXr1lWfPn00c+ZMXbx4UQ8++KASEhLUuHFjS7WEhIQoNDRUb775pipUqKCUlBQ9/vjj+Tqey9V67733Ki4uTpK0fv36fI0LAAAAAEUlX9+BvPSIiX+2uUL9+vX14osv6tlnn1WdOnW0cOFCTZs2zalP8+bNdf/996tnz54KDw/Xc889l2Mcm82mzz77TCEhIbr55pvVrl07Va1aVR988IHlWtzc3LRo0SJt375dderU0SOPPKLnn38+X8dzuVpjY2PVvHlz1axZU02bNs3XuAAAAABQVGwmH8uIbm5u6tSpk7y9vSVJS5cuVZs2beTn5+fU7+OPPy7aKq9zxhjFxsbqwQcfdLq7qlUZGRkKCgpSenp6qbmhztP/Lvj3OJ94d0kRVgIAAABcPwqbDfJ1CWv//v2d3v/73//O9wcif/744w8tWrRIR44cueyzHwEAAACguOUrQM6dO7e46kAeIiIiFBYWpjfffFMhISGuLgcAAADAdaxQN9FB8XPljYoAAAAA4O/ydRMdAAAAAMD1iwAJAAAAALCEAAkAAAAAsIQACQAAAACwhAAJAAAAALCEu7DCJZ54d4mrSwAAAACQT6xAAgAAAAAsIUACAAAAACwhQAIAAAAALCFAAgAAAAAsIUACAAAAACwhQAIAAAAALCFAAgAAAAAs4TmQcInEp7/Nc1vcE21KsBIAAAAAVrECCQAAAACwhAAJAAAAALCEAAkAAAAAsIQACQAAAACwhAAJAAAAALCEAAkAAAAAsIQACQAAAACwhAAJAAAAALCEAAkAAAAAsOSqDpDGGA0dOlRly5aVzWZTcHCwRo4c6eqyAAAAAOCa5OHqAgpj2bJlmjdvntasWaOqVavKzc1Nvr6+hRrTZrPpk08+Ubdu3YqmSAAAAAC4RlzVATI5OVkVKlRQ8+bNLfU/f/68vLy8irkqAAAAALg2XbWXsA4YMEAPPfSQUlJSZLPZFBMTo9atWztdwhoTE6OpU6eqX79+CgwM1NChQ3X+/HkNHz5cFSpUkI+Pj6KjozVt2jRHf0nq3r27Y0xJmjRpkho0aKB33nlHMTExCgoK0j333KPTp087PmvZsmVq2bKlgoODFRoaqs6dOys5Odmx/dChQ7LZbFq8eLFatWolX19f3Xjjjdq/f7+2bt2qxo0by9/fX506ddIff/zhdKxvvfWW4uLi5OPjo5o1a2r27NnFM6kAAAAAcBlXbYCcNWuWpkyZokqVKik1NVVbt27Ntd8LL7yg+vXra8eOHRo/frxefvllff7551q8eLGSkpK0cOFCR1C8NMbcuXNzjJmcnKxPP/1UX3zxhb744gutXbtW06dPd2w/c+aMRo0apW3btmnVqlVyc3NT9+7dZbfbneqZOHGinnzySf3www/y8PBQ7969NWbMGM2aNUvff/+9fv75Z02YMMHRf+HChZowYYKefvppJSYm6plnntH48eM1f/78POcmKytLGRkZTi8AAAAAKKyr9hLWoKAgBQQEyN3dXeXLl8+zX5s2bfToo4863qekpCg2NlYtW7aUzWZTdHS0Y1t4eLgkKTg4OMeYdrtd8+bNU0BAgCSpb9++WrVqlZ5++mlJ0p133unU/+2331Z4eLj27dunOnXqONofe+wxdejQQZL08MMPq1evXlq1apVatGghSRo8eLDmzZvn6D9x4kTNmDFDd9xxhySpSpUq2rdvn9544w31798/12OeNm2aJk+enOecAAAAAEBBXLUrkFY1btzY6f2AAQO0c+dO1ahRQyNGjNA333xjaZyYmBhHeJSkChUq6NixY473Bw4cUK9evVS1alUFBgY6VjVTUlKcxqlXr57j53LlykmS6tat69R2adwzZ84oOTlZgwcPlr+/v+P11FNPOV0e+0/jxo1Tenq643X48GFLxwgAAAAAl3PVrkBa5efn5/S+UaNGOnjwoL7++mutXLlSPXr0ULt27bRkyZLLjuPp6en03mazOV2e2qVLF0VHR2vOnDmKjIyU3W5XnTp1dP78+TzHsdlsubZdGjczM1OSNGfOHDVt2tRpHHd39zxr9fb2lre392WPBwAAAADy65oPkLkJDAxUz5491bNnT911113q2LGjTp48qbJly8rT01PZ2dn5Gu/EiRNKSkrSnDlz1KpVK0nSunXrCl1nuXLlFBkZqV9++UV9+vQp9HgAAAAAUBjXXYB88cUXVaFCBTVs2FBubm768MMPVb58eQUHB0v661LVS99J9Pb2VkhIyBXHDAkJUWhoqN58801VqFBBKSkpevzxx4uk3smTJ2vEiBEKCgpSx44dlZWVpW3btunUqVMaNWpUkXwGAAAAAFhxzX8H8p8CAgL03HPPqXHjxrrxxht16NAhffXVV3Jz+2sqZsyYoRUrVigqKkoNGza0NKabm5sWLVqk7du3q06dOnrkkUf0/PPPF0m99957r9566y3NnTtXdevWVUJCgubNm6cqVaoUyfgAAAAAYJXNGGNcXQSKV0ZGhoKCgpSenq7AwEBXlyNJSnz62zy3xT3RpgQrAQAAAK4fhc0G190KJAAAAACgYAiQAAAAAABLCJAAAAAAAEsIkAAAAAAASwiQAAAAAABLCJAAAAAAAEsIkAAAAAAASwiQAAAAAABLPFxdAK5PcU+0cXUJAAAAAPKJFUgAAAAAgCUESAAAAACAJQRIAAAAAIAlBEgAAAAAgCUESAAAAACAJQRIAAAAAIAlBEgAAAAAgCU8BxIuMWnSpFx/BgAAAFB6sQIJAAAAALCEAAkAAAAAsIQACQAAAACwhAAJAAAAALCEAAkAAAAAsIQACQAAAACwhAAJAAAAALCEAAkAAAAAsIQACQAAAACwhAAJAAAAALCEAFnCWrdurZEjR1rqO2/ePAUHBxdrPQAAAABgFQESAAAAAGAJARIAAAAAYAkB0oVOnTqlfv36KSQkRGXKlFGnTp104MCBHP0+/fRTxcbGysfHRx06dNDhw4cvO25WVpYyMjKcXgAAAABQWARIFxowYIC2bdumzz//XBs3bpQxRrfddpsuXLjg6HP27Fk9/fTTWrBggdavX6+0tDTdc889lx132rRpCgoKcryioqKK+1AAAAAAXAcIkC5y4MABff7553rrrbfUqlUr1a9fXwsXLtRvv/2mTz/91NHvwoULevXVV9WsWTPFx8dr/vz52rBhg7Zs2ZLn2OPGjVN6errjdaUVSwAAAACwggDpIomJifLw8FDTpk0dbaGhoapRo4YSExMdbR4eHrrxxhsd72vWrKng4GCnPv/k7e2twMBApxcAAAAAFBYBEgAAAABgCQHSReLi4nTx4kVt3rzZ0XbixAklJSWpVq1ajraLFy9q27ZtjvdJSUlKS0tTXFxcidYLAAAAAARIF4mNjVXXrl01ZMgQrVu3Trt27dK///1vVaxYUV27dnX08/T01EMPPaTNmzdr+/btGjBggG666SY1adLEhdUDAAAAuB4RIF1o7ty5io+PV+fOndWsWTMZY/TVV1/J09PT0adMmTIaO3asevfurRYtWsjf318ffPCBC6sGAAAAcL2yGWOMq4tA8crIyFBQUJDS09NLzQ11Jk2alOvPAAAAAIpPYbMBK5AAAAAAAEsIkAAAAAAASwiQAAAAAABLCJAAAAAAAEsIkAAAAAAASwiQAAAAAABLCJAAAAAAAEsIkAAAAAAAS2zGGOPqIlC8CvuwUAAAAADXhsJmA1YgAQAAAACWECABAAAAAJYQIAEAAAAAlhAgAQAAAACWECABAAAAAJYQIAEAAAAAlhAgAQAAAACWeLi6AFyfFn/YxPFzj7u3uLASAAAAAFaxAgkAAAAAsIQACQAAAACwhAAJAAAAALCEAAkAAAAAsIQACQAAAACwhAAJAAAAALCEAAkAAAAAsIQACQAAAACwhAAJAAAAALCk1AbINWvWyGazKS0tzdWlFJkBAwaoW7duri4DAAAAAArEw9UFXIsOHTqkKlWqaMeOHWrQoIGjfdasWTLGuK4wAAAAACgEAuTfnD9/Xl5eXsU2flBQULGNDQAAAADFzaWXsGZlZWnEiBGKiIiQj4+PWrZsqa1btzr1Wb9+verVqycfHx/ddNNN2rNnj2Pbr7/+qi5duigkJER+fn6qXbu2vvrqK8f2PXv2qFOnTvL391e5cuXUt29fHT9+3LG9devWGj58uEaOHKmwsDB16NBBvXv3Vs+ePZ1quHDhgsLCwrRgwQJJ0rJly9SyZUsFBwcrNDRUnTt3VnJysqN/lSpVJEkNGzaUzWZT69atJeW8hPVKx3/pMt5Vq1apcePGKlOmjJo3b66kpKQCzjgAAAAAFJxLA+SYMWP00Ucfaf78+frhhx9UrVo1dejQQSdPnnT0GT16tGbMmKGtW7cqPDxcXbp00YULFyRJw4YNU1ZWlr777jvt3r1bzz77rPz9/SVJaWlpatOmjRo2bKht27Zp2bJlOnr0qHr06OFUw/z58+Xl5aX169fr9ddfV58+fbR06VJlZmY6+ixfvlxnz55V9+7dJUlnzpzRqFGjtG3bNq1atUpubm7q3r277Ha7JGnLli2SpJUrVyo1NVUff/xxgY9fkp544gnNmDFD27Ztk4eHhwYNGnTZec3KylJGRobTCwAAAAAKzbhIZmam8fT0NAsXLnS0nT9/3kRGRprnnnvOrF692kgyixYtcmw/ceKE8fX1NR988IExxpi6deuaSZMm5Tr+1KlTTfv27Z3aDh8+bCSZpKQkY4wxCQkJpmHDhk59Lly4YMLCwsyCBQscbb169TI9e/bM81j++OMPI8ns3r3bGGPMwYMHjSSzY8cOp379+/c3Xbt2tXT8xhjHHKxcudLR58svvzSSzJ9//plnPRMnTjSScrzS09Pz3KekfbD4RscLAAAAQMlIT08vVDZw2QpkcnKyLly4oBYtWjjaPD091aRJEyUmJjramjVr5vi5bNmyqlGjhmP7iBEj9NRTT6lFixaaOHGifvzxR0ffXbt2afXq1fL393e8atas6fjsS+Lj453q8vDwUI8ePbRw4UJJf602fvbZZ+rTp4+jz4EDB9SrVy9VrVpVgYGBiomJkSSlpKQU+fFLUr169Rw/V6hQQZJ07NixPMceN26c0tPTHa/Dhw9brgsAAAAA8nJV30Tn3nvvVYcOHfTll1/qm2++0bRp0zRjxgw99NBDyszMVJcuXfTss8/m2O9SCJMkPz+/HNv79OmjhIQEHTt2TCtWrJCvr686duzo2N6lSxdFR0drzpw5ioyMlN1uV506dXT+/PliOU5PT0/HzzabTZIcl8vmxtvbW97e3sVSCwAAAIDrl8tWIG+44QbHdw8vuXDhgrZu3apatWo52jZt2uT4+dSpU9q/f7/i4uIcbVFRUbr//vv18ccf69FHH9WcOXMkSY0aNdLevXsVExOjatWqOb1yC41/17x5c0VFRemDDz7QwoULdffddztC3IkTJ5SUlKQnn3xSbdu2VVxcnE6dOuW0/6U7uWZnZxf6+AEAAACgtHDZCqSfn58eeOABjR49WmXLllXlypX13HPP6ezZsxo8eLB27dolSZoyZYpCQ0NVrlw5PfHEEwoLC3PcyXTkyJHq1KmTqlevrlOnTmn16tWOcDls2DDNmTNHvXr10pgxY1S2bFn9/PPPWrRokd566y25u7tftr7evXvr9ddf1/79+7V69WpHe0hIiEJDQ/Xmm2+qQoUKSklJ0eOPP+60b0REhHx9fbVs2TJVqlRJPj4+OR7hcaXjBwAAAIDSxqV3YZ0+fbruvPNO9e3bV40aNdLPP/+s5cuXKyQkxKnPww8/rPj4eB05ckRLly51WuEbNmyY4uLi1LFjR1WvXl2zZ8+WJEVGRmr9+vXKzs5W+/btVbduXY0cOVLBwcFyc7vyYffp00f79u1TxYoVnb6n6ObmpkWLFmn79u2qU6eOHnnkET3//PNO+3p4eOjll1/WG2+8ocjISHXt2rXAxw8AAAAApYXNGGNcXQSKV0ZGhoKCgpSenq7AwEBXlyNJWvxhE8fPPe7e4sJKAAAAgOtHYbOBS1cgAQAAAABXDwIkAAAAAMASAiQAAAAAwBICJAAAAADAEgIkAAAAAMASAiQAAAAAwBICJAAAAADAEgIkAAAAAMASD1cXgOtTj7u3uLoEAAAAAPnECiQAAAAAwBICJAAAAADAEgIkAAAAAMASAiQAAAAAwBICJAAAAADAEgIkAAAAAMASAiQAAAAAwBICJEpc/SXLXV0CAAAAgAIgQAIAAAAALCFAAgAAAAAsIUACAAAAACwhQAIAAAAALCFAAgAAAAAsIUACAAAAACwhQAIAAAAALCFAAgAAAAAsIUACAAAAACwhQAIAAAAALCFAFlBMTIxmzpzp6jIAAAAAoMQQIAEAAAAAlhAgr2Lnz593dQkAAAAAriNXfYC02+2aNm2aqlSpIl9fX9WvX19LliyRJK1Zs0Y2m03Lly9Xw4YN5evrqzZt2ujYsWP6+uuvFRcXp8DAQPXu3Vtnz551jNm6dWsNHz5cw4cPV1BQkMLCwjR+/HgZY/KsIyUlRV27dpW/v78CAwPVo0cPHT16VJJ06NAhubm5adu2bU77zJw5U9HR0bLb7ZKkPXv2qFOnTvL391e5cuXUt29fHT9+PEddI0eOVFhYmDp06JBrLVlZWcrIyHB6AQAAAEBhXfUBctq0aVqwYIFef/117d27V4888oj+/e9/a+3atY4+kyZN0quvvqoNGzbo8OHD6tGjh2bOnKn33ntPX375pb755hu98sorTuPOnz9fHh4e2rJli2bNmqUXX3xRb731Vq412O12de3aVSdPntTatWu1YsUK/fLLL+rZs6ekv74v2a5dO82dO9dpv7lz52rAgAFyc3NTWlqa2rRpo4YNG2rbtm1atmyZjh49qh49euSoy8vLS+vXr9frr7+e55wEBQU5XlFRUfmeVwAAAADIwVzFzp07Z8qUKWM2bNjg1D548GDTq1cvs3r1aiPJrFy50rFt2rRpRpJJTk52tN13332mQ4cOjvcJCQkmLi7O2O12R9vYsWNNXFyc4310dLR56aWXjDHGfPPNN8bd3d2kpKQ4tu/du9dIMlu2bDHGGPPBBx+YkJAQc+7cOWOMMdu3bzc2m80cPHjQGGPM1KlTTfv27Z2O4/Dhw0aSSUpKctTVsGFDS/OSnp7ueF0aJz09/Yr7loR6Hy5zdQkAAADAdSk9Pb1Q2eCqXoH8+eefdfbsWd16663y9/d3vBYsWKDk5GRHv3r16jl+LleunMqUKaOqVas6tR07dsxp7Jtuukk2m83xvlmzZjpw4ICys7Nz1JGYmKioqCinlb5atWopODhYiYmJkqRu3brJ3d1dn3zyiSRp3rx5uuWWWxQTEyNJ2rVrl1avXu10HDVr1pQkp2OJj4+/4rx4e3srMDDQ6QUAAAAAheXh6gIKIzMzU5L05ZdfqmLFik7bvL29HcHL09PT0W6z2ZzeX2q79D3E4uLl5aV+/fpp7ty5uuOOO/Tee+9p1qxZju2ZmZnq0qWLnn322Rz7VqhQwfGzn59fsdYJAAAAAHm5qgNkrVq15O3trZSUFCUkJOTY/veVu/zavHmz0/tNmzYpNjZW7u7uOfrGxcXp8OHDOnz4sGMVct++fUpLS1OtWrUc/e69917VqVNHs2fP1sWLF3XHHXc4tjVq1EgfffSRYmJi5OFxVf9aAAAAAFyjrupLWAMCAvTYY4/pkUce0fz585WcnKwffvhBr7zyiubPn1+osVNSUjRq1CglJSXp/fff1yuvvKKHH344177t2rVT3bp11adPH/3www/asmWL+vXrp4SEBDVu3NjRLy4uTjfddJPGjh2rXr16ydfX17Ft2LBhOnnypHr16qWtW7cqOTlZy5cv18CBA3O9bBYAAAAAStpVv9Q1depUhYeHa9q0afrll18UHBysRo0a6f/9v/9XqMtS+/Xrpz///FNNmjSRu7u7Hn74YQ0dOjTXvjabTZ999pkeeugh3XzzzXJzc1PHjh1z3NlVkgYPHqwNGzZo0KBBTu2RkZFav369xo4dq/bt2ysrK0vR0dHq2LGj3Nyu6pwPAAAA4BphM+YyDze8TrVu3VoNGjTQzJkzi3zsqVOn6sMPP9SPP/5Y5GPnJSMjQ0FBQUpPTy8VN9Spv2S5dt2V+zMsAQAAABSfwmYDlrZKSGZmpvbs2aNXX31VDz30kKvLAQAAAIB8I0CWkOHDhys+Pl6tW7fOcfkqAAAAAFwNuIT1OsAlrAAAAAAkLmEFAAAAAJQQAiQAAAAAwBICJAAAAADAEgIkAAAAAMASAiQAAAAAwBICJEocd2AFAAAArk4ESAAAAACAJQRIAAAAAIAlHq4uAMXPGCPpr4eGAgAAALh+XcoElzJCfhEgrwOnT5+WJEVFRbm4EgAAAAClwenTpxUUFJTv/WymoNETVw273a7ff/9dAQEBstlsri5HGRkZioqK0uHDhxUYGOjqcq5pzHXJYJ5LDnNdcpjrksE8lxzmumQwzyWnoHNtjNHp06cVGRkpN7f8f6ORFcjrgJubmypVquTqMnIIDAzkD0sJYa5LBvNccpjrksNclwzmueQw1yWDeS45BZnrgqw8XsJNdAAAAAAAlhAgAQAAAACWECBR4ry9vTVx4kR5e3u7upRrHnNdMpjnksNclxzmumQwzyWHuS4ZzHPJcdVccxMdAAAAAIAlrEACAAAAACwhQAIAAAAALCFAAgAAAAAsIUACAAAAACwhQCLf/vOf/ygmJkY+Pj5q2rSptmzZctn+H374oWrWrCkfHx/VrVtXX331ldN2Y4wmTJigChUqyNfXV+3atdOBAwec+pw8eVJ9+vRRYGCggoODNXjwYGVmZhb5sZU2RTnXFy5c0NixY1W3bl35+fkpMjJS/fr10++//+40RkxMjGw2m9Nr+vTpxXJ8pUlRn9cDBgzIMY8dO3Z06nM9ntdFPc//nONLr+eff97Rh3P6ynO9d+9e3XnnnY65mjlzZoHGPHfunIYNG6bQ0FD5+/vrzjvv1NGjR4vysEqdop7nadOm6cYbb1RAQIAiIiLUrVs3JSUlOfVp3bp1jnP6/vvvL+pDK3WKeq4nTZqUYx5r1qzp1Od6PKelop/r3P4O22w2DRs2zNHnejyv8zPPc+bMUatWrRQSEqKQkBC1a9cuR/8S+ze1AfJh0aJFxsvLy7z99ttm7969ZsiQISY4ONgcPXo01/7r16837u7u5rnnnjP79u0zTz75pPH09DS7d+929Jk+fboJCgoyn376qdm1a5e5/fbbTZUqVcyff/7p6NOxY0dTv359s2nTJvP999+batWqmV69ehX78bpSUc91Wlqaadeunfnggw/MTz/9ZDZu3GiaNGli4uPjncaJjo42U6ZMMampqY5XZmZmsR+vKxXHed2/f3/TsWNHp3k8efKk0zjX23ldHPP89/lNTU01b7/9trHZbCY5OdnRh3P6ynO9ZcsW89hjj5n333/flC9f3rz00ksFGvP+++83UVFRZtWqVWbbtm3mpptuMs2bNy+uw3S54pjnDh06mLlz55o9e/aYnTt3mttuu81UrlzZ6ZxNSEgwQ4YMcTqn09PTi+swS4XimOuJEyea2rVrO83jH3/84dTnejunjSmeuT527JjTPK9YscJIMqtXr3b0ud7O6/zOc+/evc1//vMfs2PHDpOYmGgGDBhggoKCzP/+9z9Hn5L6NzUBEvnSpEkTM2zYMMf77OxsExkZaaZNm5Zr/x49eph//etfTm1NmzY19913nzHGGLvdbsqXL2+ef/55x/a0tDTj7e1t3n//fWOMMfv27TOSzNatWx19vv76a2Oz2cxvv/1WZMdW2hT1XOdmy5YtRpL59ddfHW3R0dG5/vG/lhXHXPfv39907do1z8+8Hs/rkjinu3btatq0aePUxjl95bn+u7zm60pjpqWlGU9PT/Phhx86+iQmJhpJZuPGjYU4mtKrOOb5n44dO2YkmbVr1zraEhISzMMPP1yQkq9axTHXEydONPXr189zv+vxnDamZM7rhx9+2Nxwww3Gbrc72q6387ow82yMMRcvXjQBAQFm/vz5xpiS/Tc1l7DCsvPnz2v79u1q166do83NzU3t2rXTxo0bc91n48aNTv0lqUOHDo7+Bw8e1JEjR5z6BAUFqWnTpo4+GzduVHBwsBo3buzo065dO7m5uWnz5s1FdnylSXHMdW7S09Nls9kUHBzs1D59+nSFhoaqYcOGev7553Xx4sWCH0wpV5xzvWbNGkVERKhGjRp64IEHdOLECacxrqfzuiTO6aNHj+rLL7/U4MGDc2zjnL78XBfFmNu3b9eFCxec+tSsWVOVK1cu8OeWZsUxz7lJT0+XJJUtW9apfeHChQoLC1OdOnU0btw4nT17tsg+s7Qpzrk+cOCAIiMjVbVqVfXp00cpKSmObdfbOS2VzHl9/vx5vfvuuxo0aJBsNpvTtuvlvC6KeT579qwuXLjg+NtQkv+m9rDcE9e948ePKzs7W+XKlXNqL1eunH766adc9zly5Eiu/Y8cOeLYfqntcn0iIiKctnt4eKhs2bKOPtea4pjrfzp37pzGjh2rXr16KTAw0NE+YsQINWrUSGXLltWGDRs0btw4paam6sUXXyzkUZVOxTXXHTt21B133KEqVaooOTlZ/+///T916tRJGzdulLu7+3V3XpfEOT1//nwFBATojjvucGrnnP7L5ea6KMY8cuSIvLy8cvwPUpf7nV3NimOe/8lut2vkyJFq0aKF6tSp42jv3bu3oqOjFRkZqR9//FFjx45VUlKSPv744yL53NKmuOa6adOmmjdvnmrUqKHU1FRNnjxZrVq10p49exQQEHDdndNSyZzXn376qdLS0jRgwACn9uvpvC6KeR47dqwiIyMdgbEk/01NgASuQxcuXFCPHj1kjNFrr73mtG3UqFGOn+vVqycvLy/dd999mjZtmry9vUu61KvWPffc4/i5bt26qlevnm644QatWbNGbdu2dWFl1663335bffr0kY+Pj1M75zSuVsOGDdOePXu0bt06p/ahQ4c6fq5bt64qVKigtm3bKjk5WTfccENJl3nV6tSpk+PnevXqqWnTpoqOjtbixYtzvZIBReO///2vOnXqpMjISKd2zmvrpk+frkWLFmnNmjU5/ptXEriEFZaFhYXJ3d09x93Hjh49qvLly+e6T/ny5S/b/9L/vVKfY8eOOW2/ePGiTp48mefnXu2KY64vuRQef/31V61YscJp9TE3TZs21cWLF3Xo0KH8H8hVoDjn+u+qVq2qsLAw/fzzz44xrqfzurjn+fvvv1dSUpLuvffeK9bCOV08Y5YvX17nz59XWlpakX1uaVYc8/x3w4cP1xdffKHVq1erUqVKl+3btGlTSXL8fbnWFPdcXxIcHKzq1as7/Z2+ns5pqfjn+tdff9XKlSst/62Wrs3zujDz/MILL2j69On65ptvVK9ePUd7Sf6bmgAJy7y8vBQfH69Vq1Y52ux2u1atWqVmzZrluk+zZs2c+kvSihUrHP2rVKmi8uXLO/XJyMjQ5s2bHX2aNWumtLQ0bd++3dHn22+/ld1ud/xxudYUx1xL/xceDxw4oJUrVyo0NPSKtezcuVNubm45Lnm4VhTXXP/T//73P504cUIVKlRwjHE9ndfFPc///e9/FR8fr/r161+xFs7p4hkzPj5enp6eTn2SkpKUkpJS4M8tzYpjnqW/bsM/fPhwffLJJ/r2229VpUqVK+6zc+dOSXL8fbnWFNdc/1NmZqaSk5Md83i9ndNS8c/13LlzFRERoX/9619X7Hstn9cFnefnnntOU6dO1bJly5y+xyiV8L+pLd9uBzB/3XLY29vbzJs3z+zbt88MHTrUBAcHmyNHjhhjjOnbt695/PHHHf3Xr19vPDw8zAsvvGASExPNxIkTc32MR3BwsPnss8/Mjz/+aLp27ZrrLYcbNmxoNm/ebNatW2diY2Ov6ccdGFP0c33+/Hlz++23m0qVKpmdO3c63SY7KyvLGGPMhg0bzEsvvWR27txpkpOTzbvvvmvCw8NNv379Sn4CSlBRz/Xp06fNY489ZjZu3GgOHjxoVq5caRo1amRiY2PNuXPnHONcb+d1cfz9MMaY9PR0U6ZMGfPaa6/l+EzOaWtznZWVZXbs2GF27NhhKlSoYB577DGzY8cOc+DAActjGvPXIw8qV65svv32W7Nt2zbTrFkz06xZs5I78BJWHPP8wAMPmKCgILNmzRqnv9Nnz541xhjz888/mylTppht27aZgwcPms8++8xUrVrV3HzzzSV78CWsOOb60UcfNWvWrDEHDx4069evN+3atTNhYWHm2LFjjj7X2zltTPHMtTF/3WW0cuXKZuzYsTk+83o8r/M7z9OnTzdeXl5myZIlTn8bTp8+7dSnJP5NTYBEvr3yyiumcuXKxsvLyzRp0sRs2rTJsS0hIcH079/fqf/ixYtN9erVjZeXl6ldu7b58ssvnbbb7XYzfvx4U65cOePt7W3atm1rkpKSnPqcOHHC9OrVy/j7+5vAwEAzcOBAp/+HuVYV5VwfPHjQSMr1dek5TNu3bzdNmzY1QUFBxsfHx8TFxZlnnnnGKfRcq4pyrs+ePWvat29vwsPDjaenp4mOjjZDhgxx+oe2MdfneV3Ufz+MMeaNN94wvr6+Ji0tLcc2zmlrc53X34eEhATLYxpjzJ9//mkefPBBExISYsqUKWO6d+9uUlNTi/MwXa6o5zmvv9Nz5841xhiTkpJibr75ZlO2bFnj7e1tqlWrZkaPHn1NPy/vkqKe6549e5oKFSoYLy8vU7FiRdOzZ0/z888/O33m9XhOG1M8fz+WL19uJOX4N54x1+95nZ95jo6OznWeJ06c6OhTUv+mthljjPX1SgAAAADA9YrvQAIAAAAALCFAAgAAAAAsIUACAAAAACwhQAIAAAAALCFAAgAAAAAsIUACAAAAACwhQAIAAAAALCFAAgAAAAAsIUACAAAAACwhQAIAUMQGDBigbt26ubqMXB06dEg2m007d+50dSkAgKsQARIAgOvE+fPnXV0CAOAqR4AEAKAYtW7dWg899JBGjhypkJAQlStXTnPmzNGZM2c0cOBABQQEqFq1avr6668d+6xZs0Y2m01ffvml6tWrJx8fH910003as2eP09gfffSRateuLW9vb8XExGjGjBlO22NiYjR16lT169dPgYGBGjp0qKpUqSJJatiwoWw2m1q3bi1J2rp1q2699VaFhYUpKChICQkJ+uGHH5zGs9lseuutt9S9e3eVKVNGsbGx+vzzz5367N27V507d1ZgYKACAgLUqlUrJScnO7a/9dZbiouLk4+Pj2rWrKnZs2cXeo4BACWHAAkAQDGbP3++wsLCtGXLFj300EN64IEHdPfdd6t58+b64Ycf1L59e/Xt21dnz5512m/06NGaMWOGtm7dqvDwcHXp0kUXLlyQJG3fvl09evTQPffco927d2vSpEkaP3685s2b5zTGCy+8oPr162vHjh0aP368tmzZIklauXKlUlNT9fHHH0uSTp8+rf79+2vdunXatGmTYmNjddttt+n06dNO402ePFk9evTQjz/+qNtuu019+vTRyZMnJUm//fabbr75Znl7e+vbb7/V9u3bNWjQIF28eFGStHDhQk2YMEFPP/20EhMT9cwzz2j8+PGaP39+kc85AKCYGAAAUKT69+9vunbtaowxJiEhwbRs2dKx7eLFi8bPz8/07dvX0ZaammokmY0bNxpjjFm9erWRZBYtWuToc+LECePr62s++OADY4wxvXv3NrfeeqvT544ePdrUqlXL8T46Otp069bNqc/BgweNJLNjx47LHkN2drYJCAgwS5cudbRJMk8++aTjfWZmppFkvv76a2OMMePGjTNVqlQx58+fz3XMG264wbz33ntObVOnTjXNmjW7bC0AgNKDFUgAAIpZvXr1HD+7u7srNDRUdevWdbSVK1dOknTs2DGn/Zo1a+b4uWzZsqpRo4YSExMlSYmJiWrRooVT/xYtWujAgQPKzs52tDVu3NhSjUePHtWQIUMUGxuroKAgBQYGKjMzUykpKXkei5+fnwIDAx1179y5U61atZKnp2eO8c+cOaPk5GQNHjxY/v7+jtdTTz3ldIkrAKB083B1AQAAXOv+GahsNptTm81mkyTZ7fYi/2w/Pz9L/fr3768TJ05o1qxZio6Olre3t5o1a5bjxju5Hculun19ffMcPzMzU5I0Z84cNW3a1Gmbu7u7pRoBAK5HgAQAoJTatGmTKleuLEk6deqU9u/fr7i4OElSXFyc1q9f79R//fr1ql69+mUDmZeXlyQ5rVJe2nf27Nm67bbbJEmHDx/W8ePH81VvvXr1NH/+fF24cCFH0CxXrpwiIyP1yy+/qE+fPvkaFwBQehAgAQAopaZMmaLQ0FCVK1dOTzzxhMLCwhzPl3z00Ud14403aurUqerZs6c2btyoV1999Yp3NY2IiJCvr6+WLVumSpUqycfHR0FBQYqNjdU777yjxo0bKyMjQ6NHj77simJuhg8frldeeUX33HOPxo0bp6CgIG3atElNmjRRjRo1NHnyZI0YMUJBQUHq2LGjsrKytG3bNp06dUqjRo0q6DQBAEoQ34EEAKCUmj59uh5++GHFx8fryJEjWrp0qWMFsVGjRlq8eLEWLVqkOnXqaMKECZoyZYoGDBhw2TE9PDz08ssv64033lBkZKS6du0qSfrvf/+rU6dOqVGjRurbt69GjBihiIiIfNUbGhqqb7/9VpmZmUpISFB8fLzmzJnjWI2899579dZbb2nu3LmqW7euEhISNG/ePMejRQAApZ/NGGNcXQQAAPg/a9as0S233KJTp04pODjY1eUAAODACiQAAAAAwBICJAAAAADAEi5hBQAAAABYwgokAAAAAMASAiQAAAAAwBICJAAAAADAEgIkAAAAAMASAiQAAAAAwBICJAAAAADAEgIkAAAAAMASAiQAAAAAwJL/D4P2+c4HGNbuAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# calculate the feature importance using permutation importance\n",
    "plot_feature_importance(model, X_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Hyperparameter tuning\n",
    "\n",
    "We will use RandomizedSearchCV for hyperparameter tuning. This method randomly selects combinations of hyperparameters from a defined grid, fits the model for each, and identifies the best performing set. It's more efficient than GridSearchCV, which tries all combinations. The best parameters will be used to fit our model on the training data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 100 candidates, totalling 500 fits\n",
      "[CV] END classifier__max_depth=None, classifier__max_features=sqrt, classifier__min_samples_leaf=3, classifier__min_samples_split=None, classifier__n_estimators=100; total time=   0.1s\n",
      "[CV] END classifier__max_depth=None, classifier__max_features=sqrt, classifier__min_samples_leaf=3, classifier__min_samples_split=None, classifier__n_estimators=100; total time=   0.1s\n",
      "[CV] END classifier__max_depth=None, classifier__max_features=sqrt, classifier__min_samples_leaf=3, classifier__min_samples_split=None, classifier__n_estimators=100; total time=   0.1s\n",
      "[CV] END classifier__max_depth=None, classifier__max_features=sqrt, classifier__min_samples_leaf=3, classifier__min_samples_split=None, classifier__n_estimators=100; total time=   0.1s\n",
      "[CV] END classifier__max_depth=None, classifier__max_features=sqrt, classifier__min_samples_leaf=3, classifier__min_samples_split=None, classifier__n_estimators=100; total time=   0.1s\n",
      "[CV] END classifier__max_depth=None, classifier__max_features=sqrt, classifier__min_samples_leaf=2, classifier__min_samples_split=3, classifier__n_estimators=100; total time=   1.5s\n",
      "[CV] END classifier__max_depth=None, classifier__max_features=sqrt, classifier__min_samples_leaf=2, classifier__min_samples_split=3, classifier__n_estimators=100; total time=   1.4s\n",
      "[CV] END classifier__max_depth=None, classifier__max_features=sqrt, classifier__min_samples_leaf=2, classifier__min_samples_split=3, classifier__n_estimators=100; total time=   1.3s\n",
      "[CV] END classifier__max_depth=None, classifier__max_features=sqrt, classifier__min_samples_leaf=2, classifier__min_samples_split=3, classifier__n_estimators=100; total time=   1.3s\n",
      "[CV] END classifier__max_depth=None, classifier__max_features=auto, classifier__min_samples_leaf=2, classifier__min_samples_split=None, classifier__n_estimators=100; total time=   0.1s\n",
      "[CV] END classifier__max_depth=None, classifier__max_features=sqrt, classifier__min_samples_leaf=2, classifier__min_samples_split=3, classifier__n_estimators=100; total time=   1.5s\n",
      "[CV] END classifier__max_depth=None, classifier__max_features=auto, classifier__min_samples_leaf=2, classifier__min_samples_split=None, classifier__n_estimators=100; total time=   0.1s\n",
      "[CV] END classifier__max_depth=None, classifier__max_features=auto, classifier__min_samples_leaf=2, classifier__min_samples_split=None, classifier__n_estimators=100; total time=   0.1s\n",
      "[CV] END classifier__max_depth=None, classifier__max_features=auto, classifier__min_samples_leaf=2, classifier__min_samples_split=None, classifier__n_estimators=100; total time=   0.1s\n",
      "[CV] END classifier__max_depth=None, classifier__max_features=auto, classifier__min_samples_leaf=2, classifier__min_samples_split=None, classifier__n_estimators=100; total time=   0.1s\n",
      "[CV] END classifier__max_depth=5, classifier__max_features=auto, classifier__min_samples_leaf=2, classifier__min_samples_split=None, classifier__n_estimators=100; total time=   0.1s\n",
      "[CV] END classifier__max_depth=20, classifier__max_features=sqrt, classifier__min_samples_leaf=1, classifier__min_samples_split=3, classifier__n_estimators=100; total time=   1.4s\n",
      "[CV] END classifier__max_depth=5, classifier__max_features=auto, classifier__min_samples_leaf=2, classifier__min_samples_split=None, classifier__n_estimators=100; total time=   0.1s\n",
      "[CV] END classifier__max_depth=20, classifier__max_features=sqrt, classifier__min_samples_leaf=1, classifier__min_samples_split=3, classifier__n_estimators=100; total time=   1.5s\n",
      "[CV] END classifier__max_depth=5, classifier__max_features=auto, classifier__min_samples_leaf=2, classifier__min_samples_split=None, classifier__n_estimators=100; total time=   0.1s\n",
      "[CV] END classifier__max_depth=20, classifier__max_features=sqrt, classifier__min_samples_leaf=1, classifier__min_samples_split=3, classifier__n_estimators=100; total time=   1.5s\n",
      "[CV] END classifier__max_depth=5, classifier__max_features=auto, classifier__min_samples_leaf=2, classifier__min_samples_split=None, classifier__n_estimators=100; total time=   0.1s\n",
      "[CV] END classifier__max_depth=20, classifier__max_features=sqrt, classifier__min_samples_leaf=1, classifier__min_samples_split=3, classifier__n_estimators=100; total time=   1.4s\n",
      "[CV] END classifier__max_depth=5, classifier__max_features=auto, classifier__min_samples_leaf=2, classifier__min_samples_split=None, classifier__n_estimators=100; total time=   0.1s\n",
      "[CV] END classifier__max_depth=20, classifier__max_features=sqrt, classifier__min_samples_leaf=1, classifier__min_samples_split=3, classifier__n_estimators=100; total time=   1.4s\n",
      "[CV] END classifier__max_depth=20, classifier__max_features=auto, classifier__min_samples_leaf=1, classifier__min_samples_split=3, classifier__n_estimators=100; total time=   0.1s\n",
      "[CV] END classifier__max_depth=20, classifier__max_features=auto, classifier__min_samples_leaf=1, classifier__min_samples_split=3, classifier__n_estimators=100; total time=   0.1s\n",
      "[CV] END classifier__max_depth=20, classifier__max_features=auto, classifier__min_samples_leaf=1, classifier__min_samples_split=3, classifier__n_estimators=100; total time=   0.1s\n",
      "[CV] END classifier__max_depth=None, classifier__max_features=auto, classifier__min_samples_leaf=3, classifier__min_samples_split=2, classifier__n_estimators=300; total time=   0.1s\n",
      "[CV] END classifier__max_depth=20, classifier__max_features=auto, classifier__min_samples_leaf=1, classifier__min_samples_split=3, classifier__n_estimators=100; total time=   0.1s\n",
      "[CV] END classifier__max_depth=None, classifier__max_features=auto, classifier__min_samples_leaf=3, classifier__min_samples_split=2, classifier__n_estimators=300; total time=   0.1s\n",
      "[CV] END classifier__max_depth=20, classifier__max_features=auto, classifier__min_samples_leaf=1, classifier__min_samples_split=3, classifier__n_estimators=100; total time=   0.1s\n",
      "[CV] END classifier__max_depth=None, classifier__max_features=auto, classifier__min_samples_leaf=3, classifier__min_samples_split=2, classifier__n_estimators=300; total time=   0.1s\n",
      "[CV] END classifier__max_depth=None, classifier__max_features=auto, classifier__min_samples_leaf=3, classifier__min_samples_split=2, classifier__n_estimators=300; total time=   0.1s\n",
      "[CV] END classifier__max_depth=10, classifier__max_features=auto, classifier__min_samples_leaf=3, classifier__min_samples_split=2, classifier__n_estimators=200; total time=   0.1s\n",
      "[CV] END classifier__max_depth=None, classifier__max_features=auto, classifier__min_samples_leaf=3, classifier__min_samples_split=2, classifier__n_estimators=300; total time=   0.1s\n",
      "[CV] END classifier__max_depth=10, classifier__max_features=auto, classifier__min_samples_leaf=3, classifier__min_samples_split=2, classifier__n_estimators=200; total time=   0.1s\n",
      "[CV] END classifier__max_depth=10, classifier__max_features=auto, classifier__min_samples_leaf=3, classifier__min_samples_split=2, classifier__n_estimators=200; total time=   0.1s\n",
      "[CV] END classifier__max_depth=10, classifier__max_features=auto, classifier__min_samples_leaf=3, classifier__min_samples_split=2, classifier__n_estimators=200; total time=   0.1s\n",
      "[CV] END classifier__max_depth=10, classifier__max_features=auto, classifier__min_samples_leaf=3, classifier__min_samples_split=2, classifier__n_estimators=200; total time=   0.1s\n",
      "[CV] END classifier__max_depth=None, classifier__max_features=sqrt, classifier__min_samples_leaf=2, classifier__min_samples_split=None, classifier__n_estimators=200; total time=   0.1s\n",
      "[CV] END classifier__max_depth=None, classifier__max_features=sqrt, classifier__min_samples_leaf=2, classifier__min_samples_split=None, classifier__n_estimators=200; total time=   0.1s\n",
      "[CV] END classifier__max_depth=None, classifier__max_features=sqrt, classifier__min_samples_leaf=2, classifier__min_samples_split=None, classifier__n_estimators=200; total time=   0.1s\n",
      "[CV] END classifier__max_depth=None, classifier__max_features=sqrt, classifier__min_samples_leaf=2, classifier__min_samples_split=None, classifier__n_estimators=200; total time=   0.1s\n",
      "[CV] END classifier__max_depth=None, classifier__max_features=sqrt, classifier__min_samples_leaf=2, classifier__min_samples_split=None, classifier__n_estimators=200; total time=   0.1s\n",
      "[CV] END classifier__max_depth=20, classifier__max_features=sqrt, classifier__min_samples_leaf=3, classifier__min_samples_split=None, classifier__n_estimators=100; total time=   0.1s\n",
      "[CV] END classifier__max_depth=20, classifier__max_features=sqrt, classifier__min_samples_leaf=3, classifier__min_samples_split=None, classifier__n_estimators=100; total time=   0.1s\n",
      "[CV] END classifier__max_depth=20, classifier__max_features=sqrt, classifier__min_samples_leaf=3, classifier__min_samples_split=None, classifier__n_estimators=100; total time=   0.1s\n",
      "[CV] END classifier__max_depth=20, classifier__max_features=sqrt, classifier__min_samples_leaf=3, classifier__min_samples_split=None, classifier__n_estimators=100; total time=   0.1s\n",
      "[CV] END classifier__max_depth=20, classifier__max_features=sqrt, classifier__min_samples_leaf=3, classifier__min_samples_split=None, classifier__n_estimators=100; total time=   0.1s\n",
      "[CV] END classifier__max_depth=5, classifier__max_features=sqrt, classifier__min_samples_leaf=1, classifier__min_samples_split=3, classifier__n_estimators=100; total time=   0.7s\n",
      "[CV] END classifier__max_depth=5, classifier__max_features=sqrt, classifier__min_samples_leaf=1, classifier__min_samples_split=3, classifier__n_estimators=100; total time=   0.8s\n",
      "[CV] END classifier__max_depth=5, classifier__max_features=sqrt, classifier__min_samples_leaf=1, classifier__min_samples_split=3, classifier__n_estimators=100; total time=   0.7s\n",
      "[CV] END classifier__max_depth=5, classifier__max_features=sqrt, classifier__min_samples_leaf=1, classifier__min_samples_split=3, classifier__n_estimators=100; total time=   0.7s\n",
      "[CV] END classifier__max_depth=5, classifier__max_features=sqrt, classifier__min_samples_leaf=1, classifier__min_samples_split=3, classifier__n_estimators=100; total time=   0.8s\n",
      "[CV] END classifier__max_depth=5, classifier__max_features=auto, classifier__min_samples_leaf=2, classifier__min_samples_split=2, classifier__n_estimators=100; total time=   0.1s\n",
      "[CV] END classifier__max_depth=20, classifier__max_features=sqrt, classifier__min_samples_leaf=1, classifier__min_samples_split=3, classifier__n_estimators=200; total time=   2.7s\n",
      "[CV] END classifier__max_depth=5, classifier__max_features=auto, classifier__min_samples_leaf=2, classifier__min_samples_split=2, classifier__n_estimators=100; total time=   0.1s\n",
      "[CV] END classifier__max_depth=5, classifier__max_features=auto, classifier__min_samples_leaf=2, classifier__min_samples_split=2, classifier__n_estimators=100; total time=   0.1s\n",
      "[CV] END classifier__max_depth=20, classifier__max_features=sqrt, classifier__min_samples_leaf=1, classifier__min_samples_split=3, classifier__n_estimators=200; total time=   2.8s\n",
      "[CV] END classifier__max_depth=20, classifier__max_features=sqrt, classifier__min_samples_leaf=1, classifier__min_samples_split=3, classifier__n_estimators=200; total time=   2.7s\n",
      "[CV] END classifier__max_depth=5, classifier__max_features=auto, classifier__min_samples_leaf=2, classifier__min_samples_split=2, classifier__n_estimators=100; total time=   0.1s\n",
      "[CV] END classifier__max_depth=5, classifier__max_features=auto, classifier__min_samples_leaf=2, classifier__min_samples_split=2, classifier__n_estimators=100; total time=   0.1s\n",
      "[CV] END classifier__max_depth=10, classifier__max_features=auto, classifier__min_samples_leaf=2, classifier__min_samples_split=3, classifier__n_estimators=200; total time=   0.1s\n",
      "[CV] END classifier__max_depth=20, classifier__max_features=sqrt, classifier__min_samples_leaf=1, classifier__min_samples_split=3, classifier__n_estimators=200; total time=   2.8s\n",
      "[CV] END classifier__max_depth=10, classifier__max_features=auto, classifier__min_samples_leaf=2, classifier__min_samples_split=3, classifier__n_estimators=200; total time=   0.1s\n",
      "[CV] END classifier__max_depth=10, classifier__max_features=auto, classifier__min_samples_leaf=2, classifier__min_samples_split=3, classifier__n_estimators=200; total time=   0.1s\n",
      "[CV] END classifier__max_depth=20, classifier__max_features=sqrt, classifier__min_samples_leaf=1, classifier__min_samples_split=3, classifier__n_estimators=200; total time=   3.1s\n",
      "[CV] END classifier__max_depth=10, classifier__max_features=auto, classifier__min_samples_leaf=2, classifier__min_samples_split=3, classifier__n_estimators=200; total time=   0.1s\n",
      "[CV] END classifier__max_depth=10, classifier__max_features=auto, classifier__min_samples_leaf=2, classifier__min_samples_split=3, classifier__n_estimators=200; total time=   0.1s\n",
      "[CV] END classifier__max_depth=None, classifier__max_features=auto, classifier__min_samples_leaf=2, classifier__min_samples_split=3, classifier__n_estimators=300; total time=   0.1s\n",
      "[CV] END classifier__max_depth=None, classifier__max_features=auto, classifier__min_samples_leaf=2, classifier__min_samples_split=3, classifier__n_estimators=300; total time=   0.1s\n",
      "[CV] END classifier__max_depth=None, classifier__max_features=auto, classifier__min_samples_leaf=2, classifier__min_samples_split=3, classifier__n_estimators=300; total time=   0.1s\n",
      "[CV] END classifier__max_depth=None, classifier__max_features=auto, classifier__min_samples_leaf=2, classifier__min_samples_split=3, classifier__n_estimators=300; total time=   0.1s\n",
      "[CV] END classifier__max_depth=None, classifier__max_features=auto, classifier__min_samples_leaf=2, classifier__min_samples_split=3, classifier__n_estimators=300; total time=   0.1s\n",
      "[CV] END classifier__max_depth=None, classifier__max_features=auto, classifier__min_samples_leaf=2, classifier__min_samples_split=2, classifier__n_estimators=200; total time=   0.1s\n",
      "[CV] END classifier__max_depth=None, classifier__max_features=auto, classifier__min_samples_leaf=2, classifier__min_samples_split=2, classifier__n_estimators=200; total time=   0.1s\n",
      "[CV] END classifier__max_depth=None, classifier__max_features=auto, classifier__min_samples_leaf=2, classifier__min_samples_split=2, classifier__n_estimators=200; total time=   0.1s\n",
      "[CV] END classifier__max_depth=None, classifier__max_features=sqrt, classifier__min_samples_leaf=1, classifier__min_samples_split=2, classifier__n_estimators=200; total time=   2.9s\n",
      "[CV] END classifier__max_depth=None, classifier__max_features=sqrt, classifier__min_samples_leaf=1, classifier__min_samples_split=2, classifier__n_estimators=200; total time=   2.6s\n",
      "[CV] END classifier__max_depth=None, classifier__max_features=auto, classifier__min_samples_leaf=2, classifier__min_samples_split=2, classifier__n_estimators=200; total time=   0.1s\n",
      "[CV] END classifier__max_depth=None, classifier__max_features=auto, classifier__min_samples_leaf=2, classifier__min_samples_split=2, classifier__n_estimators=200; total time=   0.1s\n",
      "[CV] END classifier__max_depth=None, classifier__max_features=sqrt, classifier__min_samples_leaf=1, classifier__min_samples_split=2, classifier__n_estimators=200; total time=   3.0s\n",
      "[CV] END classifier__max_depth=5, classifier__max_features=auto, classifier__min_samples_leaf=3, classifier__min_samples_split=2, classifier__n_estimators=100; total time=   0.1s\n",
      "[CV] END classifier__max_depth=None, classifier__max_features=sqrt, classifier__min_samples_leaf=1, classifier__min_samples_split=2, classifier__n_estimators=200; total time=   2.6s\n",
      "[CV] END classifier__max_depth=None, classifier__max_features=sqrt, classifier__min_samples_leaf=1, classifier__min_samples_split=2, classifier__n_estimators=200; total time=   2.9s\n",
      "[CV] END classifier__max_depth=5, classifier__max_features=auto, classifier__min_samples_leaf=3, classifier__min_samples_split=2, classifier__n_estimators=100; total time=   0.2s\n",
      "[CV] END classifier__max_depth=5, classifier__max_features=auto, classifier__min_samples_leaf=3, classifier__min_samples_split=2, classifier__n_estimators=100; total time=   0.1s\n",
      "[CV] END classifier__max_depth=5, classifier__max_features=auto, classifier__min_samples_leaf=3, classifier__min_samples_split=2, classifier__n_estimators=100; total time=   0.1s\n",
      "[CV] END classifier__max_depth=5, classifier__max_features=auto, classifier__min_samples_leaf=3, classifier__min_samples_split=2, classifier__n_estimators=100; total time=   0.1s\n",
      "[CV] END classifier__max_depth=10, classifier__max_features=auto, classifier__min_samples_leaf=2, classifier__min_samples_split=3, classifier__n_estimators=100; total time=   0.1s\n",
      "[CV] END classifier__max_depth=10, classifier__max_features=auto, classifier__min_samples_leaf=2, classifier__min_samples_split=3, classifier__n_estimators=100; total time=   0.1s\n",
      "[CV] END classifier__max_depth=10, classifier__max_features=auto, classifier__min_samples_leaf=2, classifier__min_samples_split=3, classifier__n_estimators=100; total time=   0.1s\n",
      "[CV] END classifier__max_depth=10, classifier__max_features=auto, classifier__min_samples_leaf=2, classifier__min_samples_split=3, classifier__n_estimators=100; total time=   0.1s\n",
      "[CV] END classifier__max_depth=10, classifier__max_features=auto, classifier__min_samples_leaf=2, classifier__min_samples_split=3, classifier__n_estimators=100; total time=   0.1s\n",
      "[CV] END classifier__max_depth=None, classifier__max_features=auto, classifier__min_samples_leaf=3, classifier__min_samples_split=None, classifier__n_estimators=100; total time=   0.1s\n",
      "[CV] END classifier__max_depth=None, classifier__max_features=auto, classifier__min_samples_leaf=3, classifier__min_samples_split=None, classifier__n_estimators=100; total time=   0.1s\n",
      "[CV] END classifier__max_depth=None, classifier__max_features=auto, classifier__min_samples_leaf=3, classifier__min_samples_split=None, classifier__n_estimators=100; total time=   0.1s\n",
      "[CV] END classifier__max_depth=None, classifier__max_features=auto, classifier__min_samples_leaf=3, classifier__min_samples_split=None, classifier__n_estimators=100; total time=   0.1s\n",
      "[CV] END classifier__max_depth=None, classifier__max_features=auto, classifier__min_samples_leaf=3, classifier__min_samples_split=None, classifier__n_estimators=100; total time=   0.1s\n",
      "[CV] END classifier__max_depth=20, classifier__max_features=sqrt, classifier__min_samples_leaf=1, classifier__min_samples_split=None, classifier__n_estimators=200; total time=   0.1s\n",
      "[CV] END classifier__max_depth=20, classifier__max_features=sqrt, classifier__min_samples_leaf=3, classifier__min_samples_split=2, classifier__n_estimators=200; total time=   2.4s\n",
      "[CV] END classifier__max_depth=20, classifier__max_features=sqrt, classifier__min_samples_leaf=3, classifier__min_samples_split=2, classifier__n_estimators=200; total time=   2.3s\n",
      "[CV] END classifier__max_depth=20, classifier__max_features=sqrt, classifier__min_samples_leaf=1, classifier__min_samples_split=None, classifier__n_estimators=200; total time=   0.1s\n",
      "[CV] END classifier__max_depth=20, classifier__max_features=sqrt, classifier__min_samples_leaf=3, classifier__min_samples_split=2, classifier__n_estimators=200; total time=   2.2s\n",
      "[CV] END classifier__max_depth=20, classifier__max_features=sqrt, classifier__min_samples_leaf=1, classifier__min_samples_split=None, classifier__n_estimators=200; total time=   0.1s\n",
      "[CV] END classifier__max_depth=20, classifier__max_features=sqrt, classifier__min_samples_leaf=1, classifier__min_samples_split=None, classifier__n_estimators=200; total time=   0.1s\n",
      "[CV] END classifier__max_depth=20, classifier__max_features=sqrt, classifier__min_samples_leaf=1, classifier__min_samples_split=None, classifier__n_estimators=200; total time=   0.1s\n",
      "[CV] END classifier__max_depth=20, classifier__max_features=sqrt, classifier__min_samples_leaf=3, classifier__min_samples_split=2, classifier__n_estimators=200; total time=   2.4s\n",
      "[CV] END classifier__max_depth=20, classifier__max_features=sqrt, classifier__min_samples_leaf=3, classifier__min_samples_split=2, classifier__n_estimators=200; total time=   2.5s\n",
      "[CV] END classifier__max_depth=10, classifier__max_features=auto, classifier__min_samples_leaf=3, classifier__min_samples_split=None, classifier__n_estimators=200; total time=   0.1s\n",
      "[CV] END classifier__max_depth=10, classifier__max_features=auto, classifier__min_samples_leaf=3, classifier__min_samples_split=None, classifier__n_estimators=200; total time=   0.1s\n",
      "[CV] END classifier__max_depth=10, classifier__max_features=auto, classifier__min_samples_leaf=3, classifier__min_samples_split=None, classifier__n_estimators=200; total time=   0.1s\n",
      "[CV] END classifier__max_depth=10, classifier__max_features=auto, classifier__min_samples_leaf=3, classifier__min_samples_split=None, classifier__n_estimators=200; total time=   0.1s\n",
      "[CV] END classifier__max_depth=10, classifier__max_features=auto, classifier__min_samples_leaf=3, classifier__min_samples_split=None, classifier__n_estimators=200; total time=   0.1s\n",
      "[CV] END classifier__max_depth=5, classifier__max_features=auto, classifier__min_samples_leaf=3, classifier__min_samples_split=None, classifier__n_estimators=200; total time=   0.1s\n",
      "[CV] END classifier__max_depth=5, classifier__max_features=auto, classifier__min_samples_leaf=3, classifier__min_samples_split=None, classifier__n_estimators=200; total time=   0.1s\n",
      "[CV] END classifier__max_depth=5, classifier__max_features=auto, classifier__min_samples_leaf=3, classifier__min_samples_split=None, classifier__n_estimators=200; total time=   0.1s\n",
      "[CV] END classifier__max_depth=5, classifier__max_features=auto, classifier__min_samples_leaf=3, classifier__min_samples_split=None, classifier__n_estimators=200; total time=   0.1s\n",
      "[CV] END classifier__max_depth=5, classifier__max_features=auto, classifier__min_samples_leaf=3, classifier__min_samples_split=None, classifier__n_estimators=200; total time=   0.1s\n",
      "[CV] END classifier__max_depth=20, classifier__max_features=sqrt, classifier__min_samples_leaf=3, classifier__min_samples_split=None, classifier__n_estimators=300; total time=   0.1s\n",
      "[CV] END classifier__max_depth=20, classifier__max_features=sqrt, classifier__min_samples_leaf=3, classifier__min_samples_split=None, classifier__n_estimators=300; total time=   0.1s\n",
      "[CV] END classifier__max_depth=20, classifier__max_features=sqrt, classifier__min_samples_leaf=3, classifier__min_samples_split=None, classifier__n_estimators=300; total time=   0.1s\n",
      "[CV] END classifier__max_depth=20, classifier__max_features=sqrt, classifier__min_samples_leaf=3, classifier__min_samples_split=None, classifier__n_estimators=300; total time=   0.1s\n",
      "[CV] END classifier__max_depth=20, classifier__max_features=sqrt, classifier__min_samples_leaf=3, classifier__min_samples_split=None, classifier__n_estimators=300; total time=   0.1s\n",
      "[CV] END classifier__max_depth=5, classifier__max_features=auto, classifier__min_samples_leaf=2, classifier__min_samples_split=None, classifier__n_estimators=200; total time=   0.1s\n",
      "[CV] END classifier__max_depth=5, classifier__max_features=auto, classifier__min_samples_leaf=2, classifier__min_samples_split=None, classifier__n_estimators=200; total time=   0.1s\n",
      "[CV] END classifier__max_depth=5, classifier__max_features=auto, classifier__min_samples_leaf=2, classifier__min_samples_split=None, classifier__n_estimators=200; total time=   0.1s\n",
      "[CV] END classifier__max_depth=5, classifier__max_features=auto, classifier__min_samples_leaf=2, classifier__min_samples_split=None, classifier__n_estimators=200; total time=   0.1s\n",
      "[CV] END classifier__max_depth=5, classifier__max_features=auto, classifier__min_samples_leaf=2, classifier__min_samples_split=None, classifier__n_estimators=200; total time=   0.1s\n",
      "[CV] END classifier__max_depth=10, classifier__max_features=sqrt, classifier__min_samples_leaf=3, classifier__min_samples_split=3, classifier__n_estimators=300; total time=   2.9s\n",
      "[CV] END classifier__max_depth=10, classifier__max_features=sqrt, classifier__min_samples_leaf=3, classifier__min_samples_split=3, classifier__n_estimators=300; total time=   2.9s\n",
      "[CV] END classifier__max_depth=10, classifier__max_features=sqrt, classifier__min_samples_leaf=3, classifier__min_samples_split=3, classifier__n_estimators=300; total time=   3.1s\n",
      "[CV] END classifier__max_depth=10, classifier__max_features=sqrt, classifier__min_samples_leaf=3, classifier__min_samples_split=3, classifier__n_estimators=300; total time=   3.0s\n",
      "[CV] END classifier__max_depth=10, classifier__max_features=sqrt, classifier__min_samples_leaf=3, classifier__min_samples_split=3, classifier__n_estimators=300; total time=   3.1s\n",
      "[CV] END classifier__max_depth=5, classifier__max_features=sqrt, classifier__min_samples_leaf=3, classifier__min_samples_split=2, classifier__n_estimators=100; total time=   0.8s\n",
      "[CV] END classifier__max_depth=5, classifier__max_features=sqrt, classifier__min_samples_leaf=3, classifier__min_samples_split=2, classifier__n_estimators=100; total time=   0.8s\n",
      "[CV] END classifier__max_depth=5, classifier__max_features=sqrt, classifier__min_samples_leaf=3, classifier__min_samples_split=2, classifier__n_estimators=100; total time=   0.7s\n",
      "[CV] END classifier__max_depth=5, classifier__max_features=sqrt, classifier__min_samples_leaf=3, classifier__min_samples_split=2, classifier__n_estimators=100; total time=   0.9s\n",
      "[CV] END classifier__max_depth=5, classifier__max_features=sqrt, classifier__min_samples_leaf=3, classifier__min_samples_split=2, classifier__n_estimators=100; total time=   0.8s\n",
      "[CV] END classifier__max_depth=20, classifier__max_features=auto, classifier__min_samples_leaf=1, classifier__min_samples_split=2, classifier__n_estimators=100; total time=   0.1s\n",
      "[CV] END classifier__max_depth=20, classifier__max_features=auto, classifier__min_samples_leaf=1, classifier__min_samples_split=2, classifier__n_estimators=100; total time=   0.1s\n",
      "[CV] END classifier__max_depth=20, classifier__max_features=sqrt, classifier__min_samples_leaf=2, classifier__min_samples_split=2, classifier__n_estimators=300; total time=   3.8s\n",
      "[CV] END classifier__max_depth=20, classifier__max_features=sqrt, classifier__min_samples_leaf=2, classifier__min_samples_split=2, classifier__n_estimators=300; total time=   3.8s\n",
      "[CV] END classifier__max_depth=20, classifier__max_features=auto, classifier__min_samples_leaf=1, classifier__min_samples_split=2, classifier__n_estimators=100; total time=   0.1s\n",
      "[CV] END classifier__max_depth=20, classifier__max_features=auto, classifier__min_samples_leaf=1, classifier__min_samples_split=2, classifier__n_estimators=100; total time=   0.1s\n",
      "[CV] END classifier__max_depth=20, classifier__max_features=sqrt, classifier__min_samples_leaf=2, classifier__min_samples_split=2, classifier__n_estimators=300; total time=   3.8s\n",
      "[CV] END classifier__max_depth=20, classifier__max_features=sqrt, classifier__min_samples_leaf=2, classifier__min_samples_split=2, classifier__n_estimators=300; total time=   3.7s\n",
      "[CV] END classifier__max_depth=20, classifier__max_features=auto, classifier__min_samples_leaf=1, classifier__min_samples_split=2, classifier__n_estimators=100; total time=   0.1s\n",
      "[CV] END classifier__max_depth=10, classifier__max_features=auto, classifier__min_samples_leaf=1, classifier__min_samples_split=2, classifier__n_estimators=200; total time=   0.1s\n",
      "[CV] END classifier__max_depth=20, classifier__max_features=sqrt, classifier__min_samples_leaf=2, classifier__min_samples_split=2, classifier__n_estimators=300; total time=   3.9s\n",
      "[CV] END classifier__max_depth=10, classifier__max_features=auto, classifier__min_samples_leaf=1, classifier__min_samples_split=2, classifier__n_estimators=200; total time=   0.1s\n",
      "[CV] END classifier__max_depth=10, classifier__max_features=auto, classifier__min_samples_leaf=1, classifier__min_samples_split=2, classifier__n_estimators=200; total time=   0.1s\n",
      "[CV] END classifier__max_depth=10, classifier__max_features=auto, classifier__min_samples_leaf=1, classifier__min_samples_split=2, classifier__n_estimators=200; total time=   0.1s\n",
      "[CV] END classifier__max_depth=10, classifier__max_features=auto, classifier__min_samples_leaf=1, classifier__min_samples_split=2, classifier__n_estimators=200; total time=   0.1s\n",
      "[CV] END classifier__max_depth=20, classifier__max_features=auto, classifier__min_samples_leaf=1, classifier__min_samples_split=3, classifier__n_estimators=200; total time=   0.1s\n",
      "[CV] END classifier__max_depth=20, classifier__max_features=auto, classifier__min_samples_leaf=1, classifier__min_samples_split=3, classifier__n_estimators=200; total time=   0.1s\n",
      "[CV] END classifier__max_depth=20, classifier__max_features=auto, classifier__min_samples_leaf=1, classifier__min_samples_split=3, classifier__n_estimators=200; total time=   0.1s\n",
      "[CV] END classifier__max_depth=20, classifier__max_features=auto, classifier__min_samples_leaf=1, classifier__min_samples_split=3, classifier__n_estimators=200; total time=   0.1s\n",
      "[CV] END classifier__max_depth=20, classifier__max_features=auto, classifier__min_samples_leaf=1, classifier__min_samples_split=3, classifier__n_estimators=200; total time=   0.1s\n",
      "[CV] END classifier__max_depth=None, classifier__max_features=sqrt, classifier__min_samples_leaf=2, classifier__min_samples_split=2, classifier__n_estimators=200; total time=   2.7s\n",
      "[CV] END classifier__max_depth=None, classifier__max_features=sqrt, classifier__min_samples_leaf=2, classifier__min_samples_split=2, classifier__n_estimators=200; total time=   2.5s\n",
      "[CV] END classifier__max_depth=None, classifier__max_features=sqrt, classifier__min_samples_leaf=2, classifier__min_samples_split=2, classifier__n_estimators=200; total time=   2.7s\n",
      "[CV] END classifier__max_depth=None, classifier__max_features=sqrt, classifier__min_samples_leaf=2, classifier__min_samples_split=2, classifier__n_estimators=200; total time=   2.5s\n",
      "[CV] END classifier__max_depth=None, classifier__max_features=sqrt, classifier__min_samples_leaf=2, classifier__min_samples_split=2, classifier__n_estimators=200; total time=   2.5s\n",
      "[CV] END classifier__max_depth=20, classifier__max_features=auto, classifier__min_samples_leaf=2, classifier__min_samples_split=None, classifier__n_estimators=200; total time=   0.1s\n",
      "[CV] END classifier__max_depth=20, classifier__max_features=auto, classifier__min_samples_leaf=2, classifier__min_samples_split=None, classifier__n_estimators=200; total time=   0.1s\n",
      "[CV] END classifier__max_depth=20, classifier__max_features=auto, classifier__min_samples_leaf=2, classifier__min_samples_split=None, classifier__n_estimators=200; total time=   0.1s\n",
      "[CV] END classifier__max_depth=20, classifier__max_features=auto, classifier__min_samples_leaf=2, classifier__min_samples_split=None, classifier__n_estimators=200; total time=   0.1s\n",
      "[CV] END classifier__max_depth=None, classifier__max_features=sqrt, classifier__min_samples_leaf=1, classifier__min_samples_split=3, classifier__n_estimators=100; total time=   1.3s\n",
      "[CV] END classifier__max_depth=20, classifier__max_features=auto, classifier__min_samples_leaf=2, classifier__min_samples_split=None, classifier__n_estimators=200; total time=   0.1s\n",
      "[CV] END classifier__max_depth=None, classifier__max_features=sqrt, classifier__min_samples_leaf=1, classifier__min_samples_split=3, classifier__n_estimators=100; total time=   1.4s\n",
      "[CV] END classifier__max_depth=None, classifier__max_features=sqrt, classifier__min_samples_leaf=1, classifier__min_samples_split=3, classifier__n_estimators=100; total time=   1.3s\n",
      "[CV] END classifier__max_depth=20, classifier__max_features=auto, classifier__min_samples_leaf=3, classifier__min_samples_split=2, classifier__n_estimators=300; total time=   0.1s\n",
      "[CV] END classifier__max_depth=20, classifier__max_features=auto, classifier__min_samples_leaf=3, classifier__min_samples_split=2, classifier__n_estimators=300; total time=   0.1s\n",
      "[CV] END classifier__max_depth=None, classifier__max_features=sqrt, classifier__min_samples_leaf=1, classifier__min_samples_split=3, classifier__n_estimators=100; total time=   1.3s\n",
      "[CV] END classifier__max_depth=20, classifier__max_features=auto, classifier__min_samples_leaf=3, classifier__min_samples_split=2, classifier__n_estimators=300; total time=   0.1s\n",
      "[CV] END classifier__max_depth=20, classifier__max_features=auto, classifier__min_samples_leaf=3, classifier__min_samples_split=2, classifier__n_estimators=300; total time=   0.1s\n",
      "[CV] END classifier__max_depth=None, classifier__max_features=sqrt, classifier__min_samples_leaf=1, classifier__min_samples_split=3, classifier__n_estimators=100; total time=   1.5s\n",
      "[CV] END classifier__max_depth=20, classifier__max_features=auto, classifier__min_samples_leaf=3, classifier__min_samples_split=2, classifier__n_estimators=300; total time=   0.1s\n",
      "[CV] END classifier__max_depth=10, classifier__max_features=auto, classifier__min_samples_leaf=3, classifier__min_samples_split=3, classifier__n_estimators=100; total time=   0.1s\n",
      "[CV] END classifier__max_depth=10, classifier__max_features=auto, classifier__min_samples_leaf=3, classifier__min_samples_split=3, classifier__n_estimators=100; total time=   0.1s\n",
      "[CV] END classifier__max_depth=10, classifier__max_features=auto, classifier__min_samples_leaf=3, classifier__min_samples_split=3, classifier__n_estimators=100; total time=   0.1s\n",
      "[CV] END classifier__max_depth=10, classifier__max_features=auto, classifier__min_samples_leaf=3, classifier__min_samples_split=3, classifier__n_estimators=100; total time=   0.1s\n",
      "[CV] END classifier__max_depth=10, classifier__max_features=auto, classifier__min_samples_leaf=3, classifier__min_samples_split=3, classifier__n_estimators=100; total time=   0.1s\n",
      "[CV] END classifier__max_depth=20, classifier__max_features=auto, classifier__min_samples_leaf=2, classifier__min_samples_split=2, classifier__n_estimators=200; total time=   0.1s\n",
      "[CV] END classifier__max_depth=20, classifier__max_features=auto, classifier__min_samples_leaf=2, classifier__min_samples_split=2, classifier__n_estimators=200; total time=   0.1s\n",
      "[CV] END classifier__max_depth=20, classifier__max_features=auto, classifier__min_samples_leaf=2, classifier__min_samples_split=2, classifier__n_estimators=200; total time=   0.1s\n",
      "[CV] END classifier__max_depth=20, classifier__max_features=auto, classifier__min_samples_leaf=2, classifier__min_samples_split=2, classifier__n_estimators=200; total time=   0.1s\n",
      "[CV] END classifier__max_depth=20, classifier__max_features=sqrt, classifier__min_samples_leaf=1, classifier__min_samples_split=2, classifier__n_estimators=100; total time=   1.3s\n",
      "[CV] END classifier__max_depth=20, classifier__max_features=auto, classifier__min_samples_leaf=2, classifier__min_samples_split=2, classifier__n_estimators=200; total time=   0.1s\n",
      "[CV] END classifier__max_depth=10, classifier__max_features=sqrt, classifier__min_samples_leaf=2, classifier__min_samples_split=None, classifier__n_estimators=300; total time=   0.1s\n",
      "[CV] END classifier__max_depth=20, classifier__max_features=sqrt, classifier__min_samples_leaf=1, classifier__min_samples_split=2, classifier__n_estimators=100; total time=   1.3s\n",
      "[CV] END classifier__max_depth=20, classifier__max_features=sqrt, classifier__min_samples_leaf=1, classifier__min_samples_split=2, classifier__n_estimators=100; total time=   1.3s\n",
      "[CV] END classifier__max_depth=10, classifier__max_features=sqrt, classifier__min_samples_leaf=2, classifier__min_samples_split=None, classifier__n_estimators=300; total time=   0.1s\n",
      "[CV] END classifier__max_depth=20, classifier__max_features=sqrt, classifier__min_samples_leaf=1, classifier__min_samples_split=2, classifier__n_estimators=100; total time=   1.3s\n",
      "[CV] END classifier__max_depth=10, classifier__max_features=sqrt, classifier__min_samples_leaf=2, classifier__min_samples_split=None, classifier__n_estimators=300; total time=   0.1s\n",
      "[CV] END classifier__max_depth=20, classifier__max_features=sqrt, classifier__min_samples_leaf=1, classifier__min_samples_split=2, classifier__n_estimators=100; total time=   1.3s\n",
      "[CV] END classifier__max_depth=10, classifier__max_features=sqrt, classifier__min_samples_leaf=2, classifier__min_samples_split=None, classifier__n_estimators=300; total time=   0.1s\n",
      "[CV] END classifier__max_depth=10, classifier__max_features=sqrt, classifier__min_samples_leaf=2, classifier__min_samples_split=None, classifier__n_estimators=300; total time=   0.1s\n",
      "[CV] END classifier__max_depth=10, classifier__max_features=sqrt, classifier__min_samples_leaf=2, classifier__min_samples_split=None, classifier__n_estimators=100; total time=   0.1s\n",
      "[CV] END classifier__max_depth=10, classifier__max_features=sqrt, classifier__min_samples_leaf=2, classifier__min_samples_split=None, classifier__n_estimators=100; total time=   0.1s\n",
      "[CV] END classifier__max_depth=10, classifier__max_features=sqrt, classifier__min_samples_leaf=2, classifier__min_samples_split=None, classifier__n_estimators=100; total time=   0.1s\n",
      "[CV] END classifier__max_depth=10, classifier__max_features=sqrt, classifier__min_samples_leaf=2, classifier__min_samples_split=None, classifier__n_estimators=100; total time=   0.1s\n",
      "[CV] END classifier__max_depth=10, classifier__max_features=sqrt, classifier__min_samples_leaf=2, classifier__min_samples_split=None, classifier__n_estimators=100; total time=   0.1s\n",
      "[CV] END classifier__max_depth=None, classifier__max_features=auto, classifier__min_samples_leaf=3, classifier__min_samples_split=3, classifier__n_estimators=200; total time=   0.1s\n",
      "[CV] END classifier__max_depth=None, classifier__max_features=auto, classifier__min_samples_leaf=3, classifier__min_samples_split=3, classifier__n_estimators=200; total time=   0.1s\n",
      "[CV] END classifier__max_depth=None, classifier__max_features=auto, classifier__min_samples_leaf=3, classifier__min_samples_split=3, classifier__n_estimators=200; total time=   0.1s\n",
      "[CV] END classifier__max_depth=None, classifier__max_features=auto, classifier__min_samples_leaf=3, classifier__min_samples_split=3, classifier__n_estimators=200; total time=   0.1s\n",
      "[CV] END classifier__max_depth=None, classifier__max_features=auto, classifier__min_samples_leaf=3, classifier__min_samples_split=3, classifier__n_estimators=200; total time=   0.1s\n",
      "[CV] END classifier__max_depth=20, classifier__max_features=auto, classifier__min_samples_leaf=2, classifier__min_samples_split=2, classifier__n_estimators=100; total time=   0.1s\n",
      "[CV] END classifier__max_depth=20, classifier__max_features=auto, classifier__min_samples_leaf=2, classifier__min_samples_split=2, classifier__n_estimators=100; total time=   0.1s\n",
      "[CV] END classifier__max_depth=20, classifier__max_features=auto, classifier__min_samples_leaf=2, classifier__min_samples_split=2, classifier__n_estimators=100; total time=   0.1s\n",
      "[CV] END classifier__max_depth=20, classifier__max_features=auto, classifier__min_samples_leaf=2, classifier__min_samples_split=2, classifier__n_estimators=100; total time=   0.1s\n",
      "[CV] END classifier__max_depth=20, classifier__max_features=auto, classifier__min_samples_leaf=2, classifier__min_samples_split=2, classifier__n_estimators=100; total time=   0.1s\n",
      "[CV] END classifier__max_depth=10, classifier__max_features=sqrt, classifier__min_samples_leaf=2, classifier__min_samples_split=3, classifier__n_estimators=100; total time=   1.1s\n",
      "[CV] END classifier__max_depth=10, classifier__max_features=sqrt, classifier__min_samples_leaf=2, classifier__min_samples_split=3, classifier__n_estimators=100; total time=   1.1s\n",
      "[CV] END classifier__max_depth=10, classifier__max_features=sqrt, classifier__min_samples_leaf=2, classifier__min_samples_split=3, classifier__n_estimators=100; total time=   1.0s\n",
      "[CV] END classifier__max_depth=10, classifier__max_features=sqrt, classifier__min_samples_leaf=2, classifier__min_samples_split=3, classifier__n_estimators=100; total time=   1.1s\n",
      "[CV] END classifier__max_depth=10, classifier__max_features=sqrt, classifier__min_samples_leaf=2, classifier__min_samples_split=3, classifier__n_estimators=100; total time=   1.1s\n",
      "[CV] END classifier__max_depth=10, classifier__max_features=sqrt, classifier__min_samples_leaf=1, classifier__min_samples_split=3, classifier__n_estimators=100; total time=   1.1s\n",
      "[CV] END classifier__max_depth=10, classifier__max_features=sqrt, classifier__min_samples_leaf=1, classifier__min_samples_split=3, classifier__n_estimators=100; total time=   1.1s\n",
      "[CV] END classifier__max_depth=10, classifier__max_features=sqrt, classifier__min_samples_leaf=1, classifier__min_samples_split=3, classifier__n_estimators=100; total time=   1.2s\n",
      "[CV] END classifier__max_depth=10, classifier__max_features=sqrt, classifier__min_samples_leaf=1, classifier__min_samples_split=3, classifier__n_estimators=100; total time=   1.1s\n",
      "[CV] END classifier__max_depth=10, classifier__max_features=sqrt, classifier__min_samples_leaf=1, classifier__min_samples_split=3, classifier__n_estimators=100; total time=   1.1s\n",
      "[CV] END classifier__max_depth=None, classifier__max_features=auto, classifier__min_samples_leaf=1, classifier__min_samples_split=2, classifier__n_estimators=200; total time=   0.1s\n",
      "[CV] END classifier__max_depth=10, classifier__max_features=sqrt, classifier__min_samples_leaf=2, classifier__min_samples_split=3, classifier__n_estimators=300; total time=   3.1s\n",
      "[CV] END classifier__max_depth=None, classifier__max_features=auto, classifier__min_samples_leaf=1, classifier__min_samples_split=2, classifier__n_estimators=200; total time=   0.2s\n",
      "[CV] END classifier__max_depth=10, classifier__max_features=sqrt, classifier__min_samples_leaf=2, classifier__min_samples_split=3, classifier__n_estimators=300; total time=   3.1s\n",
      "[CV] END classifier__max_depth=None, classifier__max_features=auto, classifier__min_samples_leaf=1, classifier__min_samples_split=2, classifier__n_estimators=200; total time=   0.1s\n",
      "[CV] END classifier__max_depth=10, classifier__max_features=sqrt, classifier__min_samples_leaf=2, classifier__min_samples_split=3, classifier__n_estimators=300; total time=   3.0s\n",
      "[CV] END classifier__max_depth=None, classifier__max_features=auto, classifier__min_samples_leaf=1, classifier__min_samples_split=2, classifier__n_estimators=200; total time=   0.1s\n",
      "[CV] END classifier__max_depth=None, classifier__max_features=auto, classifier__min_samples_leaf=1, classifier__min_samples_split=2, classifier__n_estimators=200; total time=   0.1s\n",
      "[CV] END classifier__max_depth=10, classifier__max_features=sqrt, classifier__min_samples_leaf=2, classifier__min_samples_split=3, classifier__n_estimators=300; total time=   3.6s\n",
      "[CV] END classifier__max_depth=10, classifier__max_features=sqrt, classifier__min_samples_leaf=2, classifier__min_samples_split=3, classifier__n_estimators=300; total time=   3.5s\n",
      "[CV] END classifier__max_depth=None, classifier__max_features=sqrt, classifier__min_samples_leaf=3, classifier__min_samples_split=3, classifier__n_estimators=200; total time=   2.9s\n",
      "[CV] END classifier__max_depth=None, classifier__max_features=sqrt, classifier__min_samples_leaf=3, classifier__min_samples_split=3, classifier__n_estimators=200; total time=   2.7s\n",
      "[CV] END classifier__max_depth=None, classifier__max_features=sqrt, classifier__min_samples_leaf=3, classifier__min_samples_split=3, classifier__n_estimators=200; total time=   2.7s\n",
      "[CV] END classifier__max_depth=None, classifier__max_features=sqrt, classifier__min_samples_leaf=3, classifier__min_samples_split=3, classifier__n_estimators=200; total time=   3.2s\n",
      "[CV] END classifier__max_depth=None, classifier__max_features=sqrt, classifier__min_samples_leaf=3, classifier__min_samples_split=3, classifier__n_estimators=200; total time=   3.3s\n",
      "[CV] END classifier__max_depth=20, classifier__max_features=sqrt, classifier__min_samples_leaf=1, classifier__min_samples_split=2, classifier__n_estimators=300; total time=   4.2s\n",
      "[CV] END classifier__max_depth=20, classifier__max_features=sqrt, classifier__min_samples_leaf=1, classifier__min_samples_split=2, classifier__n_estimators=300; total time=   4.2s\n",
      "[CV] END classifier__max_depth=20, classifier__max_features=sqrt, classifier__min_samples_leaf=3, classifier__min_samples_split=2, classifier__n_estimators=100; total time=   1.6s\n",
      "[CV] END classifier__max_depth=20, classifier__max_features=sqrt, classifier__min_samples_leaf=3, classifier__min_samples_split=2, classifier__n_estimators=100; total time=   1.5s\n",
      "[CV] END classifier__max_depth=5, classifier__max_features=auto, classifier__min_samples_leaf=3, classifier__min_samples_split=None, classifier__n_estimators=100; total time=   0.1s\n",
      "[CV] END classifier__max_depth=20, classifier__max_features=sqrt, classifier__min_samples_leaf=1, classifier__min_samples_split=2, classifier__n_estimators=300; total time=   4.7s\n",
      "[CV] END classifier__max_depth=20, classifier__max_features=sqrt, classifier__min_samples_leaf=3, classifier__min_samples_split=2, classifier__n_estimators=100; total time=   1.4s\n",
      "[CV] END classifier__max_depth=5, classifier__max_features=auto, classifier__min_samples_leaf=3, classifier__min_samples_split=None, classifier__n_estimators=100; total time=   0.1s\n",
      "[CV] END classifier__max_depth=20, classifier__max_features=sqrt, classifier__min_samples_leaf=1, classifier__min_samples_split=2, classifier__n_estimators=300; total time=   4.4s\n",
      "[CV] END classifier__max_depth=5, classifier__max_features=auto, classifier__min_samples_leaf=3, classifier__min_samples_split=None, classifier__n_estimators=100; total time=   0.1s\n",
      "[CV] END classifier__max_depth=20, classifier__max_features=sqrt, classifier__min_samples_leaf=3, classifier__min_samples_split=2, classifier__n_estimators=100; total time=   1.3s\n",
      "[CV] END classifier__max_depth=20, classifier__max_features=sqrt, classifier__min_samples_leaf=1, classifier__min_samples_split=2, classifier__n_estimators=300; total time=   4.5s\n",
      "[CV] END classifier__max_depth=10, classifier__max_features=sqrt, classifier__min_samples_leaf=1, classifier__min_samples_split=3, classifier__n_estimators=300; total time=   3.8s\n",
      "[CV] END classifier__max_depth=5, classifier__max_features=auto, classifier__min_samples_leaf=3, classifier__min_samples_split=None, classifier__n_estimators=100; total time=   0.1s\n",
      "[CV] END classifier__max_depth=5, classifier__max_features=auto, classifier__min_samples_leaf=3, classifier__min_samples_split=None, classifier__n_estimators=100; total time=   0.1s\n",
      "[CV] END classifier__max_depth=10, classifier__max_features=sqrt, classifier__min_samples_leaf=1, classifier__min_samples_split=3, classifier__n_estimators=300; total time=   3.3s\n",
      "[CV] END classifier__max_depth=20, classifier__max_features=sqrt, classifier__min_samples_leaf=3, classifier__min_samples_split=2, classifier__n_estimators=100; total time=   1.8s\n",
      "[CV] END classifier__max_depth=10, classifier__max_features=sqrt, classifier__min_samples_leaf=1, classifier__min_samples_split=3, classifier__n_estimators=300; total time=   3.8s\n",
      "[CV] END classifier__max_depth=None, classifier__max_features=auto, classifier__min_samples_leaf=1, classifier__min_samples_split=None, classifier__n_estimators=300; total time=   0.1s\n",
      "[CV] END classifier__max_depth=None, classifier__max_features=auto, classifier__min_samples_leaf=1, classifier__min_samples_split=None, classifier__n_estimators=300; total time=   0.1s\n",
      "[CV] END classifier__max_depth=None, classifier__max_features=auto, classifier__min_samples_leaf=1, classifier__min_samples_split=None, classifier__n_estimators=300; total time=   0.1s\n",
      "[CV] END classifier__max_depth=10, classifier__max_features=sqrt, classifier__min_samples_leaf=1, classifier__min_samples_split=3, classifier__n_estimators=300; total time=   4.0s\n",
      "[CV] END classifier__max_depth=10, classifier__max_features=sqrt, classifier__min_samples_leaf=1, classifier__min_samples_split=3, classifier__n_estimators=300; total time=   3.9s\n",
      "[CV] END classifier__max_depth=None, classifier__max_features=auto, classifier__min_samples_leaf=1, classifier__min_samples_split=None, classifier__n_estimators=300; total time=   0.1s\n",
      "[CV] END classifier__max_depth=None, classifier__max_features=auto, classifier__min_samples_leaf=1, classifier__min_samples_split=None, classifier__n_estimators=300; total time=   0.1s\n",
      "[CV] END classifier__max_depth=5, classifier__max_features=sqrt, classifier__min_samples_leaf=2, classifier__min_samples_split=2, classifier__n_estimators=300; total time=   2.0s\n",
      "[CV] END classifier__max_depth=20, classifier__max_features=auto, classifier__min_samples_leaf=2, classifier__min_samples_split=3, classifier__n_estimators=100; total time=   0.1s\n",
      "[CV] END classifier__max_depth=20, classifier__max_features=auto, classifier__min_samples_leaf=2, classifier__min_samples_split=3, classifier__n_estimators=100; total time=   0.1s\n",
      "[CV] END classifier__max_depth=5, classifier__max_features=sqrt, classifier__min_samples_leaf=2, classifier__min_samples_split=2, classifier__n_estimators=300; total time=   2.1s\n",
      "[CV] END classifier__max_depth=5, classifier__max_features=sqrt, classifier__min_samples_leaf=2, classifier__min_samples_split=2, classifier__n_estimators=300; total time=   2.0s\n",
      "[CV] END classifier__max_depth=5, classifier__max_features=sqrt, classifier__min_samples_leaf=2, classifier__min_samples_split=2, classifier__n_estimators=300; total time=   2.5s\n",
      "[CV] END classifier__max_depth=20, classifier__max_features=auto, classifier__min_samples_leaf=2, classifier__min_samples_split=3, classifier__n_estimators=100; total time=   0.1s\n",
      "[CV] END classifier__max_depth=20, classifier__max_features=auto, classifier__min_samples_leaf=2, classifier__min_samples_split=3, classifier__n_estimators=100; total time=   0.1s\n",
      "[CV] END classifier__max_depth=20, classifier__max_features=auto, classifier__min_samples_leaf=2, classifier__min_samples_split=3, classifier__n_estimators=100; total time=   0.1s\n",
      "[CV] END classifier__max_depth=5, classifier__max_features=sqrt, classifier__min_samples_leaf=2, classifier__min_samples_split=2, classifier__n_estimators=300; total time=   2.1s\n",
      "[CV] END classifier__max_depth=5, classifier__max_features=auto, classifier__min_samples_leaf=3, classifier__min_samples_split=2, classifier__n_estimators=200; total time=   0.1s\n",
      "[CV] END classifier__max_depth=5, classifier__max_features=auto, classifier__min_samples_leaf=3, classifier__min_samples_split=2, classifier__n_estimators=200; total time=   0.1s\n",
      "[CV] END classifier__max_depth=5, classifier__max_features=auto, classifier__min_samples_leaf=3, classifier__min_samples_split=2, classifier__n_estimators=200; total time=   0.1s\n",
      "[CV] END classifier__max_depth=5, classifier__max_features=auto, classifier__min_samples_leaf=3, classifier__min_samples_split=2, classifier__n_estimators=200; total time=   0.1s\n",
      "[CV] END classifier__max_depth=5, classifier__max_features=auto, classifier__min_samples_leaf=3, classifier__min_samples_split=2, classifier__n_estimators=200; total time=   0.1s\n",
      "[CV] END classifier__max_depth=10, classifier__max_features=auto, classifier__min_samples_leaf=2, classifier__min_samples_split=None, classifier__n_estimators=100; total time=   0.1s\n",
      "[CV] END classifier__max_depth=10, classifier__max_features=auto, classifier__min_samples_leaf=2, classifier__min_samples_split=None, classifier__n_estimators=100; total time=   0.1s\n",
      "[CV] END classifier__max_depth=10, classifier__max_features=auto, classifier__min_samples_leaf=2, classifier__min_samples_split=None, classifier__n_estimators=100; total time=   0.1s\n",
      "[CV] END classifier__max_depth=10, classifier__max_features=auto, classifier__min_samples_leaf=2, classifier__min_samples_split=None, classifier__n_estimators=100; total time=   0.1s\n",
      "[CV] END classifier__max_depth=5, classifier__max_features=sqrt, classifier__min_samples_leaf=1, classifier__min_samples_split=2, classifier__n_estimators=300; total time=   1.8s\n",
      "[CV] END classifier__max_depth=5, classifier__max_features=sqrt, classifier__min_samples_leaf=1, classifier__min_samples_split=2, classifier__n_estimators=300; total time=   1.8s\n",
      "[CV] END classifier__max_depth=10, classifier__max_features=auto, classifier__min_samples_leaf=2, classifier__min_samples_split=None, classifier__n_estimators=100; total time=   0.1s\n",
      "[CV] END classifier__max_depth=10, classifier__max_features=sqrt, classifier__min_samples_leaf=2, classifier__min_samples_split=None, classifier__n_estimators=200; total time=   0.1s\n",
      "[CV] END classifier__max_depth=5, classifier__max_features=sqrt, classifier__min_samples_leaf=1, classifier__min_samples_split=2, classifier__n_estimators=300; total time=   1.8s\n",
      "[CV] END classifier__max_depth=10, classifier__max_features=sqrt, classifier__min_samples_leaf=2, classifier__min_samples_split=None, classifier__n_estimators=200; total time=   0.1s\n",
      "[CV] END classifier__max_depth=10, classifier__max_features=sqrt, classifier__min_samples_leaf=2, classifier__min_samples_split=None, classifier__n_estimators=200; total time=   0.1s\n",
      "[CV] END classifier__max_depth=5, classifier__max_features=sqrt, classifier__min_samples_leaf=1, classifier__min_samples_split=2, classifier__n_estimators=300; total time=   2.0s\n",
      "[CV] END classifier__max_depth=5, classifier__max_features=sqrt, classifier__min_samples_leaf=1, classifier__min_samples_split=2, classifier__n_estimators=300; total time=   1.9s\n",
      "[CV] END classifier__max_depth=10, classifier__max_features=sqrt, classifier__min_samples_leaf=2, classifier__min_samples_split=None, classifier__n_estimators=200; total time=   0.1s\n",
      "[CV] END classifier__max_depth=10, classifier__max_features=sqrt, classifier__min_samples_leaf=2, classifier__min_samples_split=None, classifier__n_estimators=200; total time=   0.1s\n",
      "[CV] END classifier__max_depth=10, classifier__max_features=auto, classifier__min_samples_leaf=1, classifier__min_samples_split=None, classifier__n_estimators=100; total time=   0.1s\n",
      "[CV] END classifier__max_depth=10, classifier__max_features=auto, classifier__min_samples_leaf=1, classifier__min_samples_split=None, classifier__n_estimators=100; total time=   0.1s\n",
      "[CV] END classifier__max_depth=10, classifier__max_features=auto, classifier__min_samples_leaf=1, classifier__min_samples_split=None, classifier__n_estimators=100; total time=   0.1s\n",
      "[CV] END classifier__max_depth=10, classifier__max_features=auto, classifier__min_samples_leaf=1, classifier__min_samples_split=None, classifier__n_estimators=100; total time=   0.1s\n",
      "[CV] END classifier__max_depth=10, classifier__max_features=auto, classifier__min_samples_leaf=1, classifier__min_samples_split=None, classifier__n_estimators=100; total time=   0.1s\n",
      "[CV] END classifier__max_depth=10, classifier__max_features=auto, classifier__min_samples_leaf=2, classifier__min_samples_split=3, classifier__n_estimators=300; total time=   0.1s\n",
      "[CV] END classifier__max_depth=10, classifier__max_features=auto, classifier__min_samples_leaf=2, classifier__min_samples_split=3, classifier__n_estimators=300; total time=   0.1s\n",
      "[CV] END classifier__max_depth=10, classifier__max_features=auto, classifier__min_samples_leaf=2, classifier__min_samples_split=3, classifier__n_estimators=300; total time=   0.1s\n",
      "[CV] END classifier__max_depth=10, classifier__max_features=auto, classifier__min_samples_leaf=2, classifier__min_samples_split=3, classifier__n_estimators=300; total time=   0.1s\n",
      "[CV] END classifier__max_depth=10, classifier__max_features=auto, classifier__min_samples_leaf=2, classifier__min_samples_split=3, classifier__n_estimators=300; total time=   0.1s\n",
      "[CV] END classifier__max_depth=5, classifier__max_features=auto, classifier__min_samples_leaf=1, classifier__min_samples_split=3, classifier__n_estimators=300; total time=   0.1s\n",
      "[CV] END classifier__max_depth=5, classifier__max_features=auto, classifier__min_samples_leaf=1, classifier__min_samples_split=3, classifier__n_estimators=300; total time=   0.1s\n",
      "[CV] END classifier__max_depth=5, classifier__max_features=auto, classifier__min_samples_leaf=1, classifier__min_samples_split=3, classifier__n_estimators=300; total time=   0.1s\n",
      "[CV] END classifier__max_depth=5, classifier__max_features=auto, classifier__min_samples_leaf=1, classifier__min_samples_split=3, classifier__n_estimators=300; total time=   0.1s\n",
      "[CV] END classifier__max_depth=5, classifier__max_features=auto, classifier__min_samples_leaf=1, classifier__min_samples_split=3, classifier__n_estimators=300; total time=   0.1s\n",
      "[CV] END classifier__max_depth=10, classifier__max_features=auto, classifier__min_samples_leaf=1, classifier__min_samples_split=2, classifier__n_estimators=300; total time=   0.1s\n",
      "[CV] END classifier__max_depth=10, classifier__max_features=auto, classifier__min_samples_leaf=1, classifier__min_samples_split=2, classifier__n_estimators=300; total time=   0.2s\n",
      "[CV] END classifier__max_depth=10, classifier__max_features=auto, classifier__min_samples_leaf=1, classifier__min_samples_split=2, classifier__n_estimators=300; total time=   0.1s\n",
      "[CV] END classifier__max_depth=10, classifier__max_features=auto, classifier__min_samples_leaf=1, classifier__min_samples_split=2, classifier__n_estimators=300; total time=   0.1s\n",
      "[CV] END classifier__max_depth=10, classifier__max_features=auto, classifier__min_samples_leaf=1, classifier__min_samples_split=2, classifier__n_estimators=300; total time=   0.1s\n",
      "[CV] END classifier__max_depth=20, classifier__max_features=sqrt, classifier__min_samples_leaf=2, classifier__min_samples_split=None, classifier__n_estimators=300; total time=   0.1s\n",
      "[CV] END classifier__max_depth=20, classifier__max_features=sqrt, classifier__min_samples_leaf=2, classifier__min_samples_split=None, classifier__n_estimators=300; total time=   0.1s\n",
      "[CV] END classifier__max_depth=20, classifier__max_features=sqrt, classifier__min_samples_leaf=2, classifier__min_samples_split=None, classifier__n_estimators=300; total time=   0.1s\n",
      "[CV] END classifier__max_depth=20, classifier__max_features=sqrt, classifier__min_samples_leaf=2, classifier__min_samples_split=None, classifier__n_estimators=300; total time=   0.1s\n",
      "[CV] END classifier__max_depth=20, classifier__max_features=sqrt, classifier__min_samples_leaf=2, classifier__min_samples_split=None, classifier__n_estimators=300; total time=   0.1s\n",
      "[CV] END classifier__max_depth=20, classifier__max_features=sqrt, classifier__min_samples_leaf=1, classifier__min_samples_split=2, classifier__n_estimators=200; total time=   2.7s\n",
      "[CV] END classifier__max_depth=20, classifier__max_features=sqrt, classifier__min_samples_leaf=1, classifier__min_samples_split=2, classifier__n_estimators=200; total time=   2.6s\n",
      "[CV] END classifier__max_depth=20, classifier__max_features=sqrt, classifier__min_samples_leaf=1, classifier__min_samples_split=2, classifier__n_estimators=200; total time=   2.6s\n",
      "[CV] END classifier__max_depth=20, classifier__max_features=sqrt, classifier__min_samples_leaf=1, classifier__min_samples_split=2, classifier__n_estimators=200; total time=   2.6s\n",
      "[CV] END classifier__max_depth=20, classifier__max_features=sqrt, classifier__min_samples_leaf=1, classifier__min_samples_split=2, classifier__n_estimators=200; total time=   2.8s\n",
      "[CV] END classifier__max_depth=10, classifier__max_features=sqrt, classifier__min_samples_leaf=3, classifier__min_samples_split=2, classifier__n_estimators=200; total time=   2.1s\n",
      "[CV] END classifier__max_depth=10, classifier__max_features=sqrt, classifier__min_samples_leaf=3, classifier__min_samples_split=2, classifier__n_estimators=200; total time=   2.0s\n",
      "[CV] END classifier__max_depth=10, classifier__max_features=auto, classifier__min_samples_leaf=2, classifier__min_samples_split=2, classifier__n_estimators=300; total time=   0.1s\n",
      "[CV] END classifier__max_depth=10, classifier__max_features=sqrt, classifier__min_samples_leaf=3, classifier__min_samples_split=2, classifier__n_estimators=200; total time=   2.0s\n",
      "[CV] END classifier__max_depth=10, classifier__max_features=auto, classifier__min_samples_leaf=2, classifier__min_samples_split=2, classifier__n_estimators=300; total time=   0.1s\n",
      "[CV] END classifier__max_depth=10, classifier__max_features=sqrt, classifier__min_samples_leaf=3, classifier__min_samples_split=2, classifier__n_estimators=200; total time=   2.1s\n",
      "[CV] END classifier__max_depth=10, classifier__max_features=auto, classifier__min_samples_leaf=2, classifier__min_samples_split=2, classifier__n_estimators=300; total time=   0.1s\n",
      "[CV] END classifier__max_depth=10, classifier__max_features=auto, classifier__min_samples_leaf=2, classifier__min_samples_split=2, classifier__n_estimators=300; total time=   0.1s\n",
      "[CV] END classifier__max_depth=10, classifier__max_features=sqrt, classifier__min_samples_leaf=3, classifier__min_samples_split=2, classifier__n_estimators=200; total time=   2.2s\n",
      "[CV] END classifier__max_depth=10, classifier__max_features=auto, classifier__min_samples_leaf=2, classifier__min_samples_split=2, classifier__n_estimators=300; total time=   0.1s\n",
      "[CV] END classifier__max_depth=20, classifier__max_features=auto, classifier__min_samples_leaf=1, classifier__min_samples_split=2, classifier__n_estimators=200; total time=   0.1s\n",
      "[CV] END classifier__max_depth=20, classifier__max_features=auto, classifier__min_samples_leaf=1, classifier__min_samples_split=2, classifier__n_estimators=200; total time=   0.1s\n",
      "[CV] END classifier__max_depth=20, classifier__max_features=auto, classifier__min_samples_leaf=1, classifier__min_samples_split=2, classifier__n_estimators=200; total time=   0.1s\n",
      "[CV] END classifier__max_depth=20, classifier__max_features=auto, classifier__min_samples_leaf=1, classifier__min_samples_split=2, classifier__n_estimators=200; total time=   0.1s\n",
      "[CV] END classifier__max_depth=20, classifier__max_features=auto, classifier__min_samples_leaf=1, classifier__min_samples_split=2, classifier__n_estimators=200; total time=   0.1s\n",
      "[CV] END classifier__max_depth=10, classifier__max_features=sqrt, classifier__min_samples_leaf=1, classifier__min_samples_split=2, classifier__n_estimators=200; total time=   2.0s\n",
      "[CV] END classifier__max_depth=10, classifier__max_features=sqrt, classifier__min_samples_leaf=1, classifier__min_samples_split=2, classifier__n_estimators=200; total time=   2.2s\n",
      "[CV] END classifier__max_depth=None, classifier__max_features=auto, classifier__min_samples_leaf=3, classifier__min_samples_split=3, classifier__n_estimators=100; total time=   0.1s\n",
      "[CV] END classifier__max_depth=10, classifier__max_features=sqrt, classifier__min_samples_leaf=1, classifier__min_samples_split=2, classifier__n_estimators=200; total time=   2.0s\n",
      "[CV] END classifier__max_depth=None, classifier__max_features=auto, classifier__min_samples_leaf=3, classifier__min_samples_split=3, classifier__n_estimators=100; total time=   0.1s\n",
      "[CV] END classifier__max_depth=10, classifier__max_features=sqrt, classifier__min_samples_leaf=1, classifier__min_samples_split=2, classifier__n_estimators=200; total time=   1.9s\n",
      "[CV] END classifier__max_depth=None, classifier__max_features=auto, classifier__min_samples_leaf=3, classifier__min_samples_split=3, classifier__n_estimators=100; total time=   0.1s\n",
      "[CV] END classifier__max_depth=10, classifier__max_features=sqrt, classifier__min_samples_leaf=1, classifier__min_samples_split=2, classifier__n_estimators=200; total time=   1.9s\n",
      "[CV] END classifier__max_depth=None, classifier__max_features=auto, classifier__min_samples_leaf=3, classifier__min_samples_split=3, classifier__n_estimators=100; total time=   0.1s\n",
      "[CV] END classifier__max_depth=None, classifier__max_features=auto, classifier__min_samples_leaf=3, classifier__min_samples_split=3, classifier__n_estimators=100; total time=   0.1s\n",
      "[CV] END classifier__max_depth=5, classifier__max_features=auto, classifier__min_samples_leaf=2, classifier__min_samples_split=3, classifier__n_estimators=100; total time=   0.1s\n",
      "[CV] END classifier__max_depth=5, classifier__max_features=auto, classifier__min_samples_leaf=2, classifier__min_samples_split=3, classifier__n_estimators=100; total time=   0.1s\n",
      "[CV] END classifier__max_depth=5, classifier__max_features=auto, classifier__min_samples_leaf=2, classifier__min_samples_split=3, classifier__n_estimators=100; total time=   0.1s\n",
      "[CV] END classifier__max_depth=5, classifier__max_features=auto, classifier__min_samples_leaf=2, classifier__min_samples_split=3, classifier__n_estimators=100; total time=   0.1s\n",
      "[CV] END classifier__max_depth=5, classifier__max_features=sqrt, classifier__min_samples_leaf=1, classifier__min_samples_split=3, classifier__n_estimators=200; total time=   1.3s\n",
      "[CV] END classifier__max_depth=5, classifier__max_features=auto, classifier__min_samples_leaf=2, classifier__min_samples_split=3, classifier__n_estimators=100; total time=   0.1s\n",
      "[CV] END classifier__max_depth=5, classifier__max_features=sqrt, classifier__min_samples_leaf=1, classifier__min_samples_split=3, classifier__n_estimators=200; total time=   1.3s\n",
      "[CV] END classifier__max_depth=5, classifier__max_features=sqrt, classifier__min_samples_leaf=1, classifier__min_samples_split=None, classifier__n_estimators=300; total time=   0.1s\n",
      "[CV] END classifier__max_depth=5, classifier__max_features=sqrt, classifier__min_samples_leaf=1, classifier__min_samples_split=3, classifier__n_estimators=200; total time=   1.3s\n",
      "[CV] END classifier__max_depth=5, classifier__max_features=sqrt, classifier__min_samples_leaf=1, classifier__min_samples_split=None, classifier__n_estimators=300; total time=   0.1s\n",
      "[CV] END classifier__max_depth=5, classifier__max_features=sqrt, classifier__min_samples_leaf=1, classifier__min_samples_split=None, classifier__n_estimators=300; total time=   0.1s\n",
      "[CV] END classifier__max_depth=5, classifier__max_features=sqrt, classifier__min_samples_leaf=1, classifier__min_samples_split=3, classifier__n_estimators=200; total time=   1.3s\n",
      "[CV] END classifier__max_depth=5, classifier__max_features=sqrt, classifier__min_samples_leaf=1, classifier__min_samples_split=3, classifier__n_estimators=200; total time=   1.2s\n",
      "[CV] END classifier__max_depth=5, classifier__max_features=sqrt, classifier__min_samples_leaf=1, classifier__min_samples_split=None, classifier__n_estimators=300; total time=   0.1s\n",
      "[CV] END classifier__max_depth=5, classifier__max_features=sqrt, classifier__min_samples_leaf=1, classifier__min_samples_split=None, classifier__n_estimators=300; total time=   0.1s\n",
      "[CV] END classifier__max_depth=5, classifier__max_features=sqrt, classifier__min_samples_leaf=2, classifier__min_samples_split=None, classifier__n_estimators=100; total time=   0.1s\n",
      "[CV] END classifier__max_depth=5, classifier__max_features=sqrt, classifier__min_samples_leaf=2, classifier__min_samples_split=None, classifier__n_estimators=100; total time=   0.1s\n",
      "[CV] END classifier__max_depth=5, classifier__max_features=sqrt, classifier__min_samples_leaf=2, classifier__min_samples_split=None, classifier__n_estimators=100; total time=   0.1s\n",
      "[CV] END classifier__max_depth=5, classifier__max_features=sqrt, classifier__min_samples_leaf=2, classifier__min_samples_split=None, classifier__n_estimators=100; total time=   0.1s\n",
      "[CV] END classifier__max_depth=5, classifier__max_features=sqrt, classifier__min_samples_leaf=2, classifier__min_samples_split=None, classifier__n_estimators=100; total time=   0.1s\n",
      "[CV] END classifier__max_depth=5, classifier__max_features=sqrt, classifier__min_samples_leaf=1, classifier__min_samples_split=2, classifier__n_estimators=200; total time=   1.3s\n",
      "[CV] END classifier__max_depth=5, classifier__max_features=sqrt, classifier__min_samples_leaf=1, classifier__min_samples_split=2, classifier__n_estimators=200; total time=   1.2s\n",
      "[CV] END classifier__max_depth=5, classifier__max_features=sqrt, classifier__min_samples_leaf=1, classifier__min_samples_split=2, classifier__n_estimators=200; total time=   1.3s\n",
      "[CV] END classifier__max_depth=5, classifier__max_features=sqrt, classifier__min_samples_leaf=1, classifier__min_samples_split=2, classifier__n_estimators=200; total time=   1.3s\n",
      "[CV] END classifier__max_depth=5, classifier__max_features=sqrt, classifier__min_samples_leaf=1, classifier__min_samples_split=2, classifier__n_estimators=200; total time=   1.3s\n",
      "[CV] END classifier__max_depth=5, classifier__max_features=sqrt, classifier__min_samples_leaf=3, classifier__min_samples_split=None, classifier__n_estimators=100; total time=   0.1s\n",
      "[CV] END classifier__max_depth=5, classifier__max_features=sqrt, classifier__min_samples_leaf=3, classifier__min_samples_split=None, classifier__n_estimators=100; total time=   0.1s\n",
      "[CV] END classifier__max_depth=5, classifier__max_features=sqrt, classifier__min_samples_leaf=3, classifier__min_samples_split=None, classifier__n_estimators=100; total time=   0.1s\n",
      "[CV] END classifier__max_depth=5, classifier__max_features=sqrt, classifier__min_samples_leaf=3, classifier__min_samples_split=None, classifier__n_estimators=100; total time=   0.1s\n",
      "[CV] END classifier__max_depth=5, classifier__max_features=sqrt, classifier__min_samples_leaf=3, classifier__min_samples_split=None, classifier__n_estimators=100; total time=   0.1s\n",
      "[CV] END classifier__max_depth=20, classifier__max_features=sqrt, classifier__min_samples_leaf=2, classifier__min_samples_split=3, classifier__n_estimators=100; total time=   1.4s\n",
      "[CV] END classifier__max_depth=20, classifier__max_features=sqrt, classifier__min_samples_leaf=2, classifier__min_samples_split=3, classifier__n_estimators=100; total time=   1.3s\n",
      "[CV] END classifier__max_depth=20, classifier__max_features=sqrt, classifier__min_samples_leaf=2, classifier__min_samples_split=3, classifier__n_estimators=100; total time=   1.3s\n",
      "[CV] END classifier__max_depth=None, classifier__max_features=sqrt, classifier__min_samples_leaf=2, classifier__min_samples_split=None, classifier__n_estimators=300; total time=   0.1s\n",
      "[CV] END classifier__max_depth=None, classifier__max_features=sqrt, classifier__min_samples_leaf=2, classifier__min_samples_split=None, classifier__n_estimators=300; total time=   0.1s\n",
      "[CV] END classifier__max_depth=None, classifier__max_features=sqrt, classifier__min_samples_leaf=2, classifier__min_samples_split=None, classifier__n_estimators=300; total time=   0.1s\n",
      "[CV] END classifier__max_depth=20, classifier__max_features=sqrt, classifier__min_samples_leaf=2, classifier__min_samples_split=3, classifier__n_estimators=100; total time=   1.4s\n",
      "[CV] END classifier__max_depth=None, classifier__max_features=sqrt, classifier__min_samples_leaf=2, classifier__min_samples_split=None, classifier__n_estimators=300; total time=   0.1s\n",
      "[CV] END classifier__max_depth=None, classifier__max_features=sqrt, classifier__min_samples_leaf=2, classifier__min_samples_split=None, classifier__n_estimators=300; total time=   0.1s\n",
      "[CV] END classifier__max_depth=20, classifier__max_features=sqrt, classifier__min_samples_leaf=2, classifier__min_samples_split=3, classifier__n_estimators=100; total time=   1.5s\n",
      "[CV] END classifier__max_depth=None, classifier__max_features=auto, classifier__min_samples_leaf=3, classifier__min_samples_split=2, classifier__n_estimators=100; total time=   0.1s\n",
      "[CV] END classifier__max_depth=None, classifier__max_features=auto, classifier__min_samples_leaf=3, classifier__min_samples_split=2, classifier__n_estimators=100; total time=   0.1s\n",
      "[CV] END classifier__max_depth=None, classifier__max_features=auto, classifier__min_samples_leaf=3, classifier__min_samples_split=2, classifier__n_estimators=100; total time=   0.1s\n",
      "[CV] END classifier__max_depth=None, classifier__max_features=sqrt, classifier__min_samples_leaf=3, classifier__min_samples_split=2, classifier__n_estimators=300; total time=   3.7s\n",
      "[CV] END classifier__max_depth=None, classifier__max_features=sqrt, classifier__min_samples_leaf=3, classifier__min_samples_split=2, classifier__n_estimators=300; total time=   3.9s\n",
      "[CV] END classifier__max_depth=None, classifier__max_features=auto, classifier__min_samples_leaf=3, classifier__min_samples_split=2, classifier__n_estimators=100; total time=   0.1s\n",
      "[CV] END classifier__max_depth=None, classifier__max_features=sqrt, classifier__min_samples_leaf=3, classifier__min_samples_split=2, classifier__n_estimators=300; total time=   3.7s\n",
      "[CV] END classifier__max_depth=10, classifier__max_features=sqrt, classifier__min_samples_leaf=3, classifier__min_samples_split=2, classifier__n_estimators=300; total time=   3.3s\n",
      "[CV] END classifier__max_depth=10, classifier__max_features=sqrt, classifier__min_samples_leaf=3, classifier__min_samples_split=2, classifier__n_estimators=300; total time=   3.1s\n",
      "[CV] END classifier__max_depth=10, classifier__max_features=sqrt, classifier__min_samples_leaf=3, classifier__min_samples_split=2, classifier__n_estimators=300; total time=   3.0s\n",
      "[CV] END classifier__max_depth=10, classifier__max_features=sqrt, classifier__min_samples_leaf=3, classifier__min_samples_split=2, classifier__n_estimators=300; total time=   3.3s\n",
      "[CV] END classifier__max_depth=None, classifier__max_features=auto, classifier__min_samples_leaf=3, classifier__min_samples_split=2, classifier__n_estimators=100; total time=   0.1s\n",
      "[CV] END classifier__max_depth=None, classifier__max_features=sqrt, classifier__min_samples_leaf=3, classifier__min_samples_split=2, classifier__n_estimators=300; total time=   3.7s\n",
      "[CV] END classifier__max_depth=10, classifier__max_features=auto, classifier__min_samples_leaf=3, classifier__min_samples_split=3, classifier__n_estimators=200; total time=   0.1s\n",
      "[CV] END classifier__max_depth=10, classifier__max_features=auto, classifier__min_samples_leaf=3, classifier__min_samples_split=3, classifier__n_estimators=200; total time=   0.1s\n",
      "[CV] END classifier__max_depth=None, classifier__max_features=sqrt, classifier__min_samples_leaf=3, classifier__min_samples_split=2, classifier__n_estimators=300; total time=   3.8s\n",
      "[CV] END classifier__max_depth=10, classifier__max_features=sqrt, classifier__min_samples_leaf=3, classifier__min_samples_split=2, classifier__n_estimators=300; total time=   3.2s\n",
      "[CV] END classifier__max_depth=10, classifier__max_features=auto, classifier__min_samples_leaf=3, classifier__min_samples_split=3, classifier__n_estimators=200; total time=   0.1s\n",
      "[CV] END classifier__max_depth=10, classifier__max_features=auto, classifier__min_samples_leaf=3, classifier__min_samples_split=3, classifier__n_estimators=200; total time=   0.1s\n",
      "[CV] END classifier__max_depth=10, classifier__max_features=auto, classifier__min_samples_leaf=3, classifier__min_samples_split=3, classifier__n_estimators=200; total time=   0.1s\n",
      "[CV] END classifier__max_depth=20, classifier__max_features=auto, classifier__min_samples_leaf=3, classifier__min_samples_split=None, classifier__n_estimators=100; total time=   0.1s\n",
      "[CV] END classifier__max_depth=20, classifier__max_features=auto, classifier__min_samples_leaf=3, classifier__min_samples_split=None, classifier__n_estimators=100; total time=   0.1s\n",
      "[CV] END classifier__max_depth=20, classifier__max_features=auto, classifier__min_samples_leaf=3, classifier__min_samples_split=None, classifier__n_estimators=100; total time=   0.1s\n",
      "[CV] END classifier__max_depth=20, classifier__max_features=auto, classifier__min_samples_leaf=3, classifier__min_samples_split=None, classifier__n_estimators=100; total time=   0.1s\n",
      "[CV] END classifier__max_depth=20, classifier__max_features=auto, classifier__min_samples_leaf=3, classifier__min_samples_split=None, classifier__n_estimators=100; total time=   0.1s\n",
      "[CV] END classifier__max_depth=10, classifier__max_features=auto, classifier__min_samples_leaf=3, classifier__min_samples_split=None, classifier__n_estimators=100; total time=   0.1s\n",
      "[CV] END classifier__max_depth=10, classifier__max_features=auto, classifier__min_samples_leaf=3, classifier__min_samples_split=None, classifier__n_estimators=100; total time=   0.1s\n",
      "[CV] END classifier__max_depth=10, classifier__max_features=auto, classifier__min_samples_leaf=3, classifier__min_samples_split=None, classifier__n_estimators=100; total time=   0.1s\n",
      "[CV] END classifier__max_depth=10, classifier__max_features=auto, classifier__min_samples_leaf=3, classifier__min_samples_split=None, classifier__n_estimators=100; total time=   0.1s\n",
      "[CV] END classifier__max_depth=5, classifier__max_features=sqrt, classifier__min_samples_leaf=2, classifier__min_samples_split=3, classifier__n_estimators=300; total time=   1.9s\n",
      "[CV] END classifier__max_depth=10, classifier__max_features=auto, classifier__min_samples_leaf=3, classifier__min_samples_split=None, classifier__n_estimators=100; total time=   0.1s\n",
      "[CV] END classifier__max_depth=5, classifier__max_features=sqrt, classifier__min_samples_leaf=2, classifier__min_samples_split=3, classifier__n_estimators=300; total time=   1.8s\n",
      "[CV] END classifier__max_depth=20, classifier__max_features=auto, classifier__min_samples_leaf=3, classifier__min_samples_split=2, classifier__n_estimators=200; total time=   0.1s\n",
      "[CV] END classifier__max_depth=5, classifier__max_features=sqrt, classifier__min_samples_leaf=2, classifier__min_samples_split=3, classifier__n_estimators=300; total time=   1.8s\n",
      "[CV] END classifier__max_depth=5, classifier__max_features=sqrt, classifier__min_samples_leaf=2, classifier__min_samples_split=3, classifier__n_estimators=300; total time=   1.9s\n",
      "[CV] END classifier__max_depth=20, classifier__max_features=auto, classifier__min_samples_leaf=3, classifier__min_samples_split=2, classifier__n_estimators=200; total time=   0.1s\n",
      "[CV] END classifier__max_depth=5, classifier__max_features=sqrt, classifier__min_samples_leaf=2, classifier__min_samples_split=3, classifier__n_estimators=300; total time=   1.9s\n",
      "[CV] END classifier__max_depth=20, classifier__max_features=auto, classifier__min_samples_leaf=3, classifier__min_samples_split=2, classifier__n_estimators=200; total time=   0.1s\n",
      "[CV] END classifier__max_depth=20, classifier__max_features=auto, classifier__min_samples_leaf=3, classifier__min_samples_split=2, classifier__n_estimators=200; total time=   0.1s\n",
      "[CV] END classifier__max_depth=20, classifier__max_features=auto, classifier__min_samples_leaf=3, classifier__min_samples_split=2, classifier__n_estimators=200; total time=   0.1s\n",
      "[CV] END classifier__max_depth=5, classifier__max_features=auto, classifier__min_samples_leaf=3, classifier__min_samples_split=None, classifier__n_estimators=300; total time=   0.1s\n",
      "[CV] END classifier__max_depth=5, classifier__max_features=auto, classifier__min_samples_leaf=3, classifier__min_samples_split=None, classifier__n_estimators=300; total time=   0.1s\n",
      "[CV] END classifier__max_depth=5, classifier__max_features=auto, classifier__min_samples_leaf=3, classifier__min_samples_split=None, classifier__n_estimators=300; total time=   0.1s\n",
      "[CV] END classifier__max_depth=5, classifier__max_features=auto, classifier__min_samples_leaf=3, classifier__min_samples_split=None, classifier__n_estimators=300; total time=   0.1s\n",
      "[CV] END classifier__max_depth=5, classifier__max_features=auto, classifier__min_samples_leaf=3, classifier__min_samples_split=None, classifier__n_estimators=300; total time=   0.1s\n",
      "[CV] END classifier__max_depth=20, classifier__max_features=sqrt, classifier__min_samples_leaf=1, classifier__min_samples_split=None, classifier__n_estimators=300; total time=   0.1s\n",
      "[CV] END classifier__max_depth=20, classifier__max_features=sqrt, classifier__min_samples_leaf=1, classifier__min_samples_split=None, classifier__n_estimators=300; total time=   0.1s\n",
      "[CV] END classifier__max_depth=20, classifier__max_features=sqrt, classifier__min_samples_leaf=1, classifier__min_samples_split=None, classifier__n_estimators=300; total time=   0.1s\n",
      "[CV] END classifier__max_depth=20, classifier__max_features=sqrt, classifier__min_samples_leaf=1, classifier__min_samples_split=None, classifier__n_estimators=300; total time=   0.1s\n",
      "[CV] END classifier__max_depth=20, classifier__max_features=sqrt, classifier__min_samples_leaf=1, classifier__min_samples_split=None, classifier__n_estimators=300; total time=   0.1s\n",
      "[CV] END classifier__max_depth=20, classifier__max_features=auto, classifier__min_samples_leaf=1, classifier__min_samples_split=3, classifier__n_estimators=300; total time=   0.1s\n",
      "[CV] END classifier__max_depth=20, classifier__max_features=auto, classifier__min_samples_leaf=1, classifier__min_samples_split=3, classifier__n_estimators=300; total time=   0.1s\n",
      "[CV] END classifier__max_depth=20, classifier__max_features=auto, classifier__min_samples_leaf=1, classifier__min_samples_split=3, classifier__n_estimators=300; total time=   0.1s\n",
      "[CV] END classifier__max_depth=20, classifier__max_features=auto, classifier__min_samples_leaf=1, classifier__min_samples_split=3, classifier__n_estimators=300; total time=   0.1s\n",
      "[CV] END classifier__max_depth=20, classifier__max_features=auto, classifier__min_samples_leaf=1, classifier__min_samples_split=3, classifier__n_estimators=300; total time=   0.1s\n",
      "[CV] END classifier__max_depth=20, classifier__max_features=auto, classifier__min_samples_leaf=2, classifier__min_samples_split=None, classifier__n_estimators=300; total time=   0.1s\n",
      "[CV] END classifier__max_depth=20, classifier__max_features=auto, classifier__min_samples_leaf=2, classifier__min_samples_split=None, classifier__n_estimators=300; total time=   0.1s\n",
      "[CV] END classifier__max_depth=20, classifier__max_features=auto, classifier__min_samples_leaf=2, classifier__min_samples_split=None, classifier__n_estimators=300; total time=   0.1s\n",
      "[CV] END classifier__max_depth=20, classifier__max_features=auto, classifier__min_samples_leaf=2, classifier__min_samples_split=None, classifier__n_estimators=300; total time=   0.1s\n",
      "[CV] END classifier__max_depth=20, classifier__max_features=auto, classifier__min_samples_leaf=2, classifier__min_samples_split=None, classifier__n_estimators=300; total time=   0.1s\n",
      "[CV] END classifier__max_depth=5, classifier__max_features=auto, classifier__min_samples_leaf=1, classifier__min_samples_split=2, classifier__n_estimators=300; total time=   0.1s\n",
      "[CV] END classifier__max_depth=5, classifier__max_features=auto, classifier__min_samples_leaf=1, classifier__min_samples_split=2, classifier__n_estimators=300; total time=   0.1s\n",
      "[CV] END classifier__max_depth=5, classifier__max_features=auto, classifier__min_samples_leaf=1, classifier__min_samples_split=2, classifier__n_estimators=300; total time=   0.1s\n",
      "[CV] END classifier__max_depth=5, classifier__max_features=auto, classifier__min_samples_leaf=1, classifier__min_samples_split=2, classifier__n_estimators=300; total time=   0.1s\n",
      "[CV] END classifier__max_depth=5, classifier__max_features=auto, classifier__min_samples_leaf=1, classifier__min_samples_split=2, classifier__n_estimators=300; total time=   0.1s\n",
      "[CV] END classifier__max_depth=10, classifier__max_features=auto, classifier__min_samples_leaf=3, classifier__min_samples_split=3, classifier__n_estimators=300; total time=   0.1s\n",
      "[CV] END classifier__max_depth=10, classifier__max_features=auto, classifier__min_samples_leaf=3, classifier__min_samples_split=3, classifier__n_estimators=300; total time=   0.1s\n",
      "[CV] END classifier__max_depth=10, classifier__max_features=auto, classifier__min_samples_leaf=3, classifier__min_samples_split=3, classifier__n_estimators=300; total time=   0.1s\n",
      "[CV] END classifier__max_depth=10, classifier__max_features=auto, classifier__min_samples_leaf=3, classifier__min_samples_split=3, classifier__n_estimators=300; total time=   0.1s\n",
      "[CV] END classifier__max_depth=10, classifier__max_features=auto, classifier__min_samples_leaf=3, classifier__min_samples_split=3, classifier__n_estimators=300; total time=   0.1s\n",
      "[CV] END classifier__max_depth=None, classifier__max_features=auto, classifier__min_samples_leaf=2, classifier__min_samples_split=3, classifier__n_estimators=200; total time=   0.1s\n",
      "[CV] END classifier__max_depth=None, classifier__max_features=auto, classifier__min_samples_leaf=2, classifier__min_samples_split=3, classifier__n_estimators=200; total time=   0.1s\n",
      "[CV] END classifier__max_depth=None, classifier__max_features=auto, classifier__min_samples_leaf=2, classifier__min_samples_split=3, classifier__n_estimators=200; total time=   0.1s\n",
      "[CV] END classifier__max_depth=None, classifier__max_features=auto, classifier__min_samples_leaf=2, classifier__min_samples_split=3, classifier__n_estimators=200; total time=   0.1s\n",
      "[CV] END classifier__max_depth=None, classifier__max_features=auto, classifier__min_samples_leaf=2, classifier__min_samples_split=3, classifier__n_estimators=200; total time=   0.1s\n",
      "[CV] END classifier__max_depth=20, classifier__max_features=auto, classifier__min_samples_leaf=1, classifier__min_samples_split=None, classifier__n_estimators=200; total time=   0.1s\n",
      "[CV] END classifier__max_depth=20, classifier__max_features=auto, classifier__min_samples_leaf=1, classifier__min_samples_split=None, classifier__n_estimators=200; total time=   0.1s\n",
      "[CV] END classifier__max_depth=20, classifier__max_features=auto, classifier__min_samples_leaf=1, classifier__min_samples_split=None, classifier__n_estimators=200; total time=   0.1s\n",
      "[CV] END classifier__max_depth=5, classifier__max_features=sqrt, classifier__min_samples_leaf=3, classifier__min_samples_split=2, classifier__n_estimators=200; total time=   1.2s\n",
      "[CV] END classifier__max_depth=5, classifier__max_features=sqrt, classifier__min_samples_leaf=3, classifier__min_samples_split=2, classifier__n_estimators=200; total time=   1.4s\n",
      "[CV] END classifier__max_depth=20, classifier__max_features=auto, classifier__min_samples_leaf=1, classifier__min_samples_split=None, classifier__n_estimators=200; total time=   0.1s\n",
      "[CV] END classifier__max_depth=5, classifier__max_features=sqrt, classifier__min_samples_leaf=3, classifier__min_samples_split=2, classifier__n_estimators=200; total time=   1.6s\n",
      "[CV] END classifier__max_depth=20, classifier__max_features=auto, classifier__min_samples_leaf=1, classifier__min_samples_split=None, classifier__n_estimators=200; total time=   0.1s\n",
      "[CV] END classifier__max_depth=5, classifier__max_features=sqrt, classifier__min_samples_leaf=3, classifier__min_samples_split=2, classifier__n_estimators=200; total time=   1.4s\n",
      "[CV] END classifier__max_depth=5, classifier__max_features=sqrt, classifier__min_samples_leaf=3, classifier__min_samples_split=2, classifier__n_estimators=200; total time=   1.4s\n",
      "[CV] END classifier__max_depth=10, classifier__max_features=sqrt, classifier__min_samples_leaf=2, classifier__min_samples_split=2, classifier__n_estimators=100; total time=   1.0s\n",
      "[CV] END classifier__max_depth=10, classifier__max_features=sqrt, classifier__min_samples_leaf=2, classifier__min_samples_split=2, classifier__n_estimators=100; total time=   1.1s\n",
      "[CV] END classifier__max_depth=10, classifier__max_features=sqrt, classifier__min_samples_leaf=2, classifier__min_samples_split=2, classifier__n_estimators=100; total time=   1.3s\n",
      "[CV] END classifier__max_depth=10, classifier__max_features=sqrt, classifier__min_samples_leaf=2, classifier__min_samples_split=2, classifier__n_estimators=100; total time=   1.1s\n",
      "[CV] END classifier__max_depth=10, classifier__max_features=sqrt, classifier__min_samples_leaf=2, classifier__min_samples_split=2, classifier__n_estimators=100; total time=   1.2s\n",
      "[CV] END classifier__max_depth=20, classifier__max_features=auto, classifier__min_samples_leaf=2, classifier__min_samples_split=3, classifier__n_estimators=300; total time=   0.1s\n",
      "[CV] END classifier__max_depth=5, classifier__max_features=sqrt, classifier__min_samples_leaf=2, classifier__min_samples_split=2, classifier__n_estimators=100; total time=   0.8s\n",
      "[CV] END classifier__max_depth=20, classifier__max_features=sqrt, classifier__min_samples_leaf=2, classifier__min_samples_split=2, classifier__n_estimators=100; total time=   1.3s\n",
      "[CV] END classifier__max_depth=20, classifier__max_features=auto, classifier__min_samples_leaf=2, classifier__min_samples_split=3, classifier__n_estimators=300; total time=   0.1s\n",
      "[CV] END classifier__max_depth=20, classifier__max_features=sqrt, classifier__min_samples_leaf=2, classifier__min_samples_split=2, classifier__n_estimators=100; total time=   1.4s\n",
      "[CV] END classifier__max_depth=10, classifier__max_features=sqrt, classifier__min_samples_leaf=1, classifier__min_samples_split=3, classifier__n_estimators=200; total time=   2.2s\n",
      "[CV] END classifier__max_depth=5, classifier__max_features=sqrt, classifier__min_samples_leaf=2, classifier__min_samples_split=2, classifier__n_estimators=100; total time=   0.7s\n",
      "[CV] END classifier__max_depth=20, classifier__max_features=auto, classifier__min_samples_leaf=2, classifier__min_samples_split=3, classifier__n_estimators=300; total time=   0.1s\n",
      "[CV] END classifier__max_depth=20, classifier__max_features=sqrt, classifier__min_samples_leaf=2, classifier__min_samples_split=2, classifier__n_estimators=100; total time=   1.3s\n",
      "[CV] END classifier__max_depth=10, classifier__max_features=sqrt, classifier__min_samples_leaf=1, classifier__min_samples_split=3, classifier__n_estimators=200; total time=   2.1s\n",
      "[CV] END classifier__max_depth=5, classifier__max_features=sqrt, classifier__min_samples_leaf=2, classifier__min_samples_split=2, classifier__n_estimators=100; total time=   0.9s\n",
      "[CV] END classifier__max_depth=20, classifier__max_features=auto, classifier__min_samples_leaf=2, classifier__min_samples_split=3, classifier__n_estimators=300; total time=   0.1s\n",
      "[CV] END classifier__max_depth=5, classifier__max_features=sqrt, classifier__min_samples_leaf=2, classifier__min_samples_split=2, classifier__n_estimators=100; total time=   0.7s\n",
      "[CV] END classifier__max_depth=20, classifier__max_features=auto, classifier__min_samples_leaf=2, classifier__min_samples_split=3, classifier__n_estimators=300; total time=   0.1s\n",
      "[CV] END classifier__max_depth=10, classifier__max_features=sqrt, classifier__min_samples_leaf=1, classifier__min_samples_split=3, classifier__n_estimators=200; total time=   2.0s\n",
      "[CV] END classifier__max_depth=20, classifier__max_features=sqrt, classifier__min_samples_leaf=2, classifier__min_samples_split=2, classifier__n_estimators=100; total time=   1.4s\n",
      "[CV] END classifier__max_depth=10, classifier__max_features=sqrt, classifier__min_samples_leaf=1, classifier__min_samples_split=3, classifier__n_estimators=200; total time=   2.0s\n",
      "[CV] END classifier__max_depth=20, classifier__max_features=sqrt, classifier__min_samples_leaf=2, classifier__min_samples_split=2, classifier__n_estimators=100; total time=   1.4s\n",
      "[CV] END classifier__max_depth=10, classifier__max_features=sqrt, classifier__min_samples_leaf=1, classifier__min_samples_split=3, classifier__n_estimators=200; total time=   2.3s\n",
      "[CV] END classifier__max_depth=5, classifier__max_features=sqrt, classifier__min_samples_leaf=2, classifier__min_samples_split=2, classifier__n_estimators=100; total time=   0.9s\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/tristan.kirscher/.venv/lib/python3.9/site-packages/sklearn/model_selection/_validation.py:547: FitFailedWarning: \n",
      "325 fits failed out of a total of 500.\n",
      "The score on these train-test partitions for these parameters will be set to nan.\n",
      "If these failures are not expected, you can try to debug them by setting error_score='raise'.\n",
      "\n",
      "Below are more details about the failures:\n",
      "--------------------------------------------------------------------------------\n",
      "70 fits failed with the following error:\n",
      "Traceback (most recent call last):\n",
      "  File \"/home/tristan.kirscher/.venv/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 895, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/home/tristan.kirscher/.venv/lib/python3.9/site-packages/sklearn/base.py\", line 1474, in wrapper\n",
      "    return fit_method(estimator, *args, **kwargs)\n",
      "  File \"/home/tristan.kirscher/.venv/lib/python3.9/site-packages/sklearn/pipeline.py\", line 475, in fit\n",
      "    self._final_estimator.fit(Xt, y, **last_step_params[\"fit\"])\n",
      "  File \"/home/tristan.kirscher/.venv/lib/python3.9/site-packages/sklearn/base.py\", line 1467, in wrapper\n",
      "    estimator._validate_params()\n",
      "  File \"/home/tristan.kirscher/.venv/lib/python3.9/site-packages/sklearn/base.py\", line 666, in _validate_params\n",
      "    validate_parameter_constraints(\n",
      "  File \"/home/tristan.kirscher/.venv/lib/python3.9/site-packages/sklearn/utils/_param_validation.py\", line 95, in validate_parameter_constraints\n",
      "    raise InvalidParameterError(\n",
      "sklearn.utils._param_validation.InvalidParameterError: The 'min_samples_split' parameter of RandomForestClassifier must be an int in the range [2, inf) or a float in the range (0.0, 1.0]. Got None instead.\n",
      "\n",
      "--------------------------------------------------------------------------------\n",
      "105 fits failed with the following error:\n",
      "Traceback (most recent call last):\n",
      "  File \"/home/tristan.kirscher/.venv/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 895, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/home/tristan.kirscher/.venv/lib/python3.9/site-packages/sklearn/base.py\", line 1474, in wrapper\n",
      "    return fit_method(estimator, *args, **kwargs)\n",
      "  File \"/home/tristan.kirscher/.venv/lib/python3.9/site-packages/sklearn/pipeline.py\", line 475, in fit\n",
      "    self._final_estimator.fit(Xt, y, **last_step_params[\"fit\"])\n",
      "  File \"/home/tristan.kirscher/.venv/lib/python3.9/site-packages/sklearn/base.py\", line 1467, in wrapper\n",
      "    estimator._validate_params()\n",
      "  File \"/home/tristan.kirscher/.venv/lib/python3.9/site-packages/sklearn/base.py\", line 666, in _validate_params\n",
      "    validate_parameter_constraints(\n",
      "  File \"/home/tristan.kirscher/.venv/lib/python3.9/site-packages/sklearn/utils/_param_validation.py\", line 95, in validate_parameter_constraints\n",
      "    raise InvalidParameterError(\n",
      "sklearn.utils._param_validation.InvalidParameterError: The 'max_features' parameter of RandomForestClassifier must be an int in the range [1, inf), a float in the range (0.0, 1.0], a str among {'sqrt', 'log2'} or None. Got 'auto' instead.\n",
      "\n",
      "--------------------------------------------------------------------------------\n",
      "150 fits failed with the following error:\n",
      "Traceback (most recent call last):\n",
      "  File \"/home/tristan.kirscher/.venv/lib/python3.9/site-packages/sklearn/model_selection/_validation.py\", line 895, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"/home/tristan.kirscher/.venv/lib/python3.9/site-packages/sklearn/base.py\", line 1474, in wrapper\n",
      "    return fit_method(estimator, *args, **kwargs)\n",
      "  File \"/home/tristan.kirscher/.venv/lib/python3.9/site-packages/sklearn/pipeline.py\", line 475, in fit\n",
      "    self._final_estimator.fit(Xt, y, **last_step_params[\"fit\"])\n",
      "  File \"/home/tristan.kirscher/.venv/lib/python3.9/site-packages/sklearn/base.py\", line 1467, in wrapper\n",
      "    estimator._validate_params()\n",
      "  File \"/home/tristan.kirscher/.venv/lib/python3.9/site-packages/sklearn/base.py\", line 666, in _validate_params\n",
      "    validate_parameter_constraints(\n",
      "  File \"/home/tristan.kirscher/.venv/lib/python3.9/site-packages/sklearn/utils/_param_validation.py\", line 95, in validate_parameter_constraints\n",
      "    raise InvalidParameterError(\n",
      "sklearn.utils._param_validation.InvalidParameterError: The 'max_features' parameter of RandomForestClassifier must be an int in the range [1, inf), a float in the range (0.0, 1.0], a str among {'log2', 'sqrt'} or None. Got 'auto' instead.\n",
      "\n",
      "  warnings.warn(some_fits_failed_message, FitFailedWarning)\n",
      "/home/tristan.kirscher/.venv/lib/python3.9/site-packages/sklearn/model_selection/_search.py:1051: UserWarning: One or more of the test scores are non-finite: [0.92611779        nan 0.92407395        nan        nan        nan\n",
      "        nan        nan        nan 0.92611792        nan 0.91295672\n",
      " 0.92198008        nan        nan        nan        nan        nan\n",
      " 0.9273143         nan        nan        nan        nan        nan\n",
      " 0.92616772 0.92746396        nan        nan 0.91380422 0.92661641\n",
      "        nan        nan        nan 0.92247862        nan        nan\n",
      "        nan 0.92392441        nan        nan        nan        nan\n",
      "        nan 0.92616769 0.92616773 0.9258686  0.92522057 0.9272146\n",
      " 0.92636715        nan 0.92681571 0.9130565         nan        nan\n",
      "        nan 0.91315618        nan        nan        nan        nan\n",
      "        nan        nan 0.9252206         nan 0.92541986        nan\n",
      " 0.92586853        nan        nan        nan 0.91275735        nan\n",
      "        nan 0.91310632        nan 0.92746386 0.92576889 0.92621763\n",
      "        nan        nan        nan        nan 0.91260781        nan\n",
      "        nan        nan        nan        nan        nan        nan\n",
      "        nan        nan 0.91240837        nan        nan 0.92621758\n",
      " 0.92576887 0.92706516 0.91245821        nan]\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-2 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-2 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-2 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-2 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-2 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-2 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-2 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-2 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-2 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomizedSearchCV(cv=5,\n",
       "                   estimator=Pipeline(steps=[(&#x27;preprocessor&#x27;,\n",
       "                                              ColumnTransformer(transformers=[(&#x27;num&#x27;,\n",
       "                                                                               Pipeline(steps=[(&#x27;imputer&#x27;,\n",
       "                                                                                                SimpleImputer())]),\n",
       "                                                                               Index([&#x27;age&#x27;, &#x27;birth_date&#x27;], dtype=&#x27;object&#x27;)),\n",
       "                                                                              (&#x27;cat&#x27;,\n",
       "                                                                               Pipeline(steps=[(&#x27;imputer&#x27;,\n",
       "                                                                                                SimpleImputer(fill_value=&#x27;missing&#x27;,\n",
       "                                                                                                              strategy=&#x27;constant&#x27;)),\n",
       "                                                                                               (&#x27;ordinal&#x27;,\n",
       "                                                                                                OrdinalEncoder(handle_unknown=&#x27;use_encoded_value&#x27;,\n",
       "                                                                                                               unk...\n",
       "       &#x27;employer&#x27;, &#x27;observation&#x27;],\n",
       "      dtype=&#x27;object&#x27;))])),\n",
       "                                             (&#x27;classifier&#x27;,\n",
       "                                              RandomForestClassifier())]),\n",
       "                   n_iter=100, n_jobs=-1,\n",
       "                   param_distributions={&#x27;classifier__max_depth&#x27;: [5, 10, 20,\n",
       "                                                                  None],\n",
       "                                        &#x27;classifier__max_features&#x27;: [&#x27;auto&#x27;,\n",
       "                                                                     &#x27;sqrt&#x27;],\n",
       "                                        &#x27;classifier__min_samples_leaf&#x27;: [1, 2,\n",
       "                                                                         3],\n",
       "                                        &#x27;classifier__min_samples_split&#x27;: [2, 3,\n",
       "                                                                          None],\n",
       "                                        &#x27;classifier__n_estimators&#x27;: [100, 200,\n",
       "                                                                     300]},\n",
       "                   random_state=42, verbose=2)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-9\" type=\"checkbox\" ><label for=\"sk-estimator-id-9\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;RandomizedSearchCV<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.model_selection.RandomizedSearchCV.html\">?<span>Documentation for RandomizedSearchCV</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>RandomizedSearchCV(cv=5,\n",
       "                   estimator=Pipeline(steps=[(&#x27;preprocessor&#x27;,\n",
       "                                              ColumnTransformer(transformers=[(&#x27;num&#x27;,\n",
       "                                                                               Pipeline(steps=[(&#x27;imputer&#x27;,\n",
       "                                                                                                SimpleImputer())]),\n",
       "                                                                               Index([&#x27;age&#x27;, &#x27;birth_date&#x27;], dtype=&#x27;object&#x27;)),\n",
       "                                                                              (&#x27;cat&#x27;,\n",
       "                                                                               Pipeline(steps=[(&#x27;imputer&#x27;,\n",
       "                                                                                                SimpleImputer(fill_value=&#x27;missing&#x27;,\n",
       "                                                                                                              strategy=&#x27;constant&#x27;)),\n",
       "                                                                                               (&#x27;ordinal&#x27;,\n",
       "                                                                                                OrdinalEncoder(handle_unknown=&#x27;use_encoded_value&#x27;,\n",
       "                                                                                                               unk...\n",
       "       &#x27;employer&#x27;, &#x27;observation&#x27;],\n",
       "      dtype=&#x27;object&#x27;))])),\n",
       "                                             (&#x27;classifier&#x27;,\n",
       "                                              RandomForestClassifier())]),\n",
       "                   n_iter=100, n_jobs=-1,\n",
       "                   param_distributions={&#x27;classifier__max_depth&#x27;: [5, 10, 20,\n",
       "                                                                  None],\n",
       "                                        &#x27;classifier__max_features&#x27;: [&#x27;auto&#x27;,\n",
       "                                                                     &#x27;sqrt&#x27;],\n",
       "                                        &#x27;classifier__min_samples_leaf&#x27;: [1, 2,\n",
       "                                                                         3],\n",
       "                                        &#x27;classifier__min_samples_split&#x27;: [2, 3,\n",
       "                                                                          None],\n",
       "                                        &#x27;classifier__n_estimators&#x27;: [100, 200,\n",
       "                                                                     300]},\n",
       "                   random_state=42, verbose=2)</pre></div> </div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-10\" type=\"checkbox\" ><label for=\"sk-estimator-id-10\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">estimator: Pipeline</label><div class=\"sk-toggleable__content fitted\"><pre>Pipeline(steps=[(&#x27;preprocessor&#x27;,\n",
       "                 ColumnTransformer(transformers=[(&#x27;num&#x27;,\n",
       "                                                  Pipeline(steps=[(&#x27;imputer&#x27;,\n",
       "                                                                   SimpleImputer())]),\n",
       "                                                  Index([&#x27;age&#x27;, &#x27;birth_date&#x27;], dtype=&#x27;object&#x27;)),\n",
       "                                                 (&#x27;cat&#x27;,\n",
       "                                                  Pipeline(steps=[(&#x27;imputer&#x27;,\n",
       "                                                                   SimpleImputer(fill_value=&#x27;missing&#x27;,\n",
       "                                                                                 strategy=&#x27;constant&#x27;)),\n",
       "                                                                  (&#x27;ordinal&#x27;,\n",
       "                                                                   OrdinalEncoder(handle_unknown=&#x27;use_encoded_value&#x27;,\n",
       "                                                                                  unknown_value=-1))]),\n",
       "                                                  Index([&#x27;firstname&#x27;, &#x27;occupation&#x27;, &#x27;civil_status&#x27;, &#x27;nationality&#x27;, &#x27;link&#x27;, &#x27;lob&#x27;,\n",
       "       &#x27;employer&#x27;, &#x27;observation&#x27;],\n",
       "      dtype=&#x27;object&#x27;))])),\n",
       "                (&#x27;classifier&#x27;, RandomForestClassifier())])</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-11\" type=\"checkbox\" ><label for=\"sk-estimator-id-11\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;preprocessor: ColumnTransformer<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.compose.ColumnTransformer.html\">?<span>Documentation for preprocessor: ColumnTransformer</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>ColumnTransformer(transformers=[(&#x27;num&#x27;,\n",
       "                                 Pipeline(steps=[(&#x27;imputer&#x27;, SimpleImputer())]),\n",
       "                                 Index([&#x27;age&#x27;, &#x27;birth_date&#x27;], dtype=&#x27;object&#x27;)),\n",
       "                                (&#x27;cat&#x27;,\n",
       "                                 Pipeline(steps=[(&#x27;imputer&#x27;,\n",
       "                                                  SimpleImputer(fill_value=&#x27;missing&#x27;,\n",
       "                                                                strategy=&#x27;constant&#x27;)),\n",
       "                                                 (&#x27;ordinal&#x27;,\n",
       "                                                  OrdinalEncoder(handle_unknown=&#x27;use_encoded_value&#x27;,\n",
       "                                                                 unknown_value=-1))]),\n",
       "                                 Index([&#x27;firstname&#x27;, &#x27;occupation&#x27;, &#x27;civil_status&#x27;, &#x27;nationality&#x27;, &#x27;link&#x27;, &#x27;lob&#x27;,\n",
       "       &#x27;employer&#x27;, &#x27;observation&#x27;],\n",
       "      dtype=&#x27;object&#x27;))])</pre></div> </div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-12\" type=\"checkbox\" ><label for=\"sk-estimator-id-12\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">num</label><div class=\"sk-toggleable__content fitted\"><pre>Index([&#x27;age&#x27;, &#x27;birth_date&#x27;], dtype=&#x27;object&#x27;)</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-13\" type=\"checkbox\" ><label for=\"sk-estimator-id-13\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;SimpleImputer<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.impute.SimpleImputer.html\">?<span>Documentation for SimpleImputer</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>SimpleImputer()</pre></div> </div></div></div></div></div></div></div><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-14\" type=\"checkbox\" ><label for=\"sk-estimator-id-14\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">cat</label><div class=\"sk-toggleable__content fitted\"><pre>Index([&#x27;firstname&#x27;, &#x27;occupation&#x27;, &#x27;civil_status&#x27;, &#x27;nationality&#x27;, &#x27;link&#x27;, &#x27;lob&#x27;,\n",
       "       &#x27;employer&#x27;, &#x27;observation&#x27;],\n",
       "      dtype=&#x27;object&#x27;)</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-15\" type=\"checkbox\" ><label for=\"sk-estimator-id-15\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;SimpleImputer<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.impute.SimpleImputer.html\">?<span>Documentation for SimpleImputer</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>SimpleImputer(fill_value=&#x27;missing&#x27;, strategy=&#x27;constant&#x27;)</pre></div> </div></div><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-16\" type=\"checkbox\" ><label for=\"sk-estimator-id-16\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;OrdinalEncoder<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.preprocessing.OrdinalEncoder.html\">?<span>Documentation for OrdinalEncoder</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>OrdinalEncoder(handle_unknown=&#x27;use_encoded_value&#x27;, unknown_value=-1)</pre></div> </div></div></div></div></div></div></div></div></div><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-17\" type=\"checkbox\" ><label for=\"sk-estimator-id-17\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;RandomForestClassifier<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.ensemble.RandomForestClassifier.html\">?<span>Documentation for RandomForestClassifier</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>RandomForestClassifier()</pre></div> </div></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "RandomizedSearchCV(cv=5,\n",
       "                   estimator=Pipeline(steps=[('preprocessor',\n",
       "                                              ColumnTransformer(transformers=[('num',\n",
       "                                                                               Pipeline(steps=[('imputer',\n",
       "                                                                                                SimpleImputer())]),\n",
       "                                                                               Index(['age', 'birth_date'], dtype='object')),\n",
       "                                                                              ('cat',\n",
       "                                                                               Pipeline(steps=[('imputer',\n",
       "                                                                                                SimpleImputer(fill_value='missing',\n",
       "                                                                                                              strategy='constant')),\n",
       "                                                                                               ('ordinal',\n",
       "                                                                                                OrdinalEncoder(handle_unknown='use_encoded_value',\n",
       "                                                                                                               unk...\n",
       "       'employer', 'observation'],\n",
       "      dtype='object'))])),\n",
       "                                             ('classifier',\n",
       "                                              RandomForestClassifier())]),\n",
       "                   n_iter=100, n_jobs=-1,\n",
       "                   param_distributions={'classifier__max_depth': [5, 10, 20,\n",
       "                                                                  None],\n",
       "                                        'classifier__max_features': ['auto',\n",
       "                                                                     'sqrt'],\n",
       "                                        'classifier__min_samples_leaf': [1, 2,\n",
       "                                                                         3],\n",
       "                                        'classifier__min_samples_split': [2, 3,\n",
       "                                                                          None],\n",
       "                                        'classifier__n_estimators': [100, 200,\n",
       "                                                                     300]},\n",
       "                   random_state=42, verbose=2)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# use random search to find the best hyperparameters for the model\n",
    "\n",
    "# define the hyperparameters grid\n",
    "param_grid = {\n",
    "    'classifier__n_estimators': [100, 200, 300],\n",
    "    'classifier__max_features': ['auto', 'sqrt'],\n",
    "    'classifier__max_depth': [5, 10, 20, None],\n",
    "    'classifier__min_samples_split': [2, 3, None],\n",
    "    'classifier__min_samples_leaf': [1, 2, 3],\n",
    "}\n",
    "\n",
    "# define the RandomizedSearchCV\n",
    "search = RandomizedSearchCV(model, param_distributions=param_grid, n_iter=100, cv=5, verbose=2, random_state=42, n_jobs=-1)\n",
    "\n",
    "# fit the model on the training data\n",
    "search.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'classifier__n_estimators': 300,\n",
       " 'classifier__min_samples_split': 2,\n",
       " 'classifier__min_samples_leaf': 2,\n",
       " 'classifier__max_features': 'sqrt',\n",
       " 'classifier__max_depth': 20}"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# get the best hyperparameters\n",
    "search.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.97      0.94      0.96      3927\n",
      "           1       0.80      0.91      0.85      1088\n",
      "\n",
      "    accuracy                           0.93      5015\n",
      "   macro avg       0.89      0.92      0.90      5015\n",
      "weighted avg       0.94      0.93      0.93      5015\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# make predictions on the testing data using the best model\n",
    "y_pred = search.predict(X_test)\n",
    "\n",
    "# evaluate the model\n",
    "print(classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot confusion matrix\n",
    "plot_confusion_matrix(search, y_pred=y_pred, y_test=y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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KU/iW9HxP/q8+73udyNBwjC9ROdS2bVs0bdoU8+bNQ1ZWFqpUqYK2bdvixx9/xN27dwvt/+DBA812r169cObMGfz666+F9nvS+9anTx/cvn0b4eHhhfbJzMzUrE5QHE9PTzRo0ABr1qzBmjVr4OTkVKAIlcvl6NWrF9avX1/kL+an82qrefPm8Pf3x7JlywosnfbEpEmTcOXKFYwbN65QT9zGjRsLjNE9duwYjh49qik6tHmdn0UulxfqgQ0LC4NKpSrQ9mTN3/8WxC/D19cXlStXRnh4OPLy8jTtUVFRmuEQz+Lq6oqhQ4di+/btCAsLK3S/Wq3GnDlzcOvWLa1yPekR/u+wj2XLlun8GB07doSVlRVCQ0ORlZVV4L6nH2thYVHk0JOX/f4oikqlKnSuKlWqoFq1agWWSCsu0385ODigdevWWLp0KW7cuFHgPl31/hOVR+zxJSqnPv30U/Tu3RvLly/HsGHDsGDBArRs2RINGjTA0KFDUbNmTdy7dw+HDx/GrVu3cObMGc3j1q1bh969e2Pw4MHw8fHBo0ePsGnTJixevBje3t4YMGAAfvnlFwwbNgx79uxBixYtoFKpcOnSJfzyyy+a9VOfpW/fvpg6dSpMTU0xZMiQQheb+Prrr7Fnzx74+flh6NChqFu3Lh49eoSYmBjs3LkTjx49euHXZsWKFejQoQPefvttBAQEoFWrVsjOzsaGDRuwd+9e9O3bF59++mmhx73yyito2bIlPvzwQ2RnZ2PevHmoXLkyxo0bp9mnpK/zs3Tt2hUrV66EjY0N6tati8OHD2Pnzp2oXLlygf0aNmwIuVyOb775BsnJyVAqlZo1al+UiYkJpk+fjlGjRqF9+/bo06cPrl+/juXLl8PDw6NEPYpz5szBtWvX8NFHH2HDhg3o2rUr7OzscOPGDaxduxaXLl0q0MNfEh07doSJiQm6deuGDz74AGlpaQgPD0eVKlWK/CPjZY5hbW2NuXPn4r333kOTJk0QEBAAOzs7nDlzBhkZGZp1qH18fLBmzRqEhISgSZMmsLS0RLdu3XTy/fFfqampcHFxwTvvvANvb29YWlpi586dOH78eIFPBorLVJQffvgBLVu2ROPGjfH+++/D3d0d169fx9atW3H69Gmt8hFVGJKsJUFEJfJkSazjx48Xuk+lUgkPDw/h4eGhWS7r2rVrYuDAgaJq1apCoVAIZ2dn0bVrV7Fu3boCj3348KEYOXKkcHZ2FiYmJsLFxUUEBQUVWFosJydHfPPNN6JevXpCqVQKOzs74ePjIz7//HORnJys2e+/y5k98ddffwkAAoA4cOBAkc/v3r17YsSIEcLV1VUoFApRtWpV0aFDB7FkyRLNPk+W6Vq7dq1Wr11qaqqYPn26qFevnjAzMxNWVlaiRYsWYvny5YWWc3qyFNTs2bPFnDlzhKurq1AqlaJVq1bizJkzhY5dktf5We/d48ePRXBwsLC3txeWlpaiU6dO4tKlS0W+luHh4aJmzZpCLpcXWNqsuOXM/vs6FbXMlRBC/PDDD6JGjRpCqVSKpk2bioMHDwofHx/xxhtvlODVFSIvL0/89NNPolWrVsLGxkYoFApRo0YNERwcXGCpsyfLmT148KDA45+8PvHx8Zq2TZs2CS8vL2Fqairc3NzEN998I5YuXVpovxo1aog333yzyFwlPcaTfZs3by7MzMyEtbW1aNq0qfjf//6nuT8tLU0EBAQIW1tbAaDAMmIl/f4AUOwSZXhqObPs7Gzx6aefCm9vb2FlZSUsLCyEt7e3WLhwYYHHFJepuPf53LlzokePHsLW1laYmpqKV199VUyZMqXIPESGQCYEP/MgIsN2/fp1uLu7Y/bs2Rg7dqzUcSShVqvh4OCAnj17FvkRPhFRRcAxvkREBiYrK6vQOM8VK1bg0aNHz71kMRFRecYxvkREBubIkSMYM2YMevfujcqVKyMmJgY///wz6tevj969e0sdj4io1LDwJSIyMG5ubnB1dcUPP/yAR48eoVKlShg4cCC+/vrrAld9IyKqaDjGl4iIiIgMAsf4EhEREZFBYOFLRERERAbB4Mb4qtVq3LlzB1ZWVqVyOVAiIiIiejlCCKSmpqJatWqFLoD0Mgyu8L1z5w5cXV2ljkFEREREz3Hz5k24uLjo7HgGV/haWVkByH8hra2tJU5DRERERP+VkpICV1dXTd2mKwZX+D4Z3mBtbc3Cl4iIiEiP6XpYKie3EREREZFBYOFLRERERAaBhS8RERERGQQWvkRERERkEFj4EhEREZFBYOFLRERERAaBhS8RERERGQQWvkRERERkEFj4EhEREZFBYOFLRERERAaBhS8RERERGQQWvkRERERkEFj4EhEREZFBYOFLRERERAaBhS8RERERGQRJC9/9+/ejW7duqFatGmQyGTZu3Pjcx+zduxeNGzeGUqnEK6+8guXLl5d6TiIiIiIq/yQtfNPT0+Ht7Y0FCxaUaP/4+Hi8+eabaNeuHU6fPo3Ro0fjvffew7Zt20o5KRERERGVd8ZSnrxz587o3LlzifdfvHgx3N3dMWfOHACAp6cnDhw4gLlz56JTp06lFZOIiIjopTxKz8Gx+EcAhNRRyoX0tNRSOa6kha+2Dh8+DH9//wJtnTp1wujRo4t9THZ2NrKzszW3U1JSSiseERGRXkhMy0aequQFVmJaNqLPJUAmK8VQFcia4zehUmtXwD5MzymlNBWPUOVB5JXO61WuCt+EhAQ4OjoWaHN0dERKSgoyMzNhZmZW6DGhoaH4/PPPyyoiEZFeuZOUiYt3DfMP/lVHbxhk39ruS/eljkDP4G5vgcoWJlLH0Et5OVk4uz4MGQ8T0DR4Om6WwjnKVeH7IiZMmICQkBDN7ZSUFLi6ukqYiMgwpGfnITNXJXUMrajVApvO3EFGTslzn7mZhD//SoSdhaIUk72YPJVgL5OBU8hL3oWbqxKo72wNn+p2pZio4qhR2QIta9lr9RhHK1PYmOvfzwp9EBMTg4CAwYi/fBkAMLy+wOZSOE+5KnyrVq2Ke/fuFWi7d+8erK2ti+ztBQClUgmlUlkW8YjK1MW7KXiQmo307DxEHv0blkr9+Xa+ej8N1x6kSx2jTN1LyX7+ThLycrGBzNA+xxYC2XlqDG7hLnWSMlfZ0gRtX60CuZGBvedU7qjVasyZMweTJk1Cbm4uqlWrhoiICDRt2rRUzqc/vylLoFmzZvj9998LtO3YsQPNmjWTKBGVd7kqNcatO4v9Vx7AzEQudZwSu/U4U+oIFV6AX/US75uTp0bn+lXhaG1aioleXE0HC5iblKsf90RkAG7duoWgoCDs3r0bANCjRw+Eh4ejcuXKpTYnS9KfhGlpabh69armdnx8PE6fPo1KlSqhevXqmDBhAm7fvo0VK1YAAIYNG4b58+dj3LhxGDx4MHbv3o1ffvkFW7duleopUDmUmpWL2NvJOH0zCbOiL/97RzntoKzrZI3sPBVqVLZAB88qUsfRMDeRo3N9J5gqys8fFEREVDaEEHjnnXdw9OhRmJub44cffsDgwYNL/ZMpSQvfEydOoF27dprbT8biBgUFYfny5bh79y5u3Lihud/d3R1bt27FmDFj8P3338PFxQU//fQTlzIzINl5KqjVQFJmDradS0CelrNqVx29gbjEoivclUOawsq0/Iy9MjaSoa6TNYz4USYREZUzMpkMYWFhGD16NJYvX45atWqVzXmFEAY16TUlJQU2NjZITk6GtbW11HEMXnaeCudup+BZ/w3/d+wm1EJgy9k7yNVieZ7ncbIxhUot8Plb9dC5gZPOjktERESFHTlyBFeuXMHAgQM1bUKIInt5S6te46AvemkqtcAf5+7iYVrJZ4//79gN5KrULz0BytnWDE3ctJuBrBLAlK6eqGKln+MxiYiIKpK8vDzMnDkTM2bMgFwuR8OGDeHl5QUAZT7ploUvaUWtFgj94yKSM3M1bb+cuPXSxzWS5S8NUxQhBHLy1Ahu4Q4zEzm6NHCCqcIIxkZGMDGW9KrbRERE9AxxcXEYMGAADh06BADo27cvqlcv+eRhXWPhS8+kVgvsvnQf91Oz8dvp2zga/+iZ+7/pVfIhA9m5agS3cIOLnVmxRS8RERGVP0IIREZGYsSIEUhNTYW1tTUWLlyIwMBASXOx8KUChBD4bscVPErPwZlbSTh3u/jlRMa98apm28FSiXd8XAxvnVAiIiIqQAiBQYMGaVblatGiBSIjI+Hm5iZtMLDwpX/EPUjDyiN/Y9nB68Xu07GuI9Jz8jD+DU80cLEpu3BERERUbshkMnh6ekIul2P69OkYP348jI31o+Tkqg4GKCtXhW3nE/Dx6tMwkgHGRkbIUakL7TfGvzYA4E2vqnililVZxyQiIqJyIicnB/fu3YOrqysAQKVS4fz585pJbNriqg70Um4nZeLDyJNQyI1w8u/Hmna1QIGit6GrLQY2q4HuDZ25PiwRERE91+XLlxEYGIjMzEycOHECZmZmkMvlL1z0liYWvhVUZo4KCSlZUKkF1p28hcX7rhW533st3TG4pTtkMqCyhZKrJBAREVGJCCHw008/YfTo0cjIyICdnR0uXLgAHx8fqaMVi4VvBXEvJQvHr+evuHDi+mMsP3S92H0XBTZGvWo2qF7ZvIzSERERUUWSmJiIoUOHYuPGjQCA9u3bIyIiAi4uLtIGew4WvuWYEAIbT9/Gt9uu4HZSZpH7WCmNkatWQyE3wrA2HnivlTuUxvIyTkpEREQVxfbt2zFo0CDcvXsXCoUCoaGhGDNmDIyM9P9TYxa+5ZBKLXA07iECfjpa6L76ztawMDFGcmYuvuheH03cKkmQkIiIiCoiIQRmzZqFu3fvwtPTE6tWrULDhg2ljlViLHzLGbVawGPi74Xag1u4YVT7WqhkYSJBKiIiIjIEMpkMy5Ytw/fff48ZM2bA3Lx8DZtk4VvOjPrfqQK336hXFYv6N+aFI4iIiEjnhBCYP38+4uPj8d133wEAXF1d8e2330qc7MWw8C1Hfjt9G1tj72pun/u8EyyVfAuJiIhI9xISEhAcHIzo6GgAwDvvvIPmzZtLnOrl6P8oZNL4ePVpzfaa919j0UtERESlYvPmzWjQoAGio6NhamqKsLAwNGvWTOpYL42VUznx9EUnJnSuA7+alSVMQ0RERBVRRkYGxo4di0WLFgEAvLy8sGrVKtSrV0/iZLrBwrecGLrihGZ7UAs36YIQERFRhSSEQMeOHXHw4EEAwCeffIKvvvoKSqVS4mS6w8K3HFCrBR6l5wAAatpbcB1eIiIi0jmZTIYxY8YgPj4eERER8Pf3lzqSznGMbzkw5pfTmu0lA32lC0JEREQVyq1bt/Dnn39qbvfq1QtXrlypkEUvwMK3XPjt9B3N9itVLCVMQkRERBXF2rVr4eXlhZ49eyIhIUHTbmFhIWGq0sXCV8+dv5Os2f6Jvb1ERET0klJTUxEcHIw+ffrg8ePHcHd3R2ZmptSxygQLXz33yS9nNNv+dR0lTEJERETl3ZEjR9CwYUMsX74cMpkMkyZNwsGDB+Hu7i51tDLByW167NC1RFxKSAUA1Khcvi4JSERERPpDCIEvvvgCM2bMgEqlQvXq1REZGYlWrVpJHa1MscdXjwWEH9VsrxtWvq+UQkRERNKRyWS4efMmVCoVAgICcObMGYMregH2+OotIYRme7R/LThYVZw19IiIiKj0CSGQlZUFMzMzAMDcuXPRqVMnvPPOOxInkw57fPXMo/Qc/HrqFtwn/K5pe7dJdQkTERERUXmTlJSEgIAAvP3221Cr1QAAS0tLgy56Afb46pVH6Tlo/MWOAm1VrJRwtGZvLxEREZXM/v37MWDAANy4cQNyuRzHjx+Hn5+f1LH0Ant89cjSA/GabbfK5vigdU0cndgBMplMwlRERERUHuTk5GDixIlo27Ytbty4AQ8PDxw8eJBF71PY46tH5u+5qtne+2k7CZMQERFReXL58mUEBgbi5MmTAIDBgwdj3rx5sLKykjiZfmHhq4da1bKXOgIRERGVE0IIBAQEICYmBnZ2dggPD0evXr2kjqWXONRBTzxOz9FsD2zmJl0QIiIiKldkMhmWLFmCN954A2fPnmXR+wwsfPXE5rN3NNsd6lSRMAkRERHpu+3btyM8PFxz28fHB3/88QdcXFwkTKX/WPjqiam/nddsGxlxMhsREREVlpWVhTFjxqBTp04YOXIkzp49K3WkcoVjfPXAkbiHmu1ejfmXGhERERV27tw5BAQEIDY2FgDw3nvv4ZVXXpE4VfnCHl89sPfyA832nD7eEiYhIiIifSOEQFhYGHx9fREbGwsHBwds3rwZCxYsgLm5udTxyhX2+OoB43+GNnA1ByIiInqaEAI9evTAb7/9BgDo3Lkzli1bBkdHR4mTlU/s8dUDey7fBwBUr8S/2oiIiOhfMpkMLVq0gKmpKcLCwrB161YWvS+Bha8ecLO3AABk56klTkJERERSy8jIQFxcnOb2J598gtjYWIwcOZJXc31JLHz1wN2kTABAA2cbiZMQERGRlGJiYuDj44M333wTGRkZAAAjIyNOYtMRFr564K97aQCArFyVxEmIiIhICmq1GrNmzcJrr72GS5cuITk5uUCvL+kGJ7dJLObGY6Rm5wH4d8gDERERGY5bt25h4MCB2LNnDwCgR48eCA8PR+XKlSVOVvGwx1diw1ae1Gy3fIWrOhARERmStWvXwsvLC3v27IG5uTnCw8Oxfv16Fr2lhD2+Erufmg0A6N6wGiyUfDuIiIgMhRACS5YswePHj+Hr64uoqCjUrl1b6lgVGnt8JXT+TrJme1ALdwmTEBERUVkRQgDIX6ps+fLl+Pzzz3Ho0CEWvWWAha+Efjl+U7Pt7cIVHYiIiCqyvLw8zJgxA6NGjdK0OTs7Y+rUqVAoFBImMxz8bF1COy/e12xzXT4iIqKKKz4+Hv3798ehQ4cAAEFBQWjSpInEqQwPe3wlolYL3P5n/d63vKtJnIaIiIhKgxACkZGR8Pb2xqFDh2BtbY3IyEgWvRJhj69EDsc91GyPaMdFqYmIiCqapKQkfPjhh1i9ejUAoEWLFoiMjISbm5u0wQwYC1+JBP50VLP9alUrCZMQERGRrgkh0KFDB8TExEAul2P69OkYP348jI1ZekmJQx0kkJKVq9muw6KXiIiowpHJZJgyZQpeeeUVHDx4EJMnT2bRqwdY+Epg8d5rmu0Nw5tLmISIiIh05cqVK9i1a5fmdvfu3XHu3Dn4+flJmIqexsJXAgufKnzNTfjXHxERUXkmhEB4eDgaNWqEPn364M6dO5r7lEqlhMnov1h1SejjDrWkjkBEREQvITExEUOHDsXGjRsBAK+99pq0geiZ2ONbxrLzVJrttxpyGTMiIqLyaseOHfDy8sLGjRuhUCgwe/Zs7NixA9Wq8fe7vmKPbxlLyczTbLtXtpAwCREREb0IIQTGjh2L7777DgDg6emJqKgoNGrUSOJk9Dzs8S1jv52+rdk2MuLV2oiIiMobmUyG9PR0AMDw4cNx4sQJFr3lBHt8y9iNRxlSRyAiIiItCSGQmpoKa2trAMCcOXPQs2dPdOzYUeJkpA32+JaxyCN/AwCCmtWQOAkRERGVREJCAt5880307NkTarUaAGBhYcGitxxij28ZU4uC/xIREZH+2rJlCwYPHowHDx7A1NQUZ86c4bCGcow9vmXo3O1kzfYb9atKmISIiIieJSMjA8OHD0e3bt3w4MEDeHl5cSxvBcDCtwzFJaZrtn3d7CRMQkRERMWJiYmBj48PFi1aBAAICQnBsWPHUK9ePYmT0cviUIcydPL6IwDAazUrQWkslzgNERER/ZdarcbgwYNx6dIlODk5ISIiAq+//rrUsUhH2ONbhlKz89fwvZucJXESIiIiKoqRkRGWLVuGPn36IDY2lkVvBcPCtwxdTkgFAHi52EobhIiIiDTWrVuHBQsWaG43atQIa9asQeXKlSVMRaWBQx3K0Pk7KQCAajamEichIiKi1NRUfPzxx1i2bBkUCgVat26NBg0aSB2LShEL3zIycOkxzfarVa0kTEJERERHjhxB//79ce3aNchkMnz66aeoU6eO1LGolLHwLSP7rzzQbHeo4yhhEiIiIsOVl5eHmTNnYsaMGVCpVKhevTpWrlyJ1q1bSx2NygAL3zIgxL9Xq1g3rBlszBUSpiEiIjJMarUaHTt2xJ49ewAA/fr1w8KFC2FrayttMCoznNxWBjbE3NZsV7IwkTAJERGR4TIyMkLXrl1hbW2NyMhIrFq1ikWvgWHhW8rSsvPwydozmtvu9hYSpiEiIjIsSUlJuHz5sub26NGjceHCBQQGBkqYiqTCwreU/f3w36u1Te9WFzKZTMI0REREhmP//v3w9vbG22+/jfT0/N/HRkZGcHZ2ljgZSYWFbxmpYqXEoBbuUscgIiKq8HJzczFp0iS0bdsWN27cQF5eHm7fvv38B1KFx8K3lKnV+f+yo5eIiKj0XblyBc2bN8fMmTMhhMDgwYNx6tQp1K5dW+popAdY+JaydSdvAgAyslUSJyEiIqq4hBAIDw9Ho0aNcOLECdjZ2WHt2rX4+eefYWXF9fMpH5czKyOp2XlSRyAiIqqwhBBYt24dMjIy0L59e0RERMDFxUXqWKRnWPiWsojDfwMAejXmNx8REZGuCSEgk8lgZGSE5cuXY82aNfjoo49gZMQPtakw/q8oRZcTUjXblSx40QoiIiJdycrKwpgxY/DBBx9o2pycnDB69GgWvVQsyf9nLFiwAG5ubjA1NYWfnx+OHTv2zP3nzZuHV199FWZmZnB1dcWYMWOQlZVVRmm1s+XsHc32Jx1flTAJERFRxXHu3Dk0bdoU8+bNQ3h4OE6fPi11JConJC1816xZg5CQEEybNg0xMTHw9vZGp06dcP/+/SL3X7VqFcaPH49p06bh4sWL+Pnnn7FmzRpMnDixjJOXjLVpfi9vjcrmMFXIJU5DRERUvgkhEBYWBl9fX8TGxsLBwQGbN29Gw4YNpY5G5YSkhe93332HoUOHIjg4GHXr1sXixYthbm6OpUuXFrn/oUOH0KJFCwQEBMDNzQ0dO3ZEv379nttLLJWVR/LH9/pUt5M4CRERUfmWkJCALl264KOPPkJ2djY6d+6M2NhYdO3aVepoVI5IVvjm5OTg5MmT8Pf3/zeMkRH8/f1x+PDhIh/TvHlznDx5UlPoxsXF4ffff0eXLl2KPU92djZSUlIKfJWVm48zAAAXnxrrS0RERNpRq9Xw9/dHdHQ0TE1NERYWhq1bt8LR0VHqaFTOSFb4JiYmQqVSFfpP6+joiISEhCIfExAQgBkzZqBly5ZQKBTw8PBA27ZtnznUITQ0FDY2NpovV1dXnT6P4jxMy4YQ+dvD2tQsk3MSERFVREZGRggNDYW3tzdOnDiBkSNHQsYrQ9ELkHxymzb27t2LmTNnYuHChYiJicGGDRuwdetWfPHFF8U+ZsKECUhOTtZ83bx5s0yyXnqql/f1uvyLlIiISBsxMTGIjo7W3O7WrRtOnjyJevXqSZiKyjvJ1vG1t7eHXC7HvXv3CrTfu3cPVatWLfIxU6ZMwYABA/Dee+8BABo0aID09HS8//77mDRpUpHLlyiVSiiVSt0/gef4ePUpAICTjSnMTbhcMhERUUmo1Wp8++23mDx5MiwtLXH27FnNhSjkck4Up5cjWY+viYkJfHx8sGvXLk2bWq3Grl270KxZsyIfk5GRUai4ffJNIJ6MK9ADF++mIDEtBwBgY8b1e4mIiEri5s2b8Pf3x2effYbc3Fy0bdsWZmZmUseiCkTSrsiQkBAEBQXB19dXsx5feno6goODAQADBw6Es7MzQkNDAeR/zPHdd9+hUaNG8PPzw9WrVzFlyhR069ZNr/4KPHUjSbMd+Z6fdEGIiIjKibVr1+KDDz7A48ePYW5ujh9++AGDBw/mWF7SKUkL3759++LBgweYOnUqEhIS0LBhQ0RHR2smvN24caNAD+/kyZMhk8kwefJk3L59Gw4ODujWrRu++uorqZ5Ckc7fSQYA1He2hr1l2Q+zICIiKi/UajXee+89LFu2DADQpEkTREVFoVatWhIno4pIJvRpjEAZSElJgY2NDZKTk2FtbV0q5+i35AgOxz2Ev6cjfgryLZVzEBERVRQjRozA4sWLMWHCBEybNg0KBYcJGrrSqtc466oUPM7IH9/7bpOyWTqNiIioPMnLy0NKSgoqVaoEAJg9ezb69+9f7BwfIl0pV8uZlTe8TDEREVFB8fHxaNOmDXr27AmVSgUAMDc3Z9FLZYKFbylIzcqTOgIREZFeEUJg5cqV8Pb2xqFDh3Dq1ClcvHhR6lhkYFj4loLbSZkAALVhDZ8mIiIqUlJSEgICAjBw4ECkpqaiRYsWOHPmDOrXry91NDIwLHxLgUKev/SKix3XHiQiIsO2b98+eHl5YfXq1ZDL5fjiiy+wd+9euLm5SR2NDBAnt+mYEAK5qvyeXl6xjYiIDJlarcZHH32EmzdvwsPDA1FRUfDz4/r2JB32+OrY/r8SNdu8ahsRERkyIyMjrFixAkOHDsXp06dZ9JLk2CWpYysPX9dsm5lwVQciIjIcQgj89NNPSEtLw5gxYwAA3t7eWLJkicTJiPKx8NUxa/byEhGRAUpMTMTQoUOxceNGGBsbo2PHjqhXr57UsYgKYOGrY4p/LrE82p+XWiQiIsOwfft2DBo0CHfv3oVCoUBoaCg8PT2ljkVUCAvfUqKQc/g0ERFVbFlZWZgwYQLmzZsHAPD09MSqVavQsGFDSXMRFYeFr45tOXtH6ghERESlTqVSoXXr1jh+/DgAYMSIEZg1axbMzc0lTkZUPBa+OnT+TjLSc/Ivv/ggNVviNERERKVHLpcjMDAQ169fx9KlS9G1a1epIxE9Fz+P16Exa05rtoe18ZAuCBERUSlISEjAuXPnNLdHjRqFCxcusOilcoOFrw5duZcGAOjesBqq2phKnIaIiEh3Nm/ejAYNGqBHjx5IS8v/fWdkZAR7e3uJkxGVHAtfHblwJ0Wz3a9pdQmTEBER6U5GRgaGDx+Ot956C4mJiTA3N0diYuLzH0ikh1j46sikjbGa7abulSRMQkREpBsxMTHw8fHBokWLAACffPIJjh07Bjc3N2mDEb0gFr46kp6dBwCoZmMKmUwmcRoiIqIXp1arMWvWLLz22mu4dOkSnJycsGPHDnz77bdQKpVSxyN6YSx8dcTon2L3615eEichIiJ6OTKZDHv27EFubi569OiB2NhY+Pv7Sx2L6KVxOTMduZSQCuDfApiIiKi8ycvLg7GxMWQyGZYtW4bo6GgEBQXxk0yqMNjjqwMJyVmabTsLhYRJiIiItJeamorg4GC8//77mraqVati0KBBLHqpQmHhqwM5eWrNdl0nawmTEBERaefIkSNo2LAhli9fjoiICJw/f17qSESlhoWvDlmYyPmXMRERlQt5eXmYMWMGWrZsibi4OFSvXh179+5FvXr1pI5GVGo4xpeIiMjAxMfHo3///jh06BAAoF+/fli4cCFsbW2lDUZUylj46kB6Tp7UEYiIiEpEpVKhU6dO+Ouvv2BtbY2FCxciMDBQ6lhEZYJDHXTg1uNMAEB6jkriJERERM8ml8sxb948tGzZEmfOnGHRSwaFPb46IP/nz4fqlcylDUJERFSE/fv3Izk5Gd26dQMAdOnSBZ07d+a8FDI47PHVgdtJ+cuZ2ZpzKTMiItIfOTk5mDhxItq2bYuBAwfi5s2bmvtY9JIhYo+vDny/8y8AwMO0HImTEBER5bt8+TICAwNx8uRJAEDPnj05eY0MHnt8dSAxLRsAUNvRUuIkRERk6IQQCA8PR+PGjXHy5EnY2dlh3bp1+Pnnn2FlZSV1PCJJscdXh3o0dpE6AhERGTCVSoXevXvj119/BQC0b98eERERcHHh7ycigD2+L+16Yrpmm1dtIyIiKcnlcri6ukKhUGD27NnYsWMHi16ip7DH9yXdT83WbHs4WEiYhIiIDFFWVhZSUlJQpUoVAMDXX3+NIUOGwMvLS+JkRPqHPb46UtPBgjNkiYioTJ0/fx5+fn7o3bs3VKr8teTNzMxY9BIVg4UvERFROSOEQFhYGHx8fHD27FlcvHgR165dkzoWkd5j4UtERFSOJCQkoEuXLvjoo4+QnZ2Nzp07IzY2FrVr15Y6GpHeY+FLRERUTmzevBkNGjRAdHQ0TE1NERYWhq1bt8LR0VHqaETlAie3ERERlQN5eXmYNGkSEhMT4eXlhVWrVqFevXpSxyIqV9jjS0REVA4YGxsjKioKn376KY4dO8ail+gFsMf3JeWp1FJHICKiCkitVmPOnDlQq9X47LPPAAANGjTArFmzJE5GVH6x8H1JF+6mAAAyslUSJyEioori1q1bCAoKwu7duyGXy/H222+jTp06UsciKvc41OElmZvk/+2Qy55fIiLSgbVr18LLywu7d++Gubk5Fi9ejFdffVXqWEQVAnt8X1JCciYAoHENO4mTEBFReZaamoqPP/4Yy5YtAwD4+voiKiqKy5QR6RAL35d0LyX/ksUPnrp0MRERkTby8vLQvHlznDt3DjKZDBMnTsS0adOgUCikjkZUoXCow0uyNM3/26GmvYXESYiIqLwyNjbG+++/j+rVq2Pfvn348ssvWfQSlQIWvjriaGMqdQQiIipH4uPjcfr0ac3tkSNHIjY2Fq1atZIuFFEFx8L3JQkhdQIiIipPhBCIjIyEt7c3evXqhdTUVACATCaDtbW1xOmIKjYWvi9px8UEACyAiYjo+ZKSkhAQEIABAwYgNTUVTk5OmsKXiEofC9+X5FY5f2xvVi7X8SUiouLt378f3t7eWL16NeRyOb744gvs3bsX1apVkzoakcHgqg4vQa0W+POvRABAvWr8eIqIiArLy8vD1KlT8fXXX0MIAQ8PD0RFRcHPz0/qaEQGhz2+L+Gv+2ma7eqVzCVMQkRE+koul+PMmTMQQmDw4ME4deoUi14iibDH9yUkZ+Zqtv1qVpYwCRER6RMhBHJycqBUKiGTybBs2TIcOHAAPXv2lDoakUFjj+9LuPkoQ+oIRESkZx4+fIhevXrh/fff17RVqVKFRS+RHnipwjcrK0tXOcolhXH+y+fOi1cQERGAHTt2oEGDBvj111/xv//9D1euXJE6EhE9RevCV61W44svvoCzszMsLS0RFxcHAJgyZQp+/vlnnQfUZ5cTUgAATrx4BRGRQcvKykJISAg6duyIu3fvwtPTE0ePHkXt2rWljkZET9G68P3yyy+xfPlyzJo1CyYmJpr2+vXr46efftJpOH1nJJMBKDjJjYiIDMv58+fh5+eHuXPnAgCGDx+OEydOoFGjRhInI6L/0rrwXbFiBZYsWYLAwEDI5XJNu7e3Ny5duqTTcPpO9k/h27Guo8RJiIhICnl5eejatSvOnj0LBwcHbN68GQsWLIC5OVf6IdJHWhe+t2/fxiuvvFKoXa1WIzc3t4hHVHxPen6JiMiwGBsbY9GiRejSpQtiY2PRtWtXqSMR0TNoXfjWrVsXf/75Z6H2devW8WMdIiKq8LZs2YINGzZobr/xxhvYsmULHB356R+RvtN6Hd+pU6ciKCgIt2/fhlqtxoYNG3D58mWsWLECW7ZsKY2MREREksvIyMDYsWOxaNEi2NjYwNfXF9WrVwfw79A3ItJvWvf4vv3229i8eTN27twJCwsLTJ06FRcvXsTmzZvx+uuvl0ZGIiIiScXExMDHxweLFi0CAAwZMoQ9vETl0Atdua1Vq1bYsWOHrrMQERHpFbVajTlz5mDSpEnIzc2Fk5MTIiIi2NFDVE5p3eNbs2ZNPHz4sFB7UlISatasqZNQREREUsvNzUXHjh0xbtw45ObmokePHjh79iyLXqJyTOvC9/r161CpVIXas7Ozcfv2bZ2EIiIikppCoUCDBg1gbm6O8PBwrF+/Hvb29lLHIqKXUOKhDps2bdJsb9u2DTY2NprbKpUKu3btgpubm07DERERlaXU1FSkpqaiWrVqAIDQ0FCMGDGiyGU8iaj8KXHh2717dwD5M1eDgoIK3KdQKODm5oY5c+boNBwREVFZOXLkCPr374+qVati7969MDY2hqmpKYteogqkxIWvWq0GALi7u+P48eP8uIeIiCqEvLw8zJw5EzNmzIBKpUJubi5u3rwJd3d3qaMRkY5pvapDfHx8aeQgIiIqc/Hx8ejfvz8OHToEAOjXrx8WLlwIW1tbaYMRUal4oeXM0tPTsW/fPty4cQM5OTkF7vvoo490EoyIiKi0CCEQFRWF4cOHIzU1FVZWVli0aBECAwOljkZEpUjrwvfUqVPo0qULMjIykJ6ejkqVKiExMRHm5uaoUqUKC18iItJ7eXl5+Pbbb5GamooWLVpg5cqVHNpAZAC0Xs5szJgx6NatGx4/fgwzMzMcOXIEf//9N3x8fPDtt9+WRka9tSHmltQRiIjoBSgUCqxatQpffPEF9u7dy6KXyEBo3eN7+vRp/PjjjzAyMoJcLkd2djZq1qyJWbNmISgoCD179iyNnHrp1uNMAEBqVq7ESYiI6Flyc3Mxffp0mJmZYfLkyQCAunXrom7duhInI6KypHXhq1AoYGSU31FcpUoV3LhxA56enrCxscHNmzd1HlCfKeQy5KoEhrX1kDoKEREV48qVKwgMDMSJEycgl8vRr18/eHjw5zaRIdK68G3UqBGOHz+OWrVqoU2bNpg6dSoSExOxcuVK1K9fvzQy6q1clQAA2JqZSJyEiIj+SwiBn376CaNHj0ZGRgbs7OwQHh7OopfIgGk9xnfmzJlwcnICAHz11Vews7PDhx9+iAcPHuDHH3/UeUB9tWjvNakjEBFRMRITE9GzZ0+8//77yMjIQPv27XH27Fn06tVL6mhEJCGte3x9fX0121WqVEF0dLROA5UX30Rf0mw7WCklTEJERE/Lzc3Fa6+9hmvXrkGhUCA0NBRjxozRDNMjIsOls58CMTEx6Nq1q64Op/esTfP/ZpjT2xtyI5nEaYiI6AmFQoGQkBB4enri6NGj+OSTT1j0EhEALQvfbdu2YezYsZg4cSLi4uIAAJcuXUL37t3RpEkTzWWNtbFgwQK4ubnB1NQUfn5+OHbs2DP3T0pKwogRI+Dk5ASlUonatWvj999/1/q8L+tJsevtalPm5yYiooLOnTuH48ePa25/+OGHOHnyJBo1aiRhKiLSNyUufH/++Wd07twZy5cvxzfffIPXXnsNkZGRaNasGapWrYpz585pXYCuWbMGISEhmDZtGmJiYuDt7Y1OnTrh/v37Re6fk5OD119/HdevX8e6detw+fJlhIeHw9nZWavzEhFRxSCEQFhYGHx9fdGnTx+kpKQAAGQyGczMzCROR0T6psRjfL///nt88803+PTTT7F+/Xr07t0bCxcuRGxsLFxcXF7o5N999x2GDh2K4OBgAMDixYuxdetWLF26FOPHjy+0/9KlS/Ho0SMcOnQICoUCAODm5vZC5yYiovItISEBwcHBmrkmnp6eyMnJkTgVEemzEvf4Xrt2Db179wYA9OzZE8bGxpg9e/YLF705OTk4efIk/P39/w1jZAR/f38cPny4yMds2rQJzZo1w4gRI+Do6Ij69etj5syZUKlUxZ4nOzsbKSkpBb50IT27+HMSEVHp2rJlC7y8vBAdHQ1TU1OEhYVh69atsLe3lzoaEemxEhe+mZmZMDc3B5D/EZJSqdQsa/YiEhMToVKp4OjoWKDd0dERCQkJRT4mLi4O69atg0qlwu+//44pU6Zgzpw5+PLLL4s9T2hoKGxsbDRfrq6uL5z5iYycPOSonoxn5sQ2IqKykpubi+HDh6Nbt2548OABvLy8cOLECYwcORIyGX8eE9GzabWc2U8//QRLS0sAQF5eHpYvX17or+uPPvpId+n+Q61Wo0qVKliyZAnkcjl8fHxw+/ZtzJ49G9OmTSvyMRMmTEBISIjmdkpKyksXv0fjH2m23Sqbv9SxiIio5IyNjXH79m0AwCeffIKvvvoKSiWXlCSikilx4Vu9enWEh4drbletWhUrV64ssI9MJitx4Wtvbw+5XI579+4VaL937x6qVq1a5GOcnJygUCggl8s1bZ6enkhISEBOTg5MTApfQU2pVOr8h2Lcg3TNtrGcS+QQEZUmtVqNrKwsmJubQyaT4aeffsLZs2fRoUMHqaMRUTlT4sL3+vXrOj2xiYkJfHx8sGvXLnTv3h1A/g+3Xbt2YeTIkUU+pkWLFli1ahXUarVmTcYrV67AycmpyKK3tJgp8gtv9vYSEZWumzdvIigoCNWqVUNkZCQAwMHBgUUvEb0QSbsrQ0JCEB4ejoiICFy8eBEffvgh0tPTNas8DBw4EBMmTNDs/+GHH+LRo0f4+OOPceXKFWzduhUzZ87EiBEjyjR37j/je2s5WpXpeYmIDMnatWvh5eWFPXv24Ndff0V8fLzUkYionNP6ksW61LdvXzx48ABTp05FQkICGjZsiOjoaM2Etxs3bhS42o6rqyu2bduGMWPGwMvLC87Ozvj444/x2WeflWnuY9fzx/jm5Gl/wQ4iInq21NRUjBo1ChEREQCAJk2aICoqCu7u7hInI6LyTiaEEFKHKEspKSmwsbFBcnIyrK2ttX68EALuE/Iv1PFazUpY/X4zXUckIjJYR44cQWBgIOLi4mBkZIQJEyZg2rRpmrXbicgwvGy9VhxJe3zLo4NXH2q2+zZ5+aXRiIgoX05ODvr06YObN2+ievXqiIyMRKtWraSORUQVCJck0NLOi/+uQtGj0YtdvIOIiAozMTHBzz//jICAAJw5c4ZFLxHp3AsVvteuXcPkyZPRr18/3L9/HwDwxx9/4Pz58zoNp4+szfI/bqtqbSpxEiKi8k0IgZUrV2L16tWattdffx1RUVGwtbWVLhgRVVhaF7779u1DgwYNcPToUWzYsAFpaWkAgDNnzhR7EYmK6PW6js/fiYiIipSUlISAgAAMHDgQ77//Pm7cuCF1JCIyAFoXvuPHj8eXX36JHTt2FFg7t3379jhy5IhOw+mj409dtY2IiLS3b98+eHl5YfXq1ZDL5Rg3bhyqVasmdSwiMgBaF76xsbHo0aNHofYqVaogMTFRJ6H02eG4/Mltd5MzJU5CRFS+5OTkYOLEiWjXrh1u3rwJDw8PHDx4EJMnT4axMedaE1Hp0/onja2tLe7evVtoPcVTp07B2dlZZ8H0UUJylma7fR0OdSAiKqns7Gy0atUKx48fBwAMHjwY33//PSwtLSVORkSGROse33fffRefffYZEhISIJPJoFarcfDgQYwdOxYDBw4sjYx6Y+2Jm5rtd3y4ogMRUUkplUq0bt0adnZ2WLduHX7++WcWvURU5rQufGfOnIk6derA1dUVaWlpqFu3Llq3bo3mzZtj8uTJpZFRbygV/75cJsZcCY6I6FkSExNx8+a/HQZfffUVYmNj0atXLwlTEZEh03qog4mJCcLDwzFlyhScO3cOaWlpaNSoEWrVqlUa+fSK+p9r3PVsVLGHdBARvazt27cjKCgI7u7u2L9/P4yNjaFUKiv8kDgi0m9aF74HDhxAy5YtUb16dVSvXr00Mumtr/+4BABQG9ZVnomISiwrKwsTJkzAvHnzAAB2dnZISEiAiwuHhxGR9LT+vL59+/Zwd3fHxIkTceHChdLIpJdibyVrtu0tlRImISLST+fOnUPTpk01Re/w4cNx4sQJFr1EpDe0Lnzv3LmDTz75BPv27UP9+vXRsGFDzJ49G7du3SqNfHrjz6sPNNuT3vSUMAkRkX4RQiAsLAy+vr6IjY2Fg4MDNm/ejAULFsDc3FzqeEREGloXvvb29hg5ciQOHjyIa9euoXfv3oiIiICbmxvat29fGhn1wvk7KQCAajamkMlkEqchItIfubm5WLZsGbKzs9G5c2fExsaia9euUsciIirkpVYMd3d3x/jx4+Ht7Y0pU6Zg3759usqld7aevQsAcHewkDgJEZF+EEJAJpPBxMQEq1atws6dOzFixAh2DhCR3nrhNbkOHjyI4cOHw8nJCQEBAahfvz62bt2qy2x6Iy07T7P9dkPOSCYiw5aRkYEPP/wQ06dP17TVqVMHI0eOZNFLRHpN6x7fCRMmYPXq1bhz5w5ef/11fP/993j77bcr9Diu64npmu1ejTlJg4gMV0xMDAIDA3Hp0iUYGxtj8ODBqFGjhtSxiIhKROvCd//+/fj000/Rp08f2Nvbl0YmvfPX/VQAgIWJHHIj9mYQkeFRq9X49ttvMXnyZOTm5sLJyQkREREseomoXNG68D148GBp5NBrcqP8ESHpOSqJkxARlb2bN28iKCgIe/bsAQD06NED4eHhqFy5ssTJiIi0U6LCd9OmTejcuTMUCgU2bdr0zH3feustnQTTR809+EOeiAxLdnY2mjdvjlu3bsHc3Bw//PADBg8ezLG8RFQulajw7d69OxISElClShV079692P1kMhlUKvaKEhFVFEqlElOmTEF4eDiioqJQu3ZtqSMREb2wEhW+arW6yG0iIqp4jhw5AiEEmjVrBgAYOnQogoODoVAoJE5GRPRytF7ObMWKFcjOzi7UnpOTgxUrVugkFBERlb28vDzMmDEDLVu2xLvvvoukpCQA+Z/msegloopA68I3ODgYycnJhdpTU1MRHBysk1D65vC1h1JHICIqVfHx8WjTpg2mTZsGlUqFFi1acBwvEVU4Whe+T67U81+3bt2CjY2NTkLpmyv38pczO3MzSdogREQ6JoTAypUr4e3tjUOHDsHa2hqRkZFYtWpVhf2ZTkSGq8TLmTVq1AgymQwymQwdOnSAsfG/D1WpVIiPj8cbb7xRKiGlVtXGFADQp4mrxEmIiHQnOzsbgwYNwurVqwEALVq0QGRkJNzc3KQNRkRUSkpc+D5ZzeH06dPo1KkTLC0tNfeZmJjAzc0NvXr10nlAfeJW2ULqCEREOmNiYoKsrCzI5XJMnz4d48ePL9CpQURU0ZT4J9y0adMAAG5ubujbty9MTU1LLZS+2X/lgdQRiIh0IicnB9nZ2bCysoJMJkN4eDji4uLQtGlTqaMREZU6rcf4BgUFGVTRCwCO1vnPNz0nT+IkREQv7sqVK2jRogWGDh0KIQQAwN7enkUvERmMEvX4VqpUCVeuXIG9vT3s7OyeOdP30aNHOgunL67eTwMANHDmRA8iKn+EEPjpp58wevRoZGRk4Nq1a7h16xZcXTlvgYgMS4kK37lz58LKykqzbUhL3CRl5Gi2rU25jiURlS+JiYkYOnQoNm7cCABo3749IiIi4OLiIm0wIiIJlKjwDQoK0mwPGjSotLLopeTMXM22t6utdEGIiLS0Y8cOBAUF4e7du1AoFJg5cyZCQkJgZKT1KDciogpB659+MTExiI2N1dz+7bff0L17d0ycOBE5OTnPeGT5FJ+YDgAwNjKcXm4iKv+ysrIwePBg3L17F56enjh69CjGjh3LopeIDJrWPwE/+OADXLlyBQAQFxeHvn37wtzcHGvXrsW4ceN0HlBq6n8mgOSphcRJiIhKztTUFBERERg+fDhOnDiBRo0aSR2JiEhyWhe+V65cQcOGDQEAa9euRZs2bbBq1SosX74c69ev13U+veHlwoltRKS/hBAICwtDZGSkpq19+/ZYsGABzM3NJUxGRKQ/tF6pXAgBtVoNANi5cye6du0KAHB1dUViYqJu0+mBhORsqSMQET1TQkICgoODER0dDUtLS7Rt25aT14iIiqB1j6+vry++/PJLrFy5Evv27cObb74JAIiPj4ejo6POA0pt+4UEAMDfDzMkTkJEVNjmzZvRoEEDREdHw9TUFKGhoXB2dpY6FhGRXtK68J03bx5iYmIwcuRITJo0Ca+88goAYN26dWjevLnOA0pt7+X8q7bZW5pInISI6F8ZGRkYPnw43nrrLSQmJsLLywsnTpzAyJEjDWrJSSIibWg91MHLy6vAqg5PzJ49G3K5XCeh9ImduQKPM3LxYdtXpI5CRAQAyMzMRJMmTXDhwgUAwCeffIKvvvoKSqVS4mRERPpN68L3iZMnT+LixYsAgLp166Jx48Y6C6WPGrpychsR6QczMzN07doVjx8/RkREBF5//XWpIxERlQtaF773799H3759sW/fPtja2gIAkpKS0K5dO6xevRoODg66zkhEZPBu3bqF3NxcuLu7AwC++OILjBs3DpUrV5Y4GRFR+aH1GN9Ro0YhLS0N58+fx6NHj/Do0SOcO3cOKSkp+Oijj0ojo6QeZ+Q+fyciolK0du1aeHl5oV+/fsjNzf+ZZGJiwqKXiEhLWvf4RkdHY+fOnfD09NS01a1bFwsWLEDHjh11Gk5q2XkqzTYnixBRWUtNTcXHH3+MZcuWAQBUKhUePXpUIVfQISIqC1r3+KrVaigUikLtCoVCs75vRXHxbqpm262yhYRJiMjQHDlyBI0aNcKyZcsgk8kwadIkHDp0iEUvEdFL0Lrwbd++PT7++GPcuXNH03b79m2MGTMGHTp00Gk4qf39MF2zLTdijy8Rlb68vDx88cUXaNmyJa5du4bq1atj7969+PLLL4vsdCAiopLTuvCdP38+UlJS4ObmBg8PD3h4eMDd3R0pKSkICwsrjYySuXIv9fk7ERHpkFqtxm+//QaVSoV+/frhzJkzaN26tdSxiIgqBK3H+Lq6uiImJga7du3SLGfm6ekJf39/nYeTWmJqDgCgTlUriZMQUUUmhIAQAkZGRjAxMUFUVBSOHz+O/v37Sx2NiKhC0arwXbNmDTZt2oScnBx06NABo0aNKq1cesFCmf/y1K1mLXESIqqokpKS8OGHH8LDwwNffvklAODVV1/Fq6++KnEyIqKKp8SF76JFizBixAjUqlULZmZm2LBhA65du4bZs2eXZj5JLT0YDwBwsOLVkIhI9/bv348BAwbgxo0bMDExwYcffghnZ2epYxERVVglHuM7f/58TJs2DZcvX8bp06cRERGBhQsXlmY2yTla5xe8Gdmq5+xJRFRyOTk5mDhxItq2bYsbN27Aw8MD+/fvZ9FLRFTKSlz4xsXFISgoSHM7ICAAeXl5uHv3bqkE0wcKef7L06MxfxkRkW5cuXIFLVq0QGhoKIQQGDx4ME6dOgU/Pz+poxERVXglHuqQnZ0NC4t/17J9MgkjMzOzVILpEy5kRkS6kJmZiVatWuH+/fuws7PDkiVL8M4770gdi4jIYGg1uW3KlCkwNzfX3M7JycFXX30FGxsbTdt3332nu3QSu/W44hf1RFR2zMzMMHPmTKxatQoRERFwcXGROhIRkUEpceHbunVrXL58uUBb8+bNERcXp7ldkS7rm5P371XorEy1XvWNiAgAsGPHDpiZmaFly5YAgMGDByM4OBhGRlovo05ERC+pxBXd3r17SzGG/hEQmm1Ha1MJkxBReZSVlYWJEydi7ty5cHV1xZkzZ2BnZweZTFahOgmIiMoTdmUSEenY+fPnERAQgLNnzwIAunXrBqWSyyISEUmNn7UV405Slmb7yeoORETPIoRAWFgYfHx8cPbsWTg4OGDz5s1YsGBBgfkRREQkDfb4FiMjJ0+zbaqQS5iEiMqDjIwM9OrVC9HR0QCAzp07Y9myZXB0dJQ4GRERPcGuzGIkJOf3+FbhVduIqATMzMxgaWkJpVKJsLAwbN26lUUvEZGeYeFbjBN/PwYA3E/NljgJEemrjIwMJCcnA8hf1ebHH3/EyZMnMXLkSE5gIyLSQy9U+P7555/o378/mjVrhtu3bwMAVq5ciQMHDug0nJT+fpgOAGhVy17iJESkj06dOgUfHx8MHToUQuSvAlOpUiXUq1dP4mRERFQcrQvf9evXo1OnTjAzM8OpU6eQnZ3fI5qcnIyZM2fqPKBUMnNUAAALEw6DJqJ/qdVqzJ49G35+frh06RIOHDiAhIQEqWMREVEJaF34fvnll1i8eDHCw8OhUCg07S1atEBMTIxOw0nJXJlf8NarZi1xEiLSF7du3cLrr7+OcePGITc3Fz169MDZs2fh5OQkdTQiIioBrQvfy5cvo3Xr1oXabWxskJSUpItMeoVXbSMiAFi3bh28vLywe/dumJubIzw8HOvXr4e9PYdDERGVF1oXvlWrVsXVq1cLtR84cAA1a9bUSSgiIn2SkZGBMWPG4PHjx/D19cWpU6fw3nvvcQIbEVE5o3XhO3ToUHz88cc4evQoZDIZ7ty5g6ioKIwdOxYffvhhaWQkIpKUubk5VqxYgYkTJ+LQoUOoXbu21JGIiOgFaP05/vjx46FWq9GhQwdkZGSgdevWUCqVGDt2LEaNGlUaGSWhVgupIxCRRPLy8hAaGgpXV1cMGjQIANCuXTu0a9dO2mBERPRStC58ZTIZJk2ahE8//RRXr15FWloa6tatC0tLy9LIJ5k/zuXP0mb9S2RY4uPjMWDAABw8eBAWFhbo1KkTJ68REVUQLzxzy8TEBHXr1tVlFr1kYsxrfBAZAiEEoqKiMHz4cKSmpsLa2hoLFy5k0UtEVIFoXfi2a9fumRM6du/e/VKB9MGTNXwBwNvFVrogRFQmkpKSMHz4cPzvf/8DkL88Y2RkJNzc3KQNRkREOqV14duwYcMCt3Nzc3H69GmcO3cOQUFBusolqQdPXaaY6/gSVWwZGRlo3Lgx4uPjIZfLMX36dIwfPx7GxlzKkIiootH6J/vcuXOLbJ8+fTrS0tJeOpA+uJucCQAwN5HDyIjLFRFVZObm5ujbty/Wrl2LqKgo+Pn5SR2JiIhKic4GsPbv3x9Lly7V1eEklfHPUIeMp4Y8EFHFceXKlQLrkX/++ec4deoUi14iogpOZ4Xv4cOHYWpqqqvDSUot8pdy8HKxkTgJEemSEALh4eFo1KgR+vXrh9zcXAD5k3WtrKwkTkdERKVN66EOPXv2LHBbCIG7d+/ixIkTmDJlis6CSenMzSQAQE6eWtogRKQziYmJGDp0KDZu3AgAsLa2RkpKCipXrixtMCIiKjNaF742NgV7QY2MjPDqq69ixowZ6Nixo86CSSkhJQsAkJSRK3ESItKF7du3Y9CgQbh79y4UCgVCQ0MxZswYGBlxuUIiIkOiVeGrUqkQHByMBg0awM7OrrQySS77n55ed3sLiZMQ0cvIzs7GhAkTNJNyPT09sWrVqkKr0xARkWHQqrtDLpejY8eOSEpK0mmIBQsWwM3NDaampvDz88OxY8dK9LjVq1dDJpOhe/fuOs1jppADALxdbXV6XCIqW0ZGRjhw4AAAYMSIEThx4gSLXiIiA6b153z169dHXFyczgKsWbMGISEhmDZtGmJiYuDt7Y1OnTrh/v37z3zc9evXMXbsWLRq1UpnWZ64nZS/nJmVKdfxJCpvhBDIy8sDACgUCkRFRWHz5s2YP38+zM3NJU5HRERS0rrw/fLLLzF27Fhs2bIFd+/eRUpKSoEvbX333XcYOnQogoODUbduXSxevBjm5ubPXBpNpVIhMDAQn3/+OWrWrKn1OZ/nz78SAQDp2Xk6PzYRlZ6EhAR06dIFkydP1rTVqlULXbt2lTAVERHpixIXvjNmzEB6ejq6dOmCM2fO4K233oKLiwvs7OxgZ2cHW1tbrcf95uTk4OTJk/D39/83kJER/P39cfjw4WdmqVKlCoYMGfLcc2RnZ79wcV7bkcsbEZUXmzdvRoMGDRAdHY2wsDDcu3dP6khERKRnSvxZ/ueff45hw4Zhz549Ojt5YmIiVCoVHB0dC7Q7Ojri0qVLRT7mwIED+Pnnn3H69OkSnSM0NBSff/55iTNl5Pzby9u4esWdwEdUUWRkZOCTTz7B4sWLAQBeXl5YtWpVoZ8rREREJS58xT8XdWjTpk2phXme1NRUDBgwAOHh4bC3ty/RYyZMmICQkBDN7ZSUFLi6uha7f+ZTV2tzrWT24mGJqNTFxMQgICAAly9fBgB88skn+Oqrr6BUKiVORkRE+kir2VsymUynJ7e3t4dcLi/0keS9e/dQtWrVQvtfu3YN169fR7du3TRtanX+0mPGxsa4fPkyPDw8CjxGqVTylyBRBZSWlobXX38djx49QrVq1RAREVFg2BQREdF/aVX41q5d+7nF76NHj0p8PBMTE/j4+GDXrl2aJcnUajV27dqFkSNHFtq/Tp06iI2NLdA2efJkpKam4vvvv39mTy4RVSyWlpaYM2cONm3ahPDwcF6BjYiInkurwvfzzz8vdOW2lxUSEoKgoCD4+vqiadOmmDdvHtLT0xEcHAwAGDhwIJydnREaGgpTU1PUr1+/wONtbW0BoFA7EVU8a9euhYODA9q2bQsACAoKQlBQkM4/jSIioopJq8L33XffRZUqVXQaoG/fvnjw4AGmTp2KhIQENGzYENHR0ZqJKTdu3OBlRYkMXGpqKj766CMsX74czs7OOHv2LCpVqsSCl4iItFLiwrc0f8GMHDmyyKENALB3795nPnb58uW6D0REeuPIkSMIDAxEXFwcZDIZBg0aBCsrLjVIRETa03pVByKispCXl4eZM2dixowZUKlUqF69OiIjI0vlao1ERGQYSlz4Plk9gYiotKWlpaFTp044dOgQACAgIAALFizQjOknIiJ6EVqN8TUEj9JzpI5AZPAsLCzg6uoKa2trLFy4EIGBgVJHIiKiCoCF7388zsjVbHPiDFHZSUpKglqt1kxaW7RoEZKSkuDu7i51NCIiqiC4XEIxajpYSB2ByGDs27cPXl5eeO+99zTzCezs7Fj0EhGRTrHwJSLJ5OTkYOLEiWjXrh1u3ryJs2fP4sGDB1LHIiKiCoqF739w9QqisnH58mU0b94coaGhEEJg8ODBOHXqlM7XCiciInqChe9//HLiFgAgT8UCmKg0CCEQHh6Oxo0b4+TJk7Czs8O6devw888/c31eIiIqVZzc9h/Hrz8CANx4lCFxEqKKKT09HV9++SUyMjLQvn17REREwMXFRepYRERkAFj4/seTgjfQr7rESYgqJktLS0RGRuLo0aMICQnhJcmJiKjM8DfOf1SzMQUAeLnYSJyEqGLIyspCSEgIwsPDNW2tWrXC2LFjWfQSEVGZYo/vfxgZ5a/dW9uRYw2JXta5c+cQEBCA2NhYWFhYoHv37nBwcJA6FhERGSh2txCRzgkhEBYWBl9fX8TGxsLBwQGrV69m0UtERJJijy8R6VRCQgKCg4MRHR0NAOjcuTOWLVsGR0dHiZMREZGhY+FLRDqTmpqKRo0aISEhAaamppg9ezZGjBjBy38TEZFe4FAHItIZKysrvPfee/Dy8sKJEycwcuRIFr1ERKQ3WPgS0Us5deoULl++rLk9depUHDt2DPXq1ZMwFRERUWEsfInohajVasyePRt+fn4ICAhATk4OAEChUECpVEqcjoiIqDCO8f2PW48zpY5ApPdu3bqFoKAg7N69GwBQo0YNZGZmwsTEROJkRERExWOP71Oy81SabXtL9lgRFWXt2rXw8vLC7t27YW5ujvDwcKxfvx42NrzoCxER6Tf2+BbD1lwhdQQivZKRkYGRI0di2bJlAABfX19ERUWhdu3aEicjIiIqGfb4ElGJmJiY4OLFi5DJZJg0aRIOHTrEopeIiMoV9vgSUbHy8vKgVqthYmICY2NjREZG4vbt22jdurXU0YiIiLTGHl8iKlJ8fDzatGmDyZMna9o8PDxY9BIRUbnFwpeIChBCYOXKlfD29sahQ4cQHh6OxMREqWMRERG9NBa+RKSRlJSEgIAADBw4EKmpqWjRogVOnToFe3t7qaMRERG9NBa+T3mcnqvZNuJlVsnA7Nu3D15eXli9ejXkcjm++OIL7N27F25ublJHIyIi0glObnvKw/RszbaFki8NGY7k5GS8/fbbSE5OhoeHB6KiouDn5yd1LCIiIp1idfeUaw/SAQCVLXj1KTIsNjY2+OGHH7Bv3z7MmzcPVlZWUkciIiLSOQ51KMLD9BypIxCVKiEEwsPDsXPnTk3bwIED8fPPP7PoJSKiCos9vkVo7lFZ6ghEpSYxMRFDhw7Fxo0b4eTkhPPnz8POzk7qWERERKWOhS+RAdm+fTsGDRqEu3fvQqFQICQkBDY2NlLHIiIiKhMsfIkMQFZWFiZMmIB58+YBADw9PREVFYVGjRpJG4yIiKgMsfB9yrnbyVJHINK55ORktGrVCrGxsQCA4cOHY/bs2TA3N5c4GRERUdli4fuUQ9fyr0519X6axEmIdMfa2hr169dHQkICli5diq5du0odiYiISBIsfJ9yOSEVACe3UfmXkJAAhUKBypUrQyaTYeHChcjOzoajo6PU0YiIiCTD5cye4uFgCQCoV42Tfaj82rx5Mxo0aIAhQ4ZACAEAsLW1ZdFLREQGj4VvETydrKWOQKS1jIwMDB8+HG+99RYSExMRHx+Px48fSx2LiIhIb7DwJaoAYmJi4OPjg0WLFgEAQkJCcOzYMVSqVEniZERERPqDhS9ROaZWqzFr1iy89tpruHTpEpycnLB9+3bMmTMHSqVS6nhERER6hYXvUxLTeKliKl/S0tKwcOFC5ObmokePHoiNjcXrr78udSwiIiK9xFUd/pGckYvEtGwAgOqfCUFE+koIAZlMBmtra0RFReHixYsYMmQIZDKZ1NGIiIj0Fnt8/xFz499JQA1dbaULQvQMqampCA4OxpIlSzRtLVq0wHvvvceil4iI6DlY+P7j1D+Fb/VK5rAxU0ichqiwI0eOoGHDhli+fDnGjh2LR48eSR2JiIioXGHh+w9zZf6oj6xclcRJiArKy8vDjBkz0LJlS8TFxaF69erYunUrV2wgIiLSEsf4/uPAX/mXK36tJq/aRvojPj4e/fv3x6FDhwAA/fr1w8KFC2FrayttMCIionKIhe8/XOzMAAAZOezxJf2QlJQEHx8fPH78GFZWVli0aBECAwOljkVERFRusfD9j0bVbaWOQAQg/zLDH330EXbu3ImVK1fC3d1d6khERETlGsf4EumR/fv34+LFi5rbkydPxt69e1n0EhER6QAL3388SM2WOgIZsNzcXEyaNAlt27ZFQEAAsrPz/z8aGxvD2JgfzBAREekCf6P+4+LdFABAJsf4Uhm7cuUKAgMDceLECQBAo0aNkJeXx0sOExER6Rh7fP9RzTZ/cpuDFYsNKhtCCISHh6NRo0Y4ceIE7OzssHbtWixduhQWFhZSxyMiIqpw2OP7j1M3kwAAjtYsfKn0paamYuDAgdi4cSMAoH379oiIiICLi4u0wYiIiCow9vj+Q6UWAIC8f/4lKk1mZma4f/8+FAoFZs+ejR07drDoJSIiKmXs8QVw/k6yZtvPnRewoNLxZMKaUqmEsbExIiMjkZSUhEaNGkmcjIiIyDCwxxdAeva/E9o4xpdKw/nz59G0aVNMnDhR0+bu7s6il4iIqAyx8H1KTQdOKCLdEkIgLCwMvr6+OHv2LCIjI/H48WOpYxERERkkFr5EpSQhIQFvvvkmPvroI2RlZeGNN97AmTNnYGdnJ3U0IiIig8TCl6gUbNmyBV5eXvjjjz+gVCoRFhaG33//HVWrVpU6GhERkcHi5DYiHXv8+DH69++P5ORkeHl5YdWqVahXr57UsYiIiAweC18AOXlqqSNQBWJnZ4eFCxfi5MmTmDlzJq/ARkREpCc41AHA5XupAIDUrDyJk1B5pFarMXv2bGzbtk3TFhAQgDlz5rDoJSIi0iPs8QVgbiIHABjJJA5C5c6tW7cQFBSE3bt3o2rVqrh48SJsbW2ljkVERERFYI8vgOPxjwAADZxtpQ1C5cratWvh5eWF3bt3w8LCAl999RVsbGykjkVERETFYI8vADsLEwDA7aRMiZNQeZCamoqPPvoIy5cvBwA0adIEUVFRqFWrlrTBiIiI6JlY+D6lTW0HqSOQnnv06BGaNGmCuLg4yGQyTJw4EdOmTYNCoZA6GhERET0HC18iLVSqVAnNmzdHXl4eVq5cidatW0sdiYiIiEqIhS/Rc8THx8PCwgJVqlQBACxYsABqtZqT2IiIiMoZTm4jKoYQAitXroS3tzeGDBkCIQQAwNramkUvERFROcTCl6gISUlJCAgIwMCBA5GamoqkpCSkpKRIHYuIiIheAgtfAMmZuVJHID2yf/9+eHt7Y/Xq1ZDL5fjyyy+xd+9eLlVGRERUznGML4DTN5MAAFm5KmmDkKRyc3Mxffp0hIaGQggBDw8PREVFwc/PT+poREREpAPs8QXgYmcGALA245JUhiwzMxP/+9//IITAkCFDcPr0aRa9REREFQh7fJ9SvZK51BGojD2ZsCaTyWBtbY1Vq1bh9u3b6NWrl8TJiIiISNfY4wtg7+UHUkcgCSQmJqJHjx5YtGiRpu21115j0UtERFRBGXzhq1ILzXauSi1hEipL27dvR4MGDfDbb79h4sSJSE5OljoSERERlTKDL3zz1P8Wu53rV5UwCZWFrKwsjBkzBp06dUJCQgI8PT25YgMREZGB0IvCd8GCBXBzc4OpqSn8/Pxw7NixYvcNDw9Hq1atYGdnBzs7O/j7+z9zf23IjWQ6OQ7pp3PnzqFp06aYN28eAGD48OE4ceIEGjZsKGkuIiIiKhuSF75r1qxBSEgIpk2bhpiYGHh7e6NTp064f/9+kfvv3bsX/fr1w549e3D48GG4urqiY8eOuH37dhknp/Lk4cOHaNasGWJjY+Hg4IDNmzdjwYIFMDfnhEYiIiJDIRNPprVLxM/PD02aNMH8+fMBAGq1Gq6urhg1ahTGjx//3MerVCrY2dlh/vz5GDhw4HP3T0lJgY2NDZKTk2FtbY3kjFx4z9gOADj3eSdYKrnQRUX1xRdf4PDhw1i2bBkcHR2ljkNERETF+G+9piuSVnk5OTk4efIkJkyYoGkzMjKCv78/Dh8+XKJjZGRkIDc3F5UqVSry/uzsbGRnZ2tu//eys7eTMgEANmYKWJjItX0KpMc2b94Md3d31K9fHwAwceJEGBkZQSbjkBYiIiJDJOlQh8TERKhUqkK9b46OjkhISCjRMT777DNUq1YN/v7+Rd4fGhoKGxsbzZerq2uR+ymNWRBVFBkZGfjwww/x1ltvITAwEFlZWQAAuVzO95iIiMiAST7G92V8/fXXWL16NX799VeYmpoWuc+ECROQnJys+bp582YZp6SyFBMTg8aNG2Px4sUAAH9/fxa7REREBEDioQ729vaQy+W4d+9egfZ79+6hatVnLy327bff4uuvv8bOnTvh5eVV7H5KpRJKpVIneUl/qdVqfPvtt5g8eTJyc3Ph5OSEFStWFPtJABERERkeSXt8TUxM4OPjg127dmna1Go1du3ahWbNmhX7uFmzZuGLL75AdHQ0fH19yyIq6bHHjx/D398fn332GXJzc9GjRw/Exsay6CUiIqICJF/CICQkBEFBQfD19dWssZqeno7g4GAAwMCBA+Hs7IzQ0FAAwDfffIOpU6di1apVcHNz04wFtrS0hKWlpdbnT0jJ1N2TIUlYW1sjNzcX5ubm+OGHHzB48GAObyAiIqJCJC98+/btiwcPHmDq1KlISEhAw4YNER0drZnwduPGDRgZ/dsxvWjRIuTk5OCdd94pcJxp06Zh+vTpWp9/18X89YLvp2Y/Z0/SJ6mpqVAoFDA1NYVcLkdUVBSys7NRq1YtqaMRERGRnpJ8Hd+y9t914ebuuILvd/2FV6pYYmdIG6njUQkcOXIEgYGB6Natm+YqbERERFRxlNY6vuV6VQdduP4wHQDQrGZliZPQ8+Tl5WHGjBlo2bIl4uLisHHjxkLrMhMREREVx+AL3y1n7wIAHqXnSJyEniU+Ph5t2rTBtGnToFKpEBAQgNOnT+v0r0AiIiKq2Ay+8HWxMwMAVLUpeh1gkpYQAitXroS3tzcOHToEa2trREZGIioqCra2tlLHIyIionJE8sltUvv7YQYAoE1tB4mTUFEePnyIUaNGITU1FS1atEBkZCTc3NykjkVERETlkEEXvir1v/P62OOrn+zt7fHjjz/ir7/+wvjx42FsbND/ZYmIiOglGHQVcfvxv2v41qhsLmESeiInJwfTp09Hy5Yt0aVLFwD5S94RERERvSyDLnyfrOgAAEpjuYRJCAAuX76MwMBAnDx5ElWqVMHVq1dhZWUldSwiIiKqIAx6ctvdZF61TR8IIRAeHo7GjRvj5MmTsLOzw8KFC1n0EhERkU4ZdI/vg3+u1mZrrpA4ieFKTEzE0KFDsXHjRgBA+/btERERARcXF2mDERERUYVj0IXv2pO3AAANnG0kTmKYHjx4AG9vb9y9excKhQKhoaEYM2ZMgUtUExEREemKQVcYDpbKAv9S2XJwcEDHjh3h6emJo0eP4pNPPmHRS0RERKXGoHt8n+hYz1HqCAbj/PnzsLe3h6Nj/ms+f/58GBkZwdycq2oQERFR6TLo7rW/7qdJHcFgCCEQFhYGHx8fDB48GELkr6FsaWnJopeIiIjKhEH3+CZn5gIAclXiOXvSy0hISEBwcDCio6M1benp6bC0tJQwFRERERkag+7xtftnNYeaDhYSJ6m4Nm/ejAYNGiA6OhqmpqaYP38+tmzZwqKXiIiIypxB9/g+oTQ26Pq/VGRkZOCTTz7B4sWLAQBeXl5YtWoV6tWrJ3EyIiIiMlSs+KhUqFQq7NixAwDwySef4NixYyx6iYiISFIG3eOblp0ndYQKRa1WAwCMjIxgZWWF//3vf0hOToa/v7/EyYiIiIgMuMc3V6V+alKbTNIsFcGtW7fw+uuvY/78+Zq2Jk2asOglIiIivWGwhW9q1r+9vW6VuZzWy1i7di28vLywe/duzJgxA2lpXCaOiIiI9I/BFr5Pkxuxx/dFpKamIjg4GH369MHjx4/RpEkTHD58mCs2EBERkV5i4Usv5MiRI2jYsCGWL18OmUyGSZMm4eDBg6hVq5bU0YiIiIiKZNCT2+jF3Lt3D+3atUNWVhaqV6+OyMhItGrVSupYRERERM9ksIVvQnKm1BHKLUdHR0yZMgXnzp3DwoULYWtrK3UkIiIioucy2ML3dlKWZlsm4xjfZxFCIDIyEt7e3vDy8gIATJgwga8bERERlSsGO8b3QWp+4WvMiW3PlJSUhICAAAwcOBABAQHIzMzvKWfRS0REROWNwfb4KuT5NX99ZxuJk+ivffv2YcCAAbh58ybkcjneffddKBQKqWMRERERvRCDLXxTsnIBAA5WSomT6J+cnBxMnz4dX3/9NYQQ8PDwQFRUFPz8/KSORkRERPTCDLbwvXov/yILqf8UwJTvwYMH6NKlC06cOAEAGDx4MObNmwcrKyuJkxERERG9HIMtfG0tTAAAVqb86P5plSpVgoWFBezs7LBkyRK88847UkciIiIi0gmDLXyf8HDgVcYSExNhYWEBMzMzyOVyREZGAgBcXFwkTkZERESkOwa7qgPl2759O7y8vDBu3DhNm4uLC4teIiIiqnBY+BqorKwshISEoFOnTrh79y527dqF9PR0qWMRERERlRoWvgbo/Pnz8PPzw9y5cwEAw4cPx4kTJ2BhYSFxMiIiIqLSY7CF759/PZA6QpkTQiAsLAw+Pj44e/YsHBwcsHnzZixYsADm5uZSxyMiIiIqVQZb+DrbmgEAkjMNZzmz+/fvY9q0acjOzkbnzp0RGxuLrl27Sh2LiIiIqEwY7KoOT66461PDTtogZcjR0RHh4eG4e/cuRowYwcsOExERkUEx2MLXEGRkZGDs2LHo0qWLpme3V69eEqciIiIikgYL3woqJiYGgYGBuHTpEtavX4+4uDhOXiMiIiKDZrBjfCsqtVqN2bNn47XXXsOlS5fg5OSEyMhIFr1ERERk8NjjW4HcunULQUFB2L17NwCgR48eCA8PR+XKlSVORkRERCQ9Fr4VxN27d+Hl5YXHjx/D3Nwc33//PYYMGcIJbERERET/MNjCV6WSOoFuOTk5oUePHjh79iyioqJQu3ZtqSMRERER6RWDLXwPxz2EkdIcQgipo7ywo0ePonr16nBycgIAhIWFQaFQQKFQSJyMiIiISP8Y/OQ2TydrqSNoLS8vDzNmzECLFi0QHBwMtVoNADA3N2fRS0RERFQMg+3xfaJG5fJ1qd74+Hj0798fhw4dAgBUqlQJ2dnZMDMzkzgZERERkX4z+B5fpbFc6gglIoRAZGQkvL29cejQIVhbWyMyMhKrVq1i0UtERERUAgbd42tnroCJsf7X/ikpKRg2bBj+97//AQBatGiBlStXwt3dXeJkREREROWHQRe+daqWj/G9crkcJ06cgFwux7Rp0zBhwgQYGxv0W0dEVGqEEMjLy4Oqoi3/Q6RnFAoF5PKy/eTdoKunuMQ0qSMUKzc3F3K5HEZGRrCwsMDq1auRm5sLPz8/qaMREVVYOTk5uHv3LjIyMqSOQlThyWQyuLi4wNLSsszOadCF7+Q360odoUhXrlxBYGAgAgMDMXr0aABA48aNpQ1FRFTBqdVqxMfHQy6Xo1q1ajAxMeFFgIhKiRACDx48wK1bt1CrVq0y6/k16MLXztxE6ggFCCHw008/YfTo0cjIyMDt27fx/vvvw9y8fK08QURUHuXk5ECtVsPV1ZU/d4nKgIODA65fv675lLss6P/MrlJka64/a94mJiaiZ8+eeP/995GRkYH27dvj2LFj/OFLRFTGjIwM+lcjUZmR4hMVg/7urmypHz2+27dvh5eXFzZu3AiFQoHZs2djx44dcHFxkToaERERUYVh0EMdZJB+7NadO3fQrVs35OTkwNPTE1FRUWjUqJHUsYiIiIgqHIPu8XW0VkodAdWqVcOMGTMwfPhwnDhxgkUvERFRGbp8+TKqVq2K1NRUqaNUKDk5OXBzc8OJEyekjlKAwRa+5iZGkowtEUJg/vz5OH36tKZt3LhxWLBgAcfzEhHRCxk0aBBkMhlkMhkUCgXc3d0xbtw4ZGVlFdp3y5YtaNOmDaysrGBubo4mTZpg+fLlRR53/fr1aNu2LWxsbGBpaQkvLy/MmDEDjx49KuVnVHYmTJiAUaNGwcrKSuoopWbBggVwc3ODqakp/Pz8cOzYsWfun5ubixkzZsDDwwOmpqbw9vZGdHR0gX3c3Nw0/+ee/hoxYgQAwMTEBGPHjsVnn31Was/rRRhs4SuFhIQEvPnmmxg1ahQCAgI0P5C4XA4REb2sN954A3fv3kVcXBzmzp2LH3/8EdOmTSuwT1hYGN5++220aNECR48exdmzZ/Huu+9i2LBhGDt2bIF9J02ahL59+6JJkyb4448/cO7cOcyZMwdnzpzBypUry+x55eTklNqxb9y4gS1btmDQoEEvdZzSzPiy1qxZg5CQEEybNg0xMTHw9vZGp06dcP/+/WIfM3nyZPz4448ICwvDhQsXMGzYMPTo0QOnTp3S7HP8+HHcvXtX87Vjxw4AQO/evTX7BAYG4sCBAzh//nzpPUFtCQOTnJwsAIhXx60r0/Nu3rxZODg4CABCqVSKsLAwoVaryzQDEREVLzMzU1y4cEFkZmZq2tRqtUjPzpXkS5vfEUFBQeLtt98u0NazZ0/RqFEjze0bN24IhUIhQkJCCj3+hx9+EADEkSNHhBBCHD16VAAQ8+bNK/J8jx8/LjbLzZs3xbvvvivs7OyEubm58PHx0Ry3qJwff/yxaNOmjeZ2mzZtxIgRI8THH38sKleuLNq2bSv69esn+vTpU+BxOTk5onLlyiIiIkIIIYRKpRIzZ84Ubm5uwtTUVHh5eYm1a9cWm1MIIWbPni18fX0LtCUmJop3331XVKtWTZiZmYn69euLVatWFdinqIxCCBEbGyveeOMNYWFhIapUqSL69+8vHjx4oHncH3/8IVq0aCFsbGxEpUqVxJtvvimuXr36zIwvq2nTpmLEiBGa2yqVSlSrVk2EhoYW+xgnJycxf/78Am09e/YUgYGBxT7m448/Fh4eHoX+37Zr105Mnjy5yMcU9T33xJN6LTk5udhzvgiDntxWFjIyMjB27FgsWrQIAODl5YVVq1ahXr16EicjIqLnycxVoe7UbZKc+8KMTjA3ebFf0+fOncOhQ4dQo0YNTdu6deuQm5tbqGcXAD744ANMnDgR//vf/+Dn54eoqChYWlpi+PDhRR7f1ta2yPa0tDS0adMGzs7O2LRpE6pWrYqYmBio1Wqt8kdERODDDz/EwYMHAQBXr15F7969kZaWprnK17Zt25CRkYEePXoAAEJDQxEZGYnFixejVq1a2L9/P/r37w8HBwe0adOmyPP8+eef8PX1LdCWlZUFHx8ffPbZZ7C2tsbWrVsxYMAAeHh4oGnTpsVmTEpKQvv27fHee+9h7ty5yMzMxGeffYY+ffpg9+7dAID09HSEhITAy8sLaWlpmDp1Knr06IHTp08Xu4zezJkzMXPmzGe+XhcuXED16tULtefk5ODkyZOYMGGCps3IyAj+/v44fPhwscfLzs6GqalpgTYzMzMcOHCgyP1zcnIQGRmJkJCQQp9iN23aFH/++ecz85clFr6l6O7du2jfvj0uXboEAAgJCcHMmTOhVEo/qY6IiCqWLVu2wNLSEnl5ecjOzoaRkRHmz5+vuf/KlSuwsbGBk5NToceamJigZs2auHLlCgDgr7/+Qs2aNaFQaLfe/apVq/DgwQMcP34clSpVAgC88sorWj+XWrVqYdasWZrbHh4esLCwwK+//ooBAwZozvXWW2/BysoK2dnZmDlzJnbu3IlmzZoBAGrWrIkDBw7gxx9/LLbw/fvvvwsVvs7OzgX+OBg1ahS2bduGX375pUDh+9+MX375JRo1alSgSF26dClcXV1x5coV1K5dG7169SpwrqVLl8LBwQEXLlxA/fr1i8w4bNgw9OnT55mvV7Vq1YpsT0xMhEqlgqOjY4F2R0dHTW1SlE6dOuG7775D69at4eHhgV27dmHDhg1QqVRF7r9x40YkJSUVOWSkWrVq+Pvvv5+Zvyyx8C1Fjo6OcHJyQnJyMiIiIvD6669LHYmIiLRgppDjwoxOkp1bG+3atcOiRYuQnp6OuXPnwtjYuFChVVJCiBd63OnTp9GoUSNN0fuifHx8Ctw2NjZGnz59EBUVhQEDBiA9PR2//fYbVq9eDSC/RzgjI6PQ79mcnJxnrpaUmZlZqGdTpVJh5syZ+OWXX3D79m3k5OQgOzu70AT0/2Y8c+YM9uzZo+mRftq1a9dQu3Zt/PXXX5g6dSqOHj2KxMRETU/4jRs3ii18K1Wq9NKvp7a+//57DB06FHXq1IFMJoOHhweCg4OxdOnSIvf/+eef0blz5yILcDMzM2RkZJR25BIz2MI3I0e7j11K6tatW6hUqRLMzc1hZGSEqKgoKBQK2Nvbl8r5iIio9MhkshceblDWLCwsNL2rS5cuhbe3N37++WcMGTIEAFC7dm0kJyfjzp07hQqUnJwcXLt2De3atdPse+DAAeTm5mrV62tmZvbM+42MjAoV1bm5uUU+l/8KDAxEmzZtcP/+fezYsQNmZmZ44403AOQPsQCArVu3wtnZucDjnvUpq729PR4/flygbfbs2fj+++8xb948NGjQABYWFhg9enShCWz/zZiWloZu3brhm2++KXSeJ73s3bp1Q40aNRAeHo5q1apBrVajfv36z5wc9zJDHezt7SGXy3Hv3r0C7ffu3UPVqlWLPZ6DgwM2btyIrKwsPHz4ENWqVcP48eNRs2bNQvv+/fff2LlzJzZs2FDksR49egQHB4dn5i9LXNVBh9auXQsvL68CH5E4OTmx6CUiojJlZGSEiRMnYvLkycjMzAQA9OrVCwqFAnPmzCm0/+LFi5Geno5+/foBAAICApCWloaFCxcWefykpKQi2728vHD69OlilztzcHDA3bt3C7Q9vbznszRv3hyurq5Ys2YNoqKi0Lt3b01RXrduXSiVSty4cQOvvPJKgS9XV9dij9moUSNcuHChQNvBgwfx9ttvo3///vD29i4wBORZGjdujPPnz8PNza1QBgsLCzx8+BCXL1/G5MmT0aFDB3h6ehYquosybNgwnD59+plfxQ11MDExgY+PD3bt2qVpU6vV2LVrl2ZIyLOYmprC2dkZeXl5WL9+Pd5+++1C+yxbtgxVqlTBm2++WeQxzp07p1fXKDDYwreZR2WdHSs1NRWDBw9Gnz598PjxY5w8eVLzg4aIiEgKvXv3hlwux4IFCwAA1atXx6xZszBv3jxMmjQJly5dwrVr1/Ddd99h3Lhx+OSTT+Dn5wcA8PPz07SNGzcOhw8fxt9//41du3ahd+/eiIiIKPKc/fr1Q9WqVdG9e3ccPHgQcXFxWL9+vWYiVfv27XHixAmsWLECf/31F6ZNm4Zz586V+DkFBARg8eLF2LFjBwIDAzXtVlZWGDt2LMaMGYOIiAhcu3YNMTExCAsLKzYrkD+W9fDhwwXGrtaqVQs7duzAoUOHcPHiRXzwwQeFekyLMmLECDx69Aj9+vXD8ePHce3aNWzbtg3BwcFQqVSws7ND5cqVsWTJEly9ehW7d+9GSEjIc49bqVKlQoX0f7+MjYv/VCIkJATh4eGIiIjAxYsX8eGHHyI9PR3BwcGafQYOHFhgAtzRo0exYcMGxMXF4c8//8Qbb7wBtVqNcePGFTi2Wq3GsmXLEBQUVGyGP//8Ex07dnzu8ywzOl0johx4sjxG7x926uR4hw8fFh4eHgKAkMlkYtKkSSInJ0cnxyYiorLzrKWV9F1Ry4QJIURoaKhwcHAQaWlpmrbffvtNtGrVSlhYWAhTU1Ph4+Mjli5dWuRx16xZI1q3bi2srKyEhYWF8PLyEjNmzHjmcmbXr18XvXr1EtbW1sLc3Fz4+vqKo0ePau6fOnWqcHR0FDY2NmLMmDFi5MiRhZYz+/jjj4s89oULFwQAUaNGjULLZqnVajFv3jzx6quvCoVCIRwcHESnTp3Evn37is2am5srqlWrJqKjozVtDx8+FG+//bawtLQUVapUEZMnTxYDBw4s8PoWl/HKlSuiR48ewtbWVpiZmYk6deqI0aNHa7Lu2LFDeHp6CqVSKby8vMTevXsFAPHrr78Wm1EXwsLCRPXq1YWJiYlo2rSpZnm5p59PUFCQ5vbevXs1OStXriwGDBggbt++Xei427ZtEwDE5cuXizzvoUOHhK2trcjIyCjyfimWM5MJ8YIj2MuplJQU2NjYYPbmGIzt+uJd73l5eZg5cyZmzJgBlUqF6tWrY+XKlWjdurUO0xIRUVnJyspCfHw83N3dC014ooprwYIF2LRpE7Ztk2bZuoqsb9++8Pb2xsSJE4u8/1nfc0/qteTkZFhbW+ssU/kYsa+HHjx4gO+//x4qlQr9+vXDwoULi13XkIiIiPTTBx98gKSkJKSmplboyxaXtZycHDRo0ABjxoyROkoBLHxfkJOTE5YuXYrU1FT0799f6jhERET0AoyNjTFp0iSpY1Q4JiYmmDx5stQxCjHYyW3aSkpKQr9+/fDbb79p2p7M+iQiIiIi/cfCtwT27dsHLy8vrF69GsOGDUNWVpbUkYiIiIhISwZb+KZk5T13n5ycHEyYMAHt2rXDzZs34eHhgY0bN3LSAxFRBWZgc76JJCPF95rBjvF91bHwJQWfdvnyZQQGBuLkyZMAgMGDB+P7778v8lKERERU/j25GEJGRsZzr0BGRC/vyRXr5HLtLs/9Mgy28DV9xjXQb968icaNGyMjIwN2dnYIDw9/4eudExFR+SCXy2Fra4v79+8DAMzNzSGTySRORVQxqdVqPHjwAObm5s+8AIeuGWzh+yyurq7o378/rl69ioiICLi4uEgdiYiIykDVqlUBQFP8ElHpMTIyQvXq1cv0D0wWvv/YsWMH6tWrp7ne9Q8//ACFQgEjI4MdBk1EZHBkMhmcnJxQpUoV5ObmSh2HqEIzMTEp8zrL4AvfrKwsTJgwAfPmzYO/vz+2bdsGIyMjKJVKqaMREZFE5HJ5mY47JKKyoRfdmQsWLICbmxtMTU3h5+eHY8eOPXP/tWvXok6dOjA1NUWDBg3w+++/v9B5z507h6ZNm2LevHkAgNq1a/MvfCIiIqIKSvLCd82aNQgJCcG0adMQExMDb29vdOrUqdjxVYcOHUK/fv0wZMgQnDp1Ct27d0f37t1x7tw5rc77+y8r4Ovri9jYWDg4OGDz5s1YsGABe3qJiIiIKiiZkHjBQj8/PzRp0gTz588HkD/Lz9XVFaNGjcL48eML7d+3b1+kp6djy5YtmrbXXnsNDRs2xOLFi597vpSUFNjY2Ghud+7cGcuWLYOjo6MOng0RERERvawn9VpycjKsra11dlxJx/jm5OTg5MmTmDBhgqbNyMgI/v7+OHz4cJGPOXz4MEJCQgq0derUCRs3bixy/+zsbGRnZ2tuJycnAwDkxgqEzvwK77//PmQyGVJSUl7y2RARERGRLjypy3TdPytp4ZuYmAiVSlWot9XR0RGXLl0q8jEJCQlF7p+QkFDk/qGhofj8888LtavycjFu3DiMGzfuBdMTERERUWl6+PBhgU/qX1aFX9VhwoQJBXqIk5KSUKNGDdy4cUOnLyTpp5SUFLi6uuLmzZs6/aiE9BPfb8PC99uw8P02LMnJyahevToqVaqk0+NKWvja29tDLpfj3r17Bdrv3bunWUT8v6pWrarV/kqlssgJazY2NvzGMSDW1tZ8vw0I32/DwvfbsPD9Niy6XudX0lUdTExM4OPjg127dmna1Go1du3ahWbNmhX5mGbNmhXYH8i/+ERx+xMRERERAXow1CEkJARBQUHw9fXVrKmbnp6O4OBgAMDAgQPh7OyM0NBQAMDHH3+MNm3aYM6cOXjzzTexevVqnDhxAkuWLJHyaRARERGRnpO88O3bty8ePHiAqVOnIiEhAQ0bNkR0dLRmAtuNGzcKdHM3b94cq1atwuTJkzFx4kTUqlULGzduRP369Ut0PqVSiWnTpnG9XgPB99uw8P02LHy/DQvfb8NSWu+35Ov4EhERERGVBcmv3EZEREREVBZY+BIRERGRQWDhS0REREQGgYUvERERERmECln4LliwAG5ubjA1NYWfnx+OHTv2zP3Xrl2LOnXqwNTUFA0aNMDvv/9eRklJF7R5v8PDw9GqVSvY2dnBzs4O/v7+z/3/QfpF2+/vJ1avXg2ZTIbu3buXbkDSKW3f76SkJIwYMQJOTk5QKpWoXbs2f6aXI9q+3/PmzcOrr74KMzMzuLq6YsyYMcjKyiqjtPQy9u/fj27duqFatWqQyWTYuHHjcx+zd+9eNG7cGEqlEq+88gqWL1+u/YlFBbN69WphYmIili5dKs6fPy+GDh0qbG1txb1794rc/+DBg0Iul4tZs2aJCxcuiMmTJwuFQiFiY2PLODm9CG3f74CAALFgwQJx6tQpcfHiRTFo0CBhY2Mjbt26VcbJ6UVo+34/ER8fL5ydnUWrVq3E22+/XTZh6aVp+35nZ2cLX19f0aVLF3HgwAERHx8v9u7dK06fPl3GyelFaPt+R0VFCaVSKaKiokR8fLzYtm2bcHJyEmPGjCnj5PQifv/9dzFp0iSxYcMGAUD8+uuvz9w/Li5OmJubi5CQEHHhwgURFhYm5HK5iI6O1uq8Fa7wbdq0qRgxYoTmtkqlEtWqVROhoaFF7t+nTx/x5ptvFmjz8/MTH3zwQanmJN3Q9v3+r7y8PGFlZSUiIiJKKyLp0Iu833l5eaJ58+bip59+EkFBQSx8yxFt3+9FixaJmjVripycnLKKSDqk7fs9YsQI0b59+wJtISEhokWLFqWak3SvJIXvuHHjRL169Qq09e3bV3Tq1Emrc1WooQ45OTk4efIk/P39NW1GRkbw9/fH4cOHi3zM4cOHC+wPAJ06dSp2f9IfL/J+/1dGRgZyc3NRqVKl0opJOvKi7/eMGTNQpUoVDBkypCxiko68yPu9adMmNGvWDCNGjICjoyPq16+PmTNnQqVSlVVsekEv8n43b94cJ0+e1AyHiIuLw++//44uXbqUSWYqW7qq1yS/cpsuJSYmQqVSaa769oSjoyMuXbpU5GMSEhKK3D8hIaHUcpJuvMj7/V+fffYZqlWrVuibifTPi7zfBw4cwM8//4zTp0+XQULSpRd5v+Pi4rB7924EBgbi999/x9WrVzF8+HDk5uZi2rRpZRGbXtCLvN8BAQFITExEy5YtIYRAXl4ehg0bhokTJ5ZFZCpjxdVrKSkpyMzMhJmZWYmOU6F6fIm08fXXX2P16tX49ddfYWpqKnUc0rHU1FQMGDAA4eHhsLe3lzoOlQG1Wo0qVapgyZIl8PHxQd++/2/v3oOiquI4gH9ZcAFx0SFllg18oZBjmiJoaI5hljipJCqUjKKiOBLiaFpMKo8MXymOOpo2jmDGCOpYOpKgmBSsU/ng0QguoqA2ok3aqCjEY3/94XCnlUcuPjD2+5m5f9xzzzn3d/bMjr89nHsNxrJly7B9+/a2Do2egezsbKxatQrbtm3DuXPncPDgQaSnp2PlypVtHRq9wNrVim/Xrl1hbW2NmzdvmpTfvHkTWq22yTZardas+vTiaM18N1i/fj3WrFmDrKwsDBw48FmGSU+JufN96dIllJeXY8KECUqZ0WgEANjY2MBgMMDd3f3ZBk2t1prvt4uLCzp06ABra2ulrF+/frhx4wZqamqgVqufaczUeq2Z7xUrVmD69OmYM2cOAGDAgAG4f/8+wsPDsWzZMqhUXNtrT5rL1xwdHR97tRdoZyu+arUaQ4YMwYkTJ5Qyo9GIEydOwNfXt8k2vr6+JvUB4Pjx483WpxdHa+YbANatW4eVK1ciIyMD3t7ezyNUegrMne9XXnkFv/32G/Lz85Vj4sSJ8PPzQ35+Ptzc3J5n+GSm1ny/R4wYgdLSUuUHDgCUlJTAxcWFSe8LrjXz/eDBg0bJbcOPnofPS1F78tTyNfOeu3vxpaamiq2trSQnJ0tRUZGEh4dLly5d5MaNGyIiMn36dImOjlbq6/V6sbGxkfXr10txcbHExsbydWb/I+bO95o1a0StVsuBAwekoqJCOe7du9dWQyAzmDvfj+JbHf5fzJ3vq1evikajkcjISDEYDHLkyBFxdnaWzz//vK2GQGYwd75jY2NFo9HI3r175fLly3Ls2DFxd3eXoKCgthoCmeHevXuSl5cneXl5AkASExMlLy9Prly5IiIi0dHRMn36dKV+w+vMli5dKsXFxbJ161a+zqzBli1bpHv37qJWq2Xo0KHy888/K9dGjRoloaGhJvX37dsnHh4eolarpX///pKenv6cI6YnYc589+jRQwA0OmJjY59/4NQq5n6//42J7/+PufN96tQpGTZsmNja2krv3r0lISFB6urqnnPU1FrmzHdtba3ExcWJu7u72NnZiZubm0RERMhff/31/AMns508ebLJf48b5jg0NFRGjRrVqM2gQYNErVZL7969JSkpyez7Wonw7wFERERE1P61qz2+RERERETNYeJLRERERBaBiS8RERERWQQmvkRERERkEZj4EhEREZFFYOJLRERERBaBiS8RERERWQQmvkRERERkEZj4EhEBSE5ORpcuXdo6jFazsrLCd99912KdmTNn4r333nsu8RARvYiY+BJRuzFz5kxYWVk1OkpLS9s6NCQnJyvxqFQquLq6YtasWfjjjz+eSv8VFRUYN24cAKC8vBxWVlbIz883qbNp0yYkJyc/lfs1Jy4uThmntbU13NzcEB4ejtu3b5vVD5N0InoWbNo6ACKip8nf3x9JSUkmZd26dWujaEw5OjrCYDDAaDSioKAAs2bNwvXr15GZmfnEfWu12v+s07lz5ye+z+Po378/srKyUF9fj+LiYsyePRt37txBWlrac7k/EVFzuOJLRO2Kra0ttFqtyWFtbY3ExEQMGDAADg4OcHNzQ0REBCorK5vtp6CgAH5+ftBoNHB0dMSQIUNw5swZ5Xpubi5GjhwJe3t7uLm5ISoqCvfv328xNisrK2i1Wuh0OowbNw5RUVHIyspCVVUVjEYjPvvsM7i6usLW1haDBg1CRkaG0rampgaRkZFwcXGBnZ0devTogdWrV5v03bDVoVevXgCAwYMHw8rKCm+++SYA01XUr776CjqdDkaj0STGgIAAzJ49Wzk/dOgQvLy8YGdnh969eyM+Ph51dXUtjtPGxgZarRYvv/wyxowZg6lTp+L48ePK9fr6eoSFhaFXr16wt7eHp6cnNm3apFyPi4vD7t27cejQIWX1ODs7GwBw7do1BAUFoUuXLnByckJAQADKy8tbjIeIqAETXyKyCCqVCps3b8b58+exe/du/PDDD/j444+brR8SEgJXV1ecPn0aZ8+eRXR0NDp06AAAuHTpEvz9/TF58mQUFhYiLS0Nubm5iIyMNCsme3t7GI1G1NXVYdOmTdiwYQPWr1+PwsJCjB07FhMnTsTFixcBAJs3b8bhw4exb98+GAwGpKSkoGfPnk32++uvvwIAsrKyUFFRgYMHDzaqM3XqVNy6dQsnT55Uym7fvo2MjAyEhIQAAHJycjBjxgwsXLgQRUVF2LFjB5KTk5GQkPDYYywvL0dmZibUarVSZjQa4erqiv3796OoqAgxMTH49NNPsW/fPgDAkiVLEBQUBH9/f1RUVKCiogLDhw9HbW0txo4dC41Gg5ycHOj1enTq1An+/v6oqal57JiIyIIJEVE7ERoaKtbW1uLg4KAcU6ZMabLu/v375aWXXlLOk5KSpHPnzsq5RqOR5OTkJtuGhYVJeHi4SVlOTo6oVCqpqqpqss2j/ZeUlIiHh4d4e3uLiIhOp5OEhASTNj4+PhIRESEiIgsWLJDRo0eL0Whssn8A8u2334qISFlZmQCQvLw8kzqhoaESEBCgnAcEBMjs2bOV8x07dohOp5P6+noREXnrrbdk1apVJn3s2bNHXFxcmoxBRCQ2NlZUKpU4ODiInZ2dABAAkpiY2GwbEZEPP/xQJk+e3GysDff29PQ0+Qz+/vtvsbe3l8zMzBb7JyISEeEeXyJqV/z8/PDll18q5w4ODgAern6uXr0aFy5cwN27d1FXV4fq6mo8ePAAHTt2bNTP4sWLMWfOHOzZs0f5c727uzuAh9sgCgsLkZKSotQXERiNRpSVlaFfv35Nxnbnzh106tQJRqMR1dXVeOONN7Bz507cvXsX169fx4gRI0zqjxgxAgUFBQAeblN4++234enpCX9/f4wfPx7vvPPOE31WISEhmDt3LrZt2wZbW1ukpKTg/fffh0qlUsap1+tNVnjr6+tb/NwAwNPTE4cPH0Z1dTW++eYb5OfnY8GCBSZ1tm7dil27duHq1auoqqpCTU0NBg0a1GK8BQUFKC0thUajMSmvrq7GpUuXWvEJEJGlYeJLRO2Kg4MD+vTpY1JWXl6O8ePHY/78+UhISICTkxNyc3MRFhaGmpqaJhO4uLg4TJs2Denp6Th69ChiY2ORmpqKSZMmobKyEvPmzUNUVFSjdt27d282No1Gg3PnzkGlUsHFxQX29vYAgLt37/7nuLy8vFBWVoajR48iKysLQUFBGDNmDA4cOPCfbZszYcIEiAjS09Ph4+ODnJwcbNy4UbleWVmJ+Ph4BAYGNmprZ2fXbL9qtVqZgzVr1uDdd99FfHw8Vq5cCQBITU3FkiVLsGHDBvj6+kKj0eCLL77AL7/80mK8lZWVGDJkiMkPjgYvygOMRPRiY+JLRO3e2bNnYTQasWHDBmU1s2E/aUs8PDzg4eGBRYsW4YMPPkBSUhImTZoELy8vFBUVNUqw/4tKpWqyjaOjI3Q6HfR6PUaNGqWU6/V6DB061KRecHAwgoODMWXKFPj7++P27dtwcnIy6a9hP219fX2L8djZ2SEwMBApKSkoLS2Fp6cnvLy8lOteXl4wGAxmj/NRy5cvx+jRozF//nxlnMOHD0dERIRS59EVW7Va3Sh+Ly8vpKWlwdnZGY6Ojk8UExFZJj7cRkTtXp8+fVBbW4stW7bg8uXL2LNnD7Zv395s/aqqKkRGRiI7OxtXrlyBXq/H6dOnlS0Mn3zyCU6dOoXIyEjk5+fj4sWLOHTokNkPt/3b0qVLsXbtWqSlpcFgMCA6Ohr5+flYuHAhACAxMRF79+7FhQsXUFJSgv3790Or1Tb5n244OzvD3t4eGRkZuHnzJu7cudPsfUNCQpCeno5du3YpD7U1iImJwddff434+HicP38excXFSE1NxfLly80am6+vLwYOHIhVq1YBAPr27YszZ84gMzMTJSUlWLFiBU6fPm3SpmfPnigsLITBYMCff/6J2tpahISEoGvXrggICEBOTg7KysqQnZ2NqKgo/P7772bFRESWiYkvEbV7r732GhITE7F27Vq8+uqrSElJMXkV2KOsra1x69YtzJgxAx4eHggKCsK4ceMQHx8PABg4cCB+/PFHlJSUYOTIkRg8eDBiYmKg0+laHWNUVBQWL16Mjz76CAMGDEBGRgYOHz6Mvn37Ani4TWLdunXw9vaGj48PysvL8f333ysr2P9mY2ODzZs3Y8eOHdDpdAgICGj2vqNHj4aTkxMMBgOmTZtmcm3s2LE4cuQIjh07Bh8fH7z++uvYuHEjevToYfb4Fi1ahJ07d+LatWuYN28eAgMDERwcjGHDhuHWrVsmq78AMHfuXHh6esLb2xvdunWDXq9Hx44d8dNPP6F79+4IDAxEv379EBYWhurqaq4AE9FjsRIRaesgiIiIiIieNa74EhEREZFFYOJLRERERBaBiS8RERERWQQmvkRERERkEZj4EhEREZFFYOJLRERERBaBiS8RERERWQQmvkRERERkEZj4EhEREZFFYOJLRERERBaBiS8RERERWYR/ANPlTbQOIuUAAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# roc curve\n",
    "plot_ROC_curve(search, X_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The RandomizedSearchCV has slightly improved the model performance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# calculate the feature importance\n",
    "plot_feature_importance(search, X_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Using a Pre-Trained model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "import time\n",
    "import torch\n",
    "\n",
    "from transformers import AutoTokenizer, AutoModelForSequenceClassification\n",
    "from torch.utils.data import DataLoader, TensorDataset\n",
    "from torch.optim import AdamW, Adam\n",
    "from torch.nn import CrossEntropyLoss\n",
    "from torch.nn import DataParallel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "X['birth_date'] = X['birth_date'].astype('Int64')\n",
    "X['age'] = X['age'].fillna(0).astype('int').replace(0, pd.NA)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "text_entries = np.array([' '.join([str(x) for x in X.iloc[i].dropna(inplace=False).values]) for i in range(len(X))], dtype=str)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Cyrille menuisier 25 Garçon française',\n",
       "       'Zélie prop re 30 française sa femme',\n",
       "       'Caroline domestique 24 Fille française', ..., '',\n",
       "       'frère 1897 patron', '1914 francaise'], dtype='<U205')"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "text_entries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split the data into training and testing sets\n",
    "train_texts, test_texts, train_labels, test_labels = train_test_split(text_entries, y, test_size=0.2)\n",
    "\n",
    "# Prepare the data for text classification\n",
    "tokenizer = AutoTokenizer.from_pretrained('distilbert-base-uncased', num_labels=2)\n",
    "train_encodings = tokenizer(train_texts.tolist(), truncation=True, padding=True, return_tensors='pt')\n",
    "test_encodings = tokenizer(test_texts.tolist(), truncation=True, padding=True, return_tensors='pt')\n",
    "\n",
    "# Convert the labels to PyTorch tensor\n",
    "train_labels = torch.tensor(np.array(train_labels))\n",
    "test_labels = torch.tensor(np.array(test_labels))\n",
    "\n",
    "# Create PyTorch datasets\n",
    "train_dataset = TensorDataset(train_encodings['input_ids'], train_encodings['attention_mask'], train_labels)\n",
    "test_dataset = TensorDataset(test_encodings['input_ids'], test_encodings['attention_mask'], test_labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('François 1905', 0)\n",
      "('Aristide carrier 34 cdm patron', 1)\n",
      "('Emile terrassier française beau frère 1869 Hirson - Aisne Girard Extrepin', 0)\n",
      "('Maria 14 Fille sa fille', 0)\n",
      "('Denise sans profession française femme 1884 Mons', 0)\n"
     ]
    }
   ],
   "source": [
    "for i in range(5):\n",
    "    print((train_texts[i], int(train_labels[i])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Some weights of DistilBertForSequenceClassification were not initialized from the model checkpoint at distilbert-base-uncased and are newly initialized: ['classifier.bias', 'classifier.weight', 'pre_classifier.bias', 'pre_classifier.weight']\n",
      "You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"
     ]
    }
   ],
   "source": [
    "# Define the model\n",
    "model = AutoModelForSequenceClassification.from_pretrained('distilbert-base-uncased', num_labels=2)\n",
    "\n",
    "# Use the 3 GPUs\n",
    "model = DataParallel(model, device_ids=[0, 1, 2])\n",
    "\n",
    "# Define the optimizer\n",
    "optimizer = AdamW(model.parameters(), lr=5e-5)\n",
    "\n",
    "# Define the loss function\n",
    "criterion = CrossEntropyLoss()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cuda:0\n"
     ]
    }
   ],
   "source": [
    "device = torch.device('cuda:0') if torch.cuda.is_available() else torch.device('cpu')\n",
    "model.to(device)\n",
    "print(device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "batch_size=128\n",
    "\n",
    "train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)\n",
    "test_loader = DataLoader(test_dataset, batch_size=batch_size, shuffle=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|          | 0/157 [00:00<?, ?it/s]/home/tristan.kirscher/.venv/lib/python3.9/site-packages/torch/nn/parallel/_functions.py:68: UserWarning: Was asked to gather along dimension 0, but all input tensors were scalars; will instead unsqueeze and return a vector.\n",
      "  warnings.warn('Was asked to gather along dimension 0, but all '\n",
      "100%|██████████| 157/157 [00:45<00:00,  3.46it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 1 | Loss: 29.374 | Accuracy: 0.977\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 157/157 [00:45<00:00,  3.42it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 2 | Loss: 26.158 | Accuracy: 0.978\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 157/157 [00:46<00:00,  3.38it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 3 | Loss: 23.321 | Accuracy: 0.981\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 157/157 [00:46<00:00,  3.37it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 4 | Loss: 19.415 | Accuracy: 0.984\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 157/157 [00:46<00:00,  3.35it/s]"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch: 5 | Loss: 18.428 | Accuracy: 0.985\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "model.train()\n",
    "\n",
    "num_epochs = 5\n",
    "\n",
    "for epoch in range(num_epochs):\n",
    "    total_loss = 0\n",
    "    accuracy = 0\n",
    "    for batch in tqdm(train_loader):\n",
    "        optimizer.zero_grad()\n",
    "        input_ids = batch[0].to(device)\n",
    "        attention_mask = batch[1].to(device)\n",
    "        labels = batch[2].to(device)\n",
    "        outputs = model(input_ids=input_ids, attention_mask=attention_mask, labels=labels)\n",
    "        logits = outputs.logits\n",
    "        preds = torch.argmax(logits, dim=1)\n",
    "        accuracy += torch.sum(preds == labels).item()\n",
    "        loss = torch.sum(outputs.loss)\n",
    "        total_loss += loss.item()\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "    print(\"Epoch:\", epoch+1, \"| Loss:\", round(total_loss, 3), \"| Accuracy:\", round(accuracy/len(train_loader.dataset), 3))                    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_loader = DataLoader(test_dataset, batch_size=batch_size, shuffle=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define the evaluation function\n",
    "def evaluate(model, test_loader):\n",
    "    model.eval()\n",
    "    all_labels = []\n",
    "    all_predictions = []\n",
    "    with torch.no_grad():\n",
    "        for batch in test_loader:\n",
    "            input_ids = batch[0].to(device)\n",
    "            attention_mask = batch[1].to(device)\n",
    "            labels = batch[2].to(device)\n",
    "            outputs = model(input_ids, attention_mask=attention_mask, labels=labels)\n",
    "            predictions = outputs.logits.argmax(dim=1)\n",
    "            all_labels.extend(labels.cpu().numpy())\n",
    "            all_predictions.extend(predictions.cpu().numpy())\n",
    "    return all_labels, all_predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_labels, all_predictions = evaluate(model, test_loader)\n",
    "cm = confusion_matrix(all_labels, all_predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "disp = ConfusionMatrixDisplay(confusion_matrix=cm)\n",
    "disp.plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.97      0.93      0.95      3862\n",
      "           1       0.80      0.89      0.84      1153\n",
      "\n",
      "    accuracy                           0.92      5015\n",
      "   macro avg       0.88      0.91      0.90      5015\n",
      "weighted avg       0.93      0.92      0.92      5015\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(all_labels, all_predictions))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Marie sans profession 73 française cdm 1 0\n",
      "Félix journalier 39 Homme marié cdm 0 1\n",
      "Auguste journalier 61 française cdm 1 0\n",
      "Annet fabricant 51 Veuf 0 1\n",
      "Jean cultivateur 64 française cdm 0 1\n",
      "Julie 6 Garçon fils premier lit 1 0\n",
      "Clervie Marie 36 1 0\n",
      "Gabriel menuisier 60 Homme marié chef de flle 0 1\n",
      "Laurent cultivateur Homme marié 0 1\n",
      "Denis propriétaire 70 Veuf 0 1\n"
     ]
    }
   ],
   "source": [
    "# print missclassified examples\n",
    "\n",
    "for i in range(len(all_labels)):\n",
    "    if all_labels[i] != all_predictions[i] and i<200:\n",
    "        print(test_texts[i], all_labels[i], all_predictions[i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "torch.save(model, \"BERT_household_classifier_fr\")"
   ]
  },
  {
   "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": "3.9.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}