{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__mlmachine - Hyperparameter Tuning with Bayesian Optimization__\n",
    "<br><br>\n",
    "Welcome to Example Notebook 4. If you're new to mlmachine, check out [Example Notebook 1](https://github.com/petersontylerd/mlmachine/blob/master/notebooks/mlmachine_part_1.ipynb), [Example Notebook 2](https://github.com/petersontylerd/mlmachine/blob/master/notebooks/mlmachine_part_2.ipynb) and [Example Notebook 3](https://github.com/petersontylerd/mlmachine/blob/master/notebooks/mlmachine_part_3.ipynb).\n",
    "<br><br>\n",
    "Check out the [GitHub repository](https://github.com/petersontylerd/mlmachine).\n",
    "<br><br>\n",
    "\n",
    "1. [Bayesian Optimization for Multiple Estimators in One Shot](#Bayesian-Optimization-for-Multiple-Estimators-in-One-Shot)\n",
    "    1. [Prepare Data](#Prepare-Data)\n",
    "    1. [Feature Importance Summary](#Feature-Importance-Summary)\n",
    "    1. [Exhaustively Iterative Feature Selection](#Exhaustively-Iterative-Feature-Selection)\n",
    "    1. [Outline Our Feature Space](#Outline-Our-Feature-Space)\n",
    "    1. [Run the Bayesian Optimization Job](#Run-the-Bayesian-Optimization-Job)\n",
    "1. [Results Analysis](#Results-Analysis)\n",
    "    1. [Results Summary](#Results-Summary)\n",
    "    1. [Model Optimization Assessment](#Model-Optimization-Assessment)\n",
    "    1. [Parameter Selection Assessment](#Parameter-Selection-Assessment)\n",
    "1. [Model Reinstantiation](#Model-Reinstantiation)\n",
    "    1. [Top Model Identification](#Top-Model-Identification)\n",
    "    1. [Putting the Models to Use](#Putting-the-Models-to-Use)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "# Bayesian Optimization for Multiple Estimators in One Shot\n",
    "---\n",
    "<br><br>\n",
    "Bayesian optimization is typically described as an advancement beyond exhaustive grid searches, and rightfully so. This hyperparameter tuning strategy succeeds by using prior information to inform future parameter selection for a given estimator. Check out [Will Koehrsen's article on Medium](https://towardsdatascience.com/an-introductory-example-of-bayesian-optimization-in-python-with-hyperopt-aae40fff4ff0) for an excellent overview of the package.\n",
    "<br><br>\n",
    "\n",
    "mlmachine uses hyperopt as a foundation for performing Bayesian optimization, and takes the functionality of hyperopt a step further through a simplified workflow that allows for optimization of multiple models in single process execution. In this article, we are going to optimize four classifiers:\n",
    "- `LogisticRegression()`\n",
    "- `XGBClassifier()`\n",
    "- `RandomForestClassifier()`\n",
    "- `KNeighborsClassifier()`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id = 'Bayesian-Optimization-for-Multiple-Estimators-in-One-Shot'></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "## Prepare Data\n",
    "---\n",
    "<br><br>\n",
    "First, we apply data preprocessing techniques to clean up our data. We'll start by creating two `Machine()` objects - one for the training data and a second for the validation data.\n",
    "<br><br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id = 'Prepare-Data'></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-03T14:46:48.617162Z",
     "start_time": "2020-04-03T14:46:43.248567Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "~/.pyenv/versions/main37/lib/python3.7/site-packages/sklearn/externals/joblib/__init__.py:15: FutureWarning: sklearn.externals.joblib is deprecated in 0.21 and will be removed in 0.23. Please import this functionality directly from joblib, which can be installed with: pip install joblib. If this warning is raised when loading pickled models, you may need to re-serialize those models with scikit-learn 0.21+.\n",
      "  warnings.warn(msg, category=FutureWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      ">>> category label encoding\n",
      "\n",
      "\t0 --> 0\n",
      "\t1 --> 1\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# import libraries\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "# import mlmachine tools\n",
    "import mlmachine as mlm\n",
    "from mlmachine.data import titanic\n",
    "\n",
    "# use titanic() function to create DataFrames for training and validation datasets\n",
    "df_train, df_valid = titanic()\n",
    "\n",
    "# ordinal encoding hierarchy\n",
    "ordinal_encodings = {\"Pclass\": [1, 2, 3]}\n",
    "\n",
    "# instantiate a Machine object for the training data\n",
    "mlmachine_titanic_train = mlm.Machine(\n",
    "    data=df_train,\n",
    "    target=\"Survived\",\n",
    "    remove_features=[\"PassengerId\",\"Ticket\",\"Name\",\"Cabin\"],\n",
    "    identify_as_continuous=[\"Age\",\"Fare\"],\n",
    "    identify_as_count=[\"Parch\",\"SibSp\"],\n",
    "    identify_as_nominal=[\"Embarked\"],\n",
    "    identify_as_ordinal=[\"Pclass\"],\n",
    "    ordinal_encodings=ordinal_encodings,\n",
    "    is_classification=True,\n",
    ")\n",
    "\n",
    "# instantiate a Machine object for the validation data\n",
    "mlmachine_titanic_valid = mlm.Machine(\n",
    "    data=df_valid,\n",
    "    remove_features=[\"PassengerId\",\"Ticket\",\"Name\",\"Cabin\"],\n",
    "    identify_as_continuous=[\"Age\",\"Fare\"],\n",
    "    identify_as_count=[\"Parch\",\"SibSp\"],\n",
    "    identify_as_nominal=[\"Embarked\"],\n",
    "    identify_as_ordinal=[\"Pclass\"],\n",
    "    ordinal_encodings=ordinal_encodings,\n",
    "    is_classification=True,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "<br><br>\n",
    "Now we process the data by imputing nulls and applying various binning, feature engineering and encoding techniques:\n",
    "<br><br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-03T04:51:07.652640Z",
     "start_time": "2020-04-03T04:51:03.525954Z"
    }
   },
   "outputs": [],
   "source": [
    "# standard libary and settings\n",
    "from sklearn.impute import SimpleImputer\n",
    "from sklearn.model_selection import KFold\n",
    "from sklearn.preprocessing import (\n",
    "    OrdinalEncoder,\n",
    "    OneHotEncoder,\n",
    "    KBinsDiscretizer,\n",
    "    RobustScaler,\n",
    "    PolynomialFeatures,\n",
    ")\n",
    "from sklearn.pipeline import make_pipeline\n",
    "\n",
    "from category_encoders import WOEEncoder, TargetEncoder, CatBoostEncoder\n",
    "\n",
    "# import mlmachine tools\n",
    "from mlmachine.features.preprocessing import (\n",
    "    DataFrameSelector,\n",
    "    PandasTransformer,\n",
    "    PandasFeatureUnion,\n",
    "    GroupbyImputer,\n",
    "    KFoldEncoder,\n",
    ")\n",
    "\n",
    "### create imputation PandasFeatureUnion pipeline\n",
    "impute_pipe = PandasFeatureUnion([\n",
    "    (\"age\", make_pipeline(\n",
    "        DataFrameSelector(include_columns=[\"Age\",\"SibSp\"]),\n",
    "        GroupbyImputer(null_column=\"Age\", groupby_column=\"SibSp\", strategy=\"mean\")\n",
    "    )),\n",
    "    (\"fare\", make_pipeline(\n",
    "        DataFrameSelector(include_columns=[\"Fare\",\"Pclass\"]),\n",
    "        GroupbyImputer(null_column=\"Fare\", groupby_column=\"Pclass\", strategy=\"mean\")\n",
    "    )),\n",
    "    (\"embarked\", make_pipeline(\n",
    "        DataFrameSelector(include_columns=[\"Embarked\"]),\n",
    "        PandasTransformer(SimpleImputer(strategy=\"most_frequent\"))\n",
    "    )),\n",
    "    (\"diff\", make_pipeline(\n",
    "        DataFrameSelector(exclude_columns=[\"Age\",\"Fare\",\"Embarked\"])\n",
    "    )),\n",
    "])\n",
    "\n",
    "# fit and transform training data, transform validation data\n",
    "mlmachine_titanic_machine.training_features = impute_pipe.fit_transform(mlmachine_titanic_machine.training_features)\n",
    "mlmachine_titanic_machine.validation_features = impute_pipe.transform(mlmachine_titanic_machine.validation_features)\n",
    "\n",
    "### create polynomial feature PandasFeatureUnion pipeline\n",
    "polynomial_pipe = PandasFeatureUnion([\n",
    "    (\"polynomial\", make_pipeline(\n",
    "        DataFrameSelector(include_mlm_dtypes=[\"continuous\"]),\n",
    "        PandasTransformer(PolynomialFeatures(degree=2, interaction_only=False, include_bias=False)),\n",
    "    )),\n",
    "    (\"diff\", make_pipeline(\n",
    "        DataFrameSelector(exclude_mlm_dtypes=[\"continuous\"]),\n",
    "    )),\n",
    "])\n",
    "\n",
    "# fit and transform training data, transform validation data\n",
    "mlmachine_titanic_machine.training_features = polynomial_pipe.fit_transform(mlmachine_titanic_machine.training_features)\n",
    "mlmachine_titanic_machine.validation_features = polynomial_pipe.transform(mlmachine_titanic_machine.validation_features)\n",
    "\n",
    "# update mlm_dtypes\n",
    "mlmachine_titanic_machine.update_dtypes()\n",
    "mlmachine_titanic_\n",
    "\n",
    "### create simple encoding & binning PandasFeatureUnion pipeline\n",
    "encode_pipe = PandasFeatureUnion([\n",
    "    (\"nominal\", make_pipeline(\n",
    "        DataFrameSelector(include_columns=mlmachine_titanic_machine.training_features.mlm_dtypes[\"nominal\"]),\n",
    "        PandasTransformer(OneHotEncoder(drop=\"first\")),\n",
    "    )),\n",
    "    (\"ordinal\", make_pipeline(\n",
    "        DataFrameSelector(include_columns=list(ordinal_encodings.keys())),\n",
    "        PandasTransformer(OrdinalEncoder(categories=list(ordinal_encodings.values()))),\n",
    "    )),\n",
    "    (\"bin\", make_pipeline(\n",
    "        DataFrameSelector(include_columns=mlmachine_titanic_machine.training_features.mlm_dtypes[\"continuous\"]),\n",
    "        PandasTransformer(KBinsDiscretizer(encode=\"ordinal\")),\n",
    "    )),\n",
    "    (\"diff\", make_pipeline(\n",
    "        DataFrameSelector(exclude_columns=mlmachine_titanic_machine.training_features.mlm_dtypes[\"nominal\"] + list(ordinal_encodings.keys())),\n",
    "    )),\n",
    "])\n",
    "\n",
    "# fit and transform training data, transform validation data\n",
    "mlmachine_titanic_machine.training_features = encode_pipe.fit_transform(mlmachine_titanic_machine.training_features)\n",
    "mlmachine_titanic_machine.validation_features = encode_pipe.fit_transform(mlmachine_titanic_machine.validation_features)\n",
    "\n",
    "# update mlm_dtypes\n",
    "mlmachine_titanic_machine.update_dtypes()\n",
    "mlmachine_titanic_\n",
    "\n",
    "### create KFold encoding PandasFeatureUnion pipeline\n",
    "target_encode_pipe = PandasFeatureUnion([\n",
    "    (\"target\", make_pipeline(\n",
    "        DataFrameSelector(include_mlm_dtypes=[\"category\"]), \n",
    "        KFoldEncoder(\n",
    "            target=mlmachine_titanic_machine.training_target,\n",
    "            cv=KFold(n_splits=5, shuffle=True, random_state=0),\n",
    "            encoder=TargetEncoder,\n",
    "        ),\n",
    "    )),\n",
    "    (\"woe\", make_pipeline(\n",
    "        DataFrameSelector(include_mlm_dtypes=[\"category\"]),\n",
    "        KFoldEncoder(\n",
    "            target=mlmachine_titanic_machine.training_target,\n",
    "            cv=KFold(n_splits=5, shuffle=False),\n",
    "            encoder=WOEEncoder,\n",
    "        ),\n",
    "    )),\n",
    "    (\"catboost\", make_pipeline(\n",
    "        DataFrameSelector(include_mlm_dtypes=[\"category\"]),\n",
    "        KFoldEncoder(\n",
    "            target=mlmachine_titanic_machine.training_target,\n",
    "            cv=KFold(n_splits=5, shuffle=False),\n",
    "            encoder=CatBoostEncoder,\n",
    "        ),\n",
    "    )),\n",
    "    (\"diff\", make_pipeline(\n",
    "        DataFrameSelector(exclude_mlm_dtypes=[\"category\"]),\n",
    "    )),\n",
    "])\n",
    "\n",
    "# fit and transform training data, transform validation data\n",
    "mlmachine_titanic_machine.training_features = target_encode_pipe.fit_transform(mlmachine_titanic_machine.training_features)\n",
    "mlmachine_titanic_machine.validation_features = target_encode_pipe.transform(mlmachine_titanic_machine.validation_features)\n",
    "\n",
    "# update mlm_dtypes\n",
    "mlmachine_titanic_machine.update_dtypes()\n",
    "mlmachine_titanic_\n",
    "\n",
    "### scale values\n",
    "scale = PandasTransformer(RobustScaler())\n",
    "\n",
    "mlmachine_titanic_machine.training_features = scale.fit_transform(mlmachine_titanic_machine.training_features)\n",
    "mlmachine_titanic_machine.validation_features = scale.transform(mlmachine_titanic_machine.validation_features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-03T04:51:07.686180Z",
     "start_time": "2020-04-03T04:51:07.655312Z"
    }
   },
   "outputs": [
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       "      <th>Age</th>\n",
       "      <th>Age*Fare</th>\n",
       "      <th>Age*Fare_binned_5</th>\n",
       "      <th>Age*Fare_binned_5_catboost_encoded</th>\n",
       "      <th>Age*Fare_binned_5_target_encoded</th>\n",
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       "      <th>Sex_male</th>\n",
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       "      <td>-0.5</td>\n",
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       "      <td>0.754887</td>\n",
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       "      <td>-0.167183</td>\n",
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       "      <td>0.017703</td>\n",
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       "      <td>1.370847</td>\n",
       "      <td>1.550231</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.996585</td>\n",
       "      <td>0.989747</td>\n",
       "      <td>0.991390</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>-0.184856</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>-1.034485</td>\n",
       "      <td>-0.868374</td>\n",
       "      <td>-1.118681</td>\n",
       "      <td>-0.309570</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>0.196885</td>\n",
       "      <td>-0.053122</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.089623</td>\n",
       "      <td>-0.121216</td>\n",
       "      <td>-0.167183</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.996585</td>\n",
       "      <td>0.991811</td>\n",
       "      <td>0.991390</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.377713</td>\n",
       "      <td>1.838762</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.746016</td>\n",
       "      <td>1.529026</td>\n",
       "      <td>1.479878</td>\n",
       "      <td>0.431320</td>\n",
       "      <td>0.5</td>\n",
       "      <td>-0.550145</td>\n",
       "      <td>-0.358207</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>-2.0</td>\n",
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       "      <td>1.550231</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.996585</td>\n",
       "      <td>1.018386</td>\n",
       "      <td>0.991390</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>-0.092152</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.528664</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>2.992442</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.746016</td>\n",
       "      <td>1.529026</td>\n",
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       "      <td>2.713372</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.660192</td>\n",
       "      <td>0.442971</td>\n",
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       "      <td>-2.0</td>\n",
       "      <td>1.760707</td>\n",
       "      <td>1.518257</td>\n",
       "      <td>1.550231</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.017268</td>\n",
       "      <td>0.017703</td>\n",
       "      <td>0.000604</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>-2.160748</td>\n",
       "      <td>-0.385571</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>-0.061922</td>\n",
       "      <td>0.257553</td>\n",
       "      <td>-0.090433</td>\n",
       "      <td>-1.216453</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>1.956874</td>\n",
       "      <td>1.754877</td>\n",
       "      <td>...</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.089623</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-0.167183</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.017268</td>\n",
       "      <td>0.017703</td>\n",
       "      <td>0.000604</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>-0.237671</td>\n",
       "      <td>-0.069069</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.528664</td>\n",
       "      <td>-0.325717</td>\n",
       "      <td>-0.541412</td>\n",
       "      <td>-0.238045</td>\n",
       "      <td>-0.5</td>\n",
       "      <td>0.196885</td>\n",
       "      <td>-0.196302</td>\n",
       "      <td>...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.089623</td>\n",
       "      <td>-0.035279</td>\n",
       "      <td>-0.167183</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.996585</td>\n",
       "      <td>0.989747</td>\n",
       "      <td>0.991390</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>-1.237671</td>\n",
       "      <td>0.078365</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.528664</td>\n",
       "      <td>-0.358860</td>\n",
       "      <td>-0.541412</td>\n",
       "      <td>-0.957344</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>1.956874</td>\n",
       "      <td>1.193962</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.909780</td>\n",
       "      <td>0.823122</td>\n",
       "      <td>0.797100</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0.996585</td>\n",
       "      <td>0.935964</td>\n",
       "      <td>0.991390</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 43 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Age  Age*Fare  Age*Fare_binned_5  Age*Fare_binned_5_catboost_encoded  \\\n",
       "0 -0.622287 -0.241862               -1.0                           -0.061922   \n",
       "1  0.608483  2.880011                1.0                            1.746016   \n",
       "2 -0.314594 -0.184856               -0.5                           -1.034485   \n",
       "3  0.377713  1.838762                1.0                            1.746016   \n",
       "4  0.377713 -0.092152                0.0                           -0.528664   \n",
       "5  0.100602 -0.111967               -0.5                           -1.034485   \n",
       "6  1.839252  2.992442                1.0                            1.746016   \n",
       "7 -2.160748 -0.385571               -1.0                           -0.061922   \n",
       "8 -0.237671 -0.069069                0.0                           -0.528664   \n",
       "9 -1.237671  0.078365                0.0                           -0.528664   \n",
       "\n",
       "   Age*Fare_binned_5_target_encoded  Age*Fare_binned_5_woe_encoded     Age^2  \\\n",
       "0                          0.257553                      -0.090433 -0.568680   \n",
       "1                          1.330575                       1.479878  0.726867   \n",
       "2                         -0.868374                      -1.118681 -0.309570   \n",
       "3                          1.529026                       1.479878  0.431320   \n",
       "4                         -0.424139                      -0.541412  0.431320   \n",
       "5                         -0.626603                      -1.118681  0.108522   \n",
       "6                          1.529026                       1.479878  2.713372   \n",
       "7                          0.257553                      -0.090433 -1.216453   \n",
       "8                         -0.325717                      -0.541412 -0.238045   \n",
       "9                         -0.358860                      -0.541412 -0.957344   \n",
       "\n",
       "   Age^2_binned_5  Age^2_binned_5_catboost_encoded  \\\n",
       "0            -0.5                         0.196885   \n",
       "1             1.0                         0.660192   \n",
       "2            -0.5                         0.196885   \n",
       "3             0.5                        -0.550145   \n",
       "4             0.5                        -0.550145   \n",
       "5             0.5                        -0.550145   \n",
       "6             1.0                         0.660192   \n",
       "7            -1.0                         1.956874   \n",
       "8            -0.5                         0.196885   \n",
       "9            -1.0                         1.956874   \n",
       "\n",
       "   Age^2_binned_5_target_encoded  ...  Parch  Pclass_ordinal_encoded  \\\n",
       "0                       0.754887  ...    0.0                     0.0   \n",
       "1                       0.000000  ...    0.0                    -2.0   \n",
       "2                      -0.053122  ...    0.0                     0.0   \n",
       "3                      -0.358207  ...    0.0                    -2.0   \n",
       "4                      -0.164268  ...    0.0                     0.0   \n",
       "5                      -0.301361  ...    0.0                     0.0   \n",
       "6                       0.442971  ...    0.0                    -2.0   \n",
       "7                       1.754877  ...    1.0                     0.0   \n",
       "8                      -0.196302  ...    2.0                     0.0   \n",
       "9                       1.193962  ...    0.0                    -1.0   \n",
       "\n",
       "   Pclass_ordinal_encoded_catboost_encoded  \\\n",
       "0                                -0.089623   \n",
       "1                                 1.760707   \n",
       "2                                -0.089623   \n",
       "3                                 1.760707   \n",
       "4                                -0.089623   \n",
       "5                                -0.089623   \n",
       "6                                 1.760707   \n",
       "7                                -0.089623   \n",
       "8                                -0.089623   \n",
       "9                                 0.909780   \n",
       "\n",
       "   Pclass_ordinal_encoded_target_encoded  Pclass_ordinal_encoded_woe_encoded  \\\n",
       "0                               0.000000                           -0.167183   \n",
       "1                               1.370847                            1.550231   \n",
       "2                              -0.121216                           -0.167183   \n",
       "3                               1.518257                            1.550231   \n",
       "4                              -0.092714                           -0.167183   \n",
       "5                              -0.035279                           -0.167183   \n",
       "6                               1.518257                            1.550231   \n",
       "7                               0.000000                           -0.167183   \n",
       "8                              -0.035279                           -0.167183   \n",
       "9                               0.823122                            0.797100   \n",
       "\n",
       "   Sex_male  Sex_male_catboost_encoded  Sex_male_target_encoded  \\\n",
       "0       0.0                   0.017268                 0.017703   \n",
       "1      -1.0                   0.996585                 0.989747   \n",
       "2      -1.0                   0.996585                 0.991811   \n",
       "3      -1.0                   0.996585                 1.018386   \n",
       "4       0.0                   0.017268                -0.004054   \n",
       "5       0.0                   0.017268                 0.000000   \n",
       "6       0.0                   0.017268                 0.017703   \n",
       "7       0.0                   0.017268                 0.017703   \n",
       "8      -1.0                   0.996585                 0.989747   \n",
       "9      -1.0                   0.996585                 0.935964   \n",
       "\n",
       "   Sex_male_woe_encoded  SibSp  \n",
       "0              0.000604    1.0  \n",
       "1              0.991390    1.0  \n",
       "2              0.991390    0.0  \n",
       "3              0.991390    1.0  \n",
       "4              0.000604    0.0  \n",
       "5              0.000604    0.0  \n",
       "6              0.000604    0.0  \n",
       "7              0.000604    3.0  \n",
       "8              0.991390    0.0  \n",
       "9              0.991390    1.0  \n",
       "\n",
       "[10 rows x 43 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mlmachine_titanic_machine.training_features[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "## Feature Importance Summary\n",
    "---\n",
    "<br><br>\n",
    "As a second preparatory step, we want to perform feature selection for each of our classifiers.\n",
    "<br><br>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id = 'Feature-Importance-Summary'></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-03T05:03:58.193945Z",
     "start_time": "2020-04-03T04:51:07.688700Z"
    }
   },
   "outputs": [],
   "source": [
    "# import libraries\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from xgboost import XGBClassifier\n",
    "\n",
    "# estimator list for model-specific feature importance techniques\n",
    "estimators = [\n",
    "    LogisticRegression,\n",
    "    XGBClassifier,\n",
    "    RandomForestClassifier,\n",
    "    KNeighborsClassifier,\n",
    "]\n",
    "\n",
    "# instantiate FeatureSelector object\n",
    "fs = mlmachine_titanic_machine.FeatureSelector(\n",
    "    data=mlmachine_titanic_machine.training_features,\n",
    "    target=mlmachine_titanic_machine.training_target,\n",
    "    estimators=estimators,\n",
    ")\n",
    "\n",
    "# run full feature selector suite, use accuracy metric and \n",
    "# 0 CV folds where applicable\n",
    "feature_selector_summary = fs.feature_selector_suite(\n",
    "    sequential_scoring=\"accuracy\",\n",
    "    sequential_n_folds=0,\n",
    "    add_stats=True,\n",
    "    n_jobs=1,\n",
    "    save_to_csv=True,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "## Exhaustively Iterative Feature Selection\n",
    "---\n",
    "<br><br>\n",
    "For our final preparatory step, we use this feature selection summary to perform iterative cross-validation on smaller and smaller subsets of features for each of our estimators:\n",
    "<br><br>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id = 'Exhaustively-Iterative-Feature-Selection'></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-03T05:05:50.977740Z",
     "start_time": "2020-04-03T05:03:58.196014Z"
    }
   },
   "outputs": [],
   "source": [
    "# use cross validation on progressively smaller feature subsets\n",
    "# to find optimal feature set\n",
    "cv_summary = fs.feature_selector_cross_val(\n",
    "    feature_selector_summary=feature_selector_summary,\n",
    "    estimators=estimators,\n",
    "    scoring=[\"accuracy\"],\n",
    "    n_folds=5,\n",
    "    step=1,\n",
    "    n_jobs=4,\n",
    "    save_to_csv=True,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "<br><br>\n",
    "From this result, we extract our dictionary of optimum feature sets for each estimator.\n",
    "<br><br>\n",
    "The keys are estimator names, and the associated values are lists containing the column names of the best performing feature subset for each estimator. Taking  `XGBClassifier()` as an example, we used only 10 of the available 43 features to achieve the best average cross-validation accuracy on the validation dataset.\n",
    "<br><br>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-03T05:05:51.011570Z",
     "start_time": "2020-04-03T05:05:50.979885Z"
    }
   },
   "outputs": [],
   "source": [
    "# create feature selector summary dictionary\n",
    "cross_val_feature_dict = fs.create_cross_val_features_dict(\n",
    "    scoring=\"accuracy_score\",\n",
    "    cv_summary=cv_summary,\n",
    "    feature_selector_summary=feature_selector_summary,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "<br><br>\n",
    "With our processed dataset and optimum feature subsets in hand, it's time to use Bayesian optimization to tune the hyperparameters of our 4 estimators.\n",
    "<br><br>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "## Outline Our Feature Space\n",
    "---\n",
    "<br><br>\n",
    "First, we need to establish our feature space for each parameter for each estimator:\n",
    "<br><br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id = 'Outline-Our-Feature-Space'></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-03T05:05:51.162807Z",
     "start_time": "2020-04-03T05:05:51.013587Z"
    },
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "# import libraries\n",
    "from hyperopt import hp\n",
    "\n",
    "# create hyperopt parameter space for set of estimators\n",
    "estimator_parameter_space = {\n",
    "    \"LogisticRegression\": {\n",
    "        \"C\": hp.loguniform(\"C\", np.log(0.001), np.log(0.2)),\n",
    "        \"penalty\": hp.choice(\"penalty\", [\"l2\"]),\n",
    "    },\n",
    "    \"XGBClassifier\": {\n",
    "        \"colsample_bytree\": hp.uniform(\"colsample_bytree\", 0.5, 1.0),\n",
    "        \"gamma\": hp.uniform(\"gamma\", 0.0, 10),\n",
    "        \"learning_rate\": hp.uniform(\"learning_rate\", 0.01, 0.3),\n",
    "        \"max_depth\": hp.choice(\"max_depth\", np.arange(2, 20, dtype=int)),\n",
    "        \"min_child_weight\": hp.uniform(\"min_child_weight\", 1, 20),\n",
    "        \"n_estimators\": hp.choice(\"n_estimators\", np.arange(100, 10000, 1, dtype=int)),\n",
    "        \"subsample\": hp.uniform(\"subsample\", 0.3, 1),\n",
    "    },\n",
    "    \"RandomForestClassifier\": {\n",
    "        \"bootstrap\": hp.choice(\"bootstrap\", [True, False]),\n",
    "        \"max_depth\": hp.choice(\"max_depth\", np.arange(2, 20, dtype=int)),\n",
    "        \"n_estimators\": hp.choice(\"n_estimators\", np.arange(100, 10000, 1, dtype=int)),\n",
    "        \"max_features\": hp.choice(\"max_features\", [\"auto\", \"sqrt\"]),\n",
    "        \"min_samples_split\": hp.choice(\n",
    "            \"min_samples_split\", np.arange(2, 40, dtype=int)\n",
    "        ),\n",
    "        \"min_samples_leaf\": hp.choice(\"min_samples_leaf\", np.arange(2, 40, dtype=int)),\n",
    "    },\n",
    "    \"KNeighborsClassifier\": {\n",
    "        \"algorithm\": hp.choice(\"algorithm\", [\"auto\", \"ball_tree\", \"kd_tree\", \"brute\"]),\n",
    "        \"n_neighbors\": hp.choice(\"n_neighbors\", np.arange(1, 20, dtype=int)),\n",
    "        \"weights\": hp.choice(\"weights\", [\"distance\", \"uniform\"]),\n",
    "    },\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "<br><br>\n",
    "The outermost keys of the dictionary are names of classifiers, represented by strings. The associated values are also dictionaries, where the keys are parameter names, represented as strings, and the values are hyperopt sampling distributions from which parameter values will be chosen.\n",
    "<br><br>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "## Run the Bayesian Optimization Job\n",
    "---\n",
    "<br><br>\n",
    "Now we're ready to run our Bayesian optimization hyperparameter tuning job. We will use a built-in method belonging to our Machine object called exec_bayes_optim_search(). Let's see mlmachine in action:\n",
    "<br><br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id = 'Run-the-Bayesian-Optimization-Job'></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-03T05:12:34.560028Z",
     "start_time": "2020-04-03T05:05:51.167395Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "####################################################################################################\n",
      "\n",
      "Tuning LogisticRegression\n",
      "\n",
      "100%|██████████| 20/20 [00:03<00:00,  6.42trial/s, best loss: 0.18967422007406953]\n",
      "\n",
      "####################################################################################################\n",
      "\n",
      "Tuning XGBClassifier\n",
      "\n",
      "100%|██████████| 20/20 [01:16<00:00,  3.83s/trial, best loss: 0.16612265394513837]\n",
      "\n",
      "####################################################################################################\n",
      "\n",
      "Tuning RandomForestClassifier\n",
      "\n",
      "100%|██████████| 20/20 [05:16<00:00, 15.83s/trial, best loss: 0.16945577804280965]\n",
      "\n",
      "####################################################################################################\n",
      "\n",
      "Tuning KNeighborsClassifier\n",
      "\n",
      "100%|██████████| 20/20 [00:02<00:00,  7.45trial/s, best loss: 0.1784131567384346] \n"
     ]
    }
   ],
   "source": [
    "# execute bayesian optimization grid search\n",
    "mlmachine_titanic_machine.exec_bayes_optim_search(\n",
    "    estimator_parameter_space=estimator_parameter_space,\n",
    "    data=mlmachine_titanic_machine.training_features,\n",
    "    target=mlmachine_titanic_machine.training_target,\n",
    "    columns=cross_val_feature_dict,\n",
    "    scoring=\"accuracy\",\n",
    "    n_folds=5,\n",
    "    n_jobs=5,\n",
    "    iters=20,\n",
    "    show_progressbar=True,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "<br><br>\n",
    "Let's review the parameters:\n",
    "- `estimator_parameter_space`: The dictionary-based feature space we setup above.\n",
    "- `data`: Our observations.\n",
    "- `target`: Our target data.\n",
    "- `columns`: An optional parameter that allows us to subset the input dataset features. Accepts a list of feature names, which will apply equally to all estimators. Also accepts a dictionary, where the keys represent estimator class names and values are lists of feature names to be used with the associated estimator. In this example, we use the latter by passing in the dictionary returned by `cross_val_feature_dict` in the - `FeatureSelector` workflow above.\n",
    "- `scoring`: The scoring metric to be evaluated.\n",
    "- `n_folds`: Number of folds to use in cross-validation procedure.\n",
    "- `iters`: Total number of iterations to run the hyperparameter tuning process. In this example, we run the experiment for 200 iterations.\n",
    "- `show_progressbar`: Controls whether progress bar displays and actively updates during the course of the process.\n",
    "<br><br>\n",
    "\n",
    "Anyone familiar with hyperopt will be wondering where the objective function is. mlmachine abstracts away this complexity. \n",
    "<br><br>\n",
    "\n",
    "The process runtime depends on several attributes, including hardware, the number and type of estimators used, the Number of folds, feature selection, and the number of sampling iterations. Runtimes can be quite lengthy. For this reason, `exec_bayes_optim_search()` automatically saves the result of each iteration to a CSV.\n",
    "<br><br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Results Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id = 'Results-Analysis'></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "## Results Summary\n",
    "---\n",
    "<br><br>\n",
    "Let's start by loading and reviewing the results:\n",
    "<br><br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id = 'Results-Summary'></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-03T14:47:07.875266Z",
     "start_time": "2020-04-03T14:47:07.867374Z"
    }
   },
   "outputs": [],
   "source": [
    "# reload bayes optimization summary\n",
    "bayes_optim_summary = pd.read_csv(\"data/bayes_optimization_summary_accuracy.csv\", na_values=\"nan\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-03T14:47:07.899160Z",
     "start_time": "2020-04-03T14:47:07.877614Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>iteration</th>\n",
       "      <th>estimator</th>\n",
       "      <th>scoring</th>\n",
       "      <th>loss</th>\n",
       "      <th>mean_score</th>\n",
       "      <th>std_score</th>\n",
       "      <th>min_score</th>\n",
       "      <th>max_score</th>\n",
       "      <th>train_time</th>\n",
       "      <th>status</th>\n",
       "      <th>params</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>LogisticRegression</td>\n",
       "      <td>accuracy</td>\n",
       "      <td>0.204218</td>\n",
       "      <td>0.795782</td>\n",
       "      <td>0.026768</td>\n",
       "      <td>0.754190</td>\n",
       "      <td>0.825843</td>\n",
       "      <td>1.080047</td>\n",
       "      <td>ok</td>\n",
       "      <td>{'C': 0.008174796663349533, 'penalty': 'l2', '...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>LogisticRegression</td>\n",
       "      <td>accuracy</td>\n",
       "      <td>0.210922</td>\n",
       "      <td>0.789078</td>\n",
       "      <td>0.037798</td>\n",
       "      <td>0.720670</td>\n",
       "      <td>0.820225</td>\n",
       "      <td>1.232385</td>\n",
       "      <td>ok</td>\n",
       "      <td>{'C': 0.007456580176063114, 'penalty': 'l2', '...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>LogisticRegression</td>\n",
       "      <td>accuracy</td>\n",
       "      <td>0.196384</td>\n",
       "      <td>0.803616</td>\n",
       "      <td>0.027326</td>\n",
       "      <td>0.769663</td>\n",
       "      <td>0.848315</td>\n",
       "      <td>1.175930</td>\n",
       "      <td>ok</td>\n",
       "      <td>{'C': 0.015197336097355196, 'penalty': 'l2', '...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>LogisticRegression</td>\n",
       "      <td>accuracy</td>\n",
       "      <td>0.191890</td>\n",
       "      <td>0.808110</td>\n",
       "      <td>0.025218</td>\n",
       "      <td>0.780899</td>\n",
       "      <td>0.842697</td>\n",
       "      <td>1.189311</td>\n",
       "      <td>ok</td>\n",
       "      <td>{'C': 0.016970348333069277, 'penalty': 'l2', '...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>LogisticRegression</td>\n",
       "      <td>accuracy</td>\n",
       "      <td>0.196391</td>\n",
       "      <td>0.803609</td>\n",
       "      <td>0.014813</td>\n",
       "      <td>0.786517</td>\n",
       "      <td>0.825843</td>\n",
       "      <td>1.207008</td>\n",
       "      <td>ok</td>\n",
       "      <td>{'C': 0.03346957219746275, 'penalty': 'l2', 'n...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>LogisticRegression</td>\n",
       "      <td>accuracy</td>\n",
       "      <td>0.193064</td>\n",
       "      <td>0.806936</td>\n",
       "      <td>0.020473</td>\n",
       "      <td>0.780899</td>\n",
       "      <td>0.831461</td>\n",
       "      <td>1.180980</td>\n",
       "      <td>ok</td>\n",
       "      <td>{'C': 0.18501222413286775, 'penalty': 'l2', 'n...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>LogisticRegression</td>\n",
       "      <td>accuracy</td>\n",
       "      <td>0.292838</td>\n",
       "      <td>0.707162</td>\n",
       "      <td>0.044509</td>\n",
       "      <td>0.625698</td>\n",
       "      <td>0.747191</td>\n",
       "      <td>1.202759</td>\n",
       "      <td>ok</td>\n",
       "      <td>{'C': 0.0011479966819945002, 'penalty': 'l2', ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>LogisticRegression</td>\n",
       "      <td>accuracy</td>\n",
       "      <td>0.210922</td>\n",
       "      <td>0.789078</td>\n",
       "      <td>0.037965</td>\n",
       "      <td>0.720670</td>\n",
       "      <td>0.820225</td>\n",
       "      <td>1.166395</td>\n",
       "      <td>ok</td>\n",
       "      <td>{'C': 0.007362723951833776, 'penalty': 'l2', '...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>LogisticRegression</td>\n",
       "      <td>accuracy</td>\n",
       "      <td>0.195255</td>\n",
       "      <td>0.804745</td>\n",
       "      <td>0.023097</td>\n",
       "      <td>0.776536</td>\n",
       "      <td>0.837079</td>\n",
       "      <td>1.171910</td>\n",
       "      <td>ok</td>\n",
       "      <td>{'C': 0.028150535343720546, 'penalty': 'l2', '...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>LogisticRegression</td>\n",
       "      <td>accuracy</td>\n",
       "      <td>0.298456</td>\n",
       "      <td>0.701544</td>\n",
       "      <td>0.042828</td>\n",
       "      <td>0.625698</td>\n",
       "      <td>0.747191</td>\n",
       "      <td>1.179324</td>\n",
       "      <td>ok</td>\n",
       "      <td>{'C': 0.0010719047187090322, 'penalty': 'l2', ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>11</td>\n",
       "      <td>LogisticRegression</td>\n",
       "      <td>accuracy</td>\n",
       "      <td>0.191903</td>\n",
       "      <td>0.808097</td>\n",
       "      <td>0.014580</td>\n",
       "      <td>0.792135</td>\n",
       "      <td>0.831461</td>\n",
       "      <td>1.179505</td>\n",
       "      <td>ok</td>\n",
       "      <td>{'C': 0.04917602741481999, 'penalty': 'l2', 'n...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>12</td>\n",
       "      <td>LogisticRegression</td>\n",
       "      <td>accuracy</td>\n",
       "      <td>0.188551</td>\n",
       "      <td>0.811449</td>\n",
       "      <td>0.010974</td>\n",
       "      <td>0.797753</td>\n",
       "      <td>0.831461</td>\n",
       "      <td>1.170878</td>\n",
       "      <td>ok</td>\n",
       "      <td>{'C': 0.06940715377237823, 'penalty': 'l2', 'n...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>13</td>\n",
       "      <td>LogisticRegression</td>\n",
       "      <td>accuracy</td>\n",
       "      <td>0.241215</td>\n",
       "      <td>0.758785</td>\n",
       "      <td>0.043890</td>\n",
       "      <td>0.681564</td>\n",
       "      <td>0.803371</td>\n",
       "      <td>1.184253</td>\n",
       "      <td>ok</td>\n",
       "      <td>{'C': 0.0029054318718131663, 'penalty': 'l2', ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>14</td>\n",
       "      <td>LogisticRegression</td>\n",
       "      <td>accuracy</td>\n",
       "      <td>0.193014</td>\n",
       "      <td>0.806986</td>\n",
       "      <td>0.023733</td>\n",
       "      <td>0.780899</td>\n",
       "      <td>0.837079</td>\n",
       "      <td>1.156993</td>\n",
       "      <td>ok</td>\n",
       "      <td>{'C': 0.02173685952136848, 'penalty': 'l2', 'n...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>15</td>\n",
       "      <td>LogisticRegression</td>\n",
       "      <td>accuracy</td>\n",
       "      <td>0.193020</td>\n",
       "      <td>0.806980</td>\n",
       "      <td>0.015832</td>\n",
       "      <td>0.787709</td>\n",
       "      <td>0.831461</td>\n",
       "      <td>1.139732</td>\n",
       "      <td>ok</td>\n",
       "      <td>{'C': 0.042661619723085194, 'penalty': 'l2', '...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>16</td>\n",
       "      <td>LogisticRegression</td>\n",
       "      <td>accuracy</td>\n",
       "      <td>0.240092</td>\n",
       "      <td>0.759908</td>\n",
       "      <td>0.044651</td>\n",
       "      <td>0.681564</td>\n",
       "      <td>0.803371</td>\n",
       "      <td>1.172876</td>\n",
       "      <td>ok</td>\n",
       "      <td>{'C': 0.003325935337999729, 'penalty': 'l2', '...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>17</td>\n",
       "      <td>LogisticRegression</td>\n",
       "      <td>accuracy</td>\n",
       "      <td>0.195305</td>\n",
       "      <td>0.804695</td>\n",
       "      <td>0.019433</td>\n",
       "      <td>0.780899</td>\n",
       "      <td>0.831461</td>\n",
       "      <td>1.192614</td>\n",
       "      <td>ok</td>\n",
       "      <td>{'C': 0.15489548435948908, 'penalty': 'l2', 'n...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>18</td>\n",
       "      <td>LogisticRegression</td>\n",
       "      <td>accuracy</td>\n",
       "      <td>0.186310</td>\n",
       "      <td>0.813690</td>\n",
       "      <td>0.010923</td>\n",
       "      <td>0.797753</td>\n",
       "      <td>0.831461</td>\n",
       "      <td>1.204993</td>\n",
       "      <td>ok</td>\n",
       "      <td>{'C': 0.05931036664011822, 'penalty': 'l2', 'n...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>19</td>\n",
       "      <td>LogisticRegression</td>\n",
       "      <td>accuracy</td>\n",
       "      <td>0.201971</td>\n",
       "      <td>0.798029</td>\n",
       "      <td>0.028659</td>\n",
       "      <td>0.754190</td>\n",
       "      <td>0.831461</td>\n",
       "      <td>1.141704</td>\n",
       "      <td>ok</td>\n",
       "      <td>{'C': 0.008415834542277328, 'penalty': 'l2', '...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>20</td>\n",
       "      <td>LogisticRegression</td>\n",
       "      <td>accuracy</td>\n",
       "      <td>0.194143</td>\n",
       "      <td>0.805857</td>\n",
       "      <td>0.015445</td>\n",
       "      <td>0.787709</td>\n",
       "      <td>0.831461</td>\n",
       "      <td>1.147760</td>\n",
       "      <td>ok</td>\n",
       "      <td>{'C': 0.03837255565837678, 'penalty': 'l2', 'n...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    iteration           estimator   scoring      loss  mean_score  std_score  \\\n",
       "0           1  LogisticRegression  accuracy  0.204218    0.795782   0.026768   \n",
       "1           2  LogisticRegression  accuracy  0.210922    0.789078   0.037798   \n",
       "2           3  LogisticRegression  accuracy  0.196384    0.803616   0.027326   \n",
       "3           4  LogisticRegression  accuracy  0.191890    0.808110   0.025218   \n",
       "4           5  LogisticRegression  accuracy  0.196391    0.803609   0.014813   \n",
       "5           6  LogisticRegression  accuracy  0.193064    0.806936   0.020473   \n",
       "6           7  LogisticRegression  accuracy  0.292838    0.707162   0.044509   \n",
       "7           8  LogisticRegression  accuracy  0.210922    0.789078   0.037965   \n",
       "8           9  LogisticRegression  accuracy  0.195255    0.804745   0.023097   \n",
       "9          10  LogisticRegression  accuracy  0.298456    0.701544   0.042828   \n",
       "10         11  LogisticRegression  accuracy  0.191903    0.808097   0.014580   \n",
       "11         12  LogisticRegression  accuracy  0.188551    0.811449   0.010974   \n",
       "12         13  LogisticRegression  accuracy  0.241215    0.758785   0.043890   \n",
       "13         14  LogisticRegression  accuracy  0.193014    0.806986   0.023733   \n",
       "14         15  LogisticRegression  accuracy  0.193020    0.806980   0.015832   \n",
       "15         16  LogisticRegression  accuracy  0.240092    0.759908   0.044651   \n",
       "16         17  LogisticRegression  accuracy  0.195305    0.804695   0.019433   \n",
       "17         18  LogisticRegression  accuracy  0.186310    0.813690   0.010923   \n",
       "18         19  LogisticRegression  accuracy  0.201971    0.798029   0.028659   \n",
       "19         20  LogisticRegression  accuracy  0.194143    0.805857   0.015445   \n",
       "\n",
       "    min_score  max_score  train_time status  \\\n",
       "0    0.754190   0.825843    1.080047     ok   \n",
       "1    0.720670   0.820225    1.232385     ok   \n",
       "2    0.769663   0.848315    1.175930     ok   \n",
       "3    0.780899   0.842697    1.189311     ok   \n",
       "4    0.786517   0.825843    1.207008     ok   \n",
       "5    0.780899   0.831461    1.180980     ok   \n",
       "6    0.625698   0.747191    1.202759     ok   \n",
       "7    0.720670   0.820225    1.166395     ok   \n",
       "8    0.776536   0.837079    1.171910     ok   \n",
       "9    0.625698   0.747191    1.179324     ok   \n",
       "10   0.792135   0.831461    1.179505     ok   \n",
       "11   0.797753   0.831461    1.170878     ok   \n",
       "12   0.681564   0.803371    1.184253     ok   \n",
       "13   0.780899   0.837079    1.156993     ok   \n",
       "14   0.787709   0.831461    1.139732     ok   \n",
       "15   0.681564   0.803371    1.172876     ok   \n",
       "16   0.780899   0.831461    1.192614     ok   \n",
       "17   0.797753   0.831461    1.204993     ok   \n",
       "18   0.754190   0.831461    1.141704     ok   \n",
       "19   0.787709   0.831461    1.147760     ok   \n",
       "\n",
       "                                               params  \n",
       "0   {'C': 0.008174796663349533, 'penalty': 'l2', '...  \n",
       "1   {'C': 0.007456580176063114, 'penalty': 'l2', '...  \n",
       "2   {'C': 0.015197336097355196, 'penalty': 'l2', '...  \n",
       "3   {'C': 0.016970348333069277, 'penalty': 'l2', '...  \n",
       "4   {'C': 0.03346957219746275, 'penalty': 'l2', 'n...  \n",
       "5   {'C': 0.18501222413286775, 'penalty': 'l2', 'n...  \n",
       "6   {'C': 0.0011479966819945002, 'penalty': 'l2', ...  \n",
       "7   {'C': 0.007362723951833776, 'penalty': 'l2', '...  \n",
       "8   {'C': 0.028150535343720546, 'penalty': 'l2', '...  \n",
       "9   {'C': 0.0010719047187090322, 'penalty': 'l2', ...  \n",
       "10  {'C': 0.04917602741481999, 'penalty': 'l2', 'n...  \n",
       "11  {'C': 0.06940715377237823, 'penalty': 'l2', 'n...  \n",
       "12  {'C': 0.0029054318718131663, 'penalty': 'l2', ...  \n",
       "13  {'C': 0.02173685952136848, 'penalty': 'l2', 'n...  \n",
       "14  {'C': 0.042661619723085194, 'penalty': 'l2', '...  \n",
       "15  {'C': 0.003325935337999729, 'penalty': 'l2', '...  \n",
       "16  {'C': 0.15489548435948908, 'penalty': 'l2', 'n...  \n",
       "17  {'C': 0.05931036664011822, 'penalty': 'l2', 'n...  \n",
       "18  {'C': 0.008415834542277328, 'penalty': 'l2', '...  \n",
       "19  {'C': 0.03837255565837678, 'penalty': 'l2', 'n...  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bayes_optim_summary[:20]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "<br><br>\n",
    "Our Bayesian optimization log maintains key information about each iteration:\n",
    "- Iteration number, estimator and scoring metric\n",
    "- Cross-validation summary statistics\n",
    "- Iteration training time\n",
    "- Dictionary of parameters used\n",
    "\n",
    "This log provides an immense amount of data for us to analyze and evaluate the effectiveness of the Bayesian optimization process.\n",
    "<br><br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "## Model Optimization Assessment\n",
    "---\n",
    "<br><br>\n",
    "First and foremost, we want to see how if performance improved over the iterations.\n",
    "<br><br>\n",
    "\n",
    "Let's visualize the `XGBClassifier()` loss by iteration:\n",
    "<br><br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id = 'Model-Optimization-Assessment'></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-03T05:12:35.375647Z",
     "start_time": "2020-04-03T05:12:34.819790Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x540 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# generate model loss by iteration plot for XGBClassifier\n",
    "mlmachine_titanic_machine.model_loss_plot(\n",
    "    bayes_optim_summary=bayes_optim_summary,\n",
    "    estimator_class=\"XGBClassifier\",\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "<br><br>\n",
    "Each dot represents the performance of one of our 200 experiments. The key detail to notice is that the line of best fit has a clear downward slope -  exactly what we want. This means that with each iteration, model performance tends to improve compared to the previous iterations.\n",
    "<br><br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "## Parameter Selection Assessment\n",
    "---\n",
    "<br><br>\n",
    "One of the coolest parts of Bayesian optimization is seeing how parameter selection is optimized.\n",
    "<br><br>\n",
    "\n",
    "For each model and for each model's parameters, we can generate a two-panel visual.\n",
    "<br><br>\n",
    "\n",
    "For numeric parameters, such as `n_estimators` or `learning_rate`, the two-visual panel includes:\n",
    "- Parameter selection KDE, overplayed on a theoretical distribution KDE\n",
    "- Parameter selection by iteration scatter plot, with line of best fit\n",
    "<br><br>\n",
    "\n",
    "For categorical parameters, such as loss function, the two-visual panel includes:\n",
    "- Parameter selection and theoretical distribution bar chart\n",
    "- Parameter selection by iteration scatter plot, faceted by parameter category\n",
    "\n",
    "Let's review the parameter selection panels for `KNeighborsClassifier()`:\n",
    "<br><br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id = 'Parameter-Selection-Assessment'></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-03T05:12:37.842100Z",
     "start_time": "2020-04-03T05:12:35.378139Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "****************************************************************************************************\n",
      "* KNeighborsClassifier\n",
      "****************************************************************************************************\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 2160x453.6 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 2160x453.6 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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hRMeYtI67WV6GwqZG+N5tZxTiMA6WxsZoEwrVHudoZga/YVQKqVLU3ITv7iTiWG42uiQScNlsPODjhycnTkL43Sqev82ai+SaaoWZS/2Nt3fA05OmaG1N9yO5XI6nTx5TmK/VX7dEgt+fPo6L2x7FOHNzPa9OM09rG0ZxJlwuHNVUiQxXl1iMVmE3rIxN+ma7KdPMoIoNAJo1VJExweUwT1wPjH1v/gNo7BIgVklbN3MjI3yzbOWIK9gI0eStuQuQ01iPoubmYR1f1taq8L8TqyqRWFWJv8yYhaeilH9nBDk44qNFS/DBgkUQiMUwMzICdwgPgbw3fyGau7uUPkBgZWyC72JWIWTcOMxw98T1cvVtE/1sbFHNcKZfdn0d4zXq2+qgCeBy2PjXjWt9LZwBwIjNwYrx4/GPOQvAGyMPtMjl8r4qXztTU7BYI2/teT/RekIsPz8fhYWFWL9+PaysrCCVSiGVSmE0oK93XV0dSktLMW3atL5kGABYW1sjMjISt27dQmtrq9qEGiGEEEIIIYQQQgyPQCxGJsMbY4nVVX0JMVMeD+snhOCH5CS1x2wKDtPajauk6kpsP3oIHaLfqnskMhlOF+ThfFEBPlm0FMsCAuFuZYUD6zfj/etXcbG4sK+FlgmXi5WBQfjrzNmwNKZ5RiNxo7wMOQ31amMEYjH2ZKThxejpelqVIrFUimtlJajr7ISVsQnmeXn3JZ8C7ewRMc4ZKbXVas8REzBeq/Pvsuvr8M2d2ziTnw+RTAoem41Fvn54MmoKQpW0FbXnm2lsmQgAjmYjTzpGObvAlMtFl0SiNs7HxgYu5hYK20x5PPxv5VpcLS3G3ox0lLS2gM/lYZGvHzYEh8DWlD/i9RGiiT2fjwPrN+PrpNs4kJnR1xY1wNYO64NDcTIvBym1NUM+779uxGKmuyeC1bT+5XE4sBrGd50Jl4cfYlbjRnkZ9mamobSlBXyeERb5+mHdhGBY3p3R9+zkqYirKFPbXvG5KdH44U4io+sa+izMmIDxWOoXgGtlJShr7fmZzPPygT1/bHyWiKVS/JSWgl3pqShp6UnQelpZY2toOB4Oj6QKdYa0nhDLzOwpjbS1tcWXX36JrKwsyGQyODo6YsmSJZg6dSoAoLS0J/vs4+Mz6Bze3t6Ii4tDWVkZQkNDtb1EQgghhBBCCCGEGIoBN+Jeip6B5Ooq3KlRnlSY5uaBZ7RUiSWUSPDsyeMKybD+JDIZXjl/GpNcXDHO3BzuVlb4atkK1HS0I7uhHmywEOHkbJAzU8aii8WFjOIuFBWMSkLsl7QUfHH7Fuo6f6sStDQ2xu/CI/HC1Olgs1j484xZ+N2RAxCrmDlkY2KCZydN1dqarpWW4KkTRyGU/pZwEstkOJmfh/OFhfhi6XIsHNDybW1QMN6/flXteblsNlYEjnw+l6WxCVaNn4A9GWlq47aFRSitcmCzWJjn5YN5XoPvHxKiL9Ympnh1xmy8HD0DdZ0d4LLZcLqbwH0oNBxHcrPxa0YaCpubYMrlIcjeHteUVDYO9HNaCj5Y+KBO1sxisTDTwxMzPTxVxkS7ueOjRUvwlwvnFD5DAIDDYuEvM2ZjZWAQchrqkcHgIRd11zIUHDZ7TH6eCCUSPHH8yKCKvtLWFrx//SqulBbjh5jVfXMYiWpa/wnV1tYCAHbt2gVHR0c8/PDDkEqluHDhAnbu3AmZTIZp06ahtbWnXLR/dViv3m29MQMJBAJ0DXiShc1mw8aGWWk6IYQQQgghhBBCdIfP4yHAzh55jQ0aYyMGzHHi83j4efV6fJ+ciF8z0lB9d66Qm6UltoaGY3tElNaegj5dkKeyBWIvkVQ6qCLJydyi72Yo0Z4uhnO1NFUb6cJXifH48Ob1QdvbhEJ8ntCTJPvngkWIdnPHN8tX4bWL51DT2aEQ629rh08XL4Onknthw9EhEuH50ycG3cjuJZJJ8eLZU7i+/QlYm5j2bd8QHIIdqXcU2oYNtDkkTCsVYgDw15lzkFlfhzQVVTRL/QKwLSxSK9ca68paW3CmIB/tIiFcLSyxzD8QFgwqT6UyGfIaGyCSyeBlZU1Jeh0x4nDgZqnYzcyYy8XG4FBsDP6tqOP92CuMEmJMZnPp2orAIMxw98S+rHQkVFZAJpcjbJwTNoeEwcWipyXp1tBw/JCc1FcZrYyjmRkW+/rra9kAeiqGi5ubwGGz4WdrN6S2kmPNl4nxattb3iwvwxe3b+GVaTP1uKqxSesJse7ubgCAiYkJXnzxRXDvZiXDw8Pxxhtv4OjRo5g6dSpEd5++4irJWvLulpqLVDyhdenSJZw6dUphm62tLd59912t/TsIIYQQQgghhBAyfA+FhuPvVy6qjZns4opAO/tB2015PDw/ZRqenTQVNR0dAAtwNrcAW8tzMpTNJlIeVzJqLfruJ94MH3T2ZjirS1vqOjvw6a2bamP2ZqZjU3Aowp2cMdfLG9e2P4ELRYXIqKsFh81CtKs7prl7aHVdh7Iz0S5SP29PIBbjQFYmHp84qW+bpbEJflq1Do8fP9LXdqu/dROC8cbseVpbp7mREXav2YD/pdzBnozUvkRcoJ09toVFYFNImNbf22NNh0iEVy+cxZnCfMj6Vc2+G3sFz0yagucmRys9TiKT4Zuk29iVltKXgDXmcLE8IBAvRk+HqwXNWBsNEjUtCPuTylUnmPTJjs/HM5Om4hkV1atullb454JF+MuFswqvz17mPCP8d2mM3mZwNQoE+Cz+Jo7kZKND3JM/cDQzw+aQMDwdNeWeq5LqfTBHk18z0vH8lGnUOlEDrb86epNZkyZNUkh28fl8hIWFIT4+HrW1tX0zxSRKnuoR330iaODcsV7z58/HtGnTFLax7+EMMCGEEEIIIYQQMtZsDA7FhaJCXCsrUbrfxsQE781/QO05OGw2XC11d0NXLJMyjDOMm5b3ujVBwfgk7iZEGn4vm0PC9LSiHvuzMhi9BnZlpCLcyRlAT8vBxX7+WOynu4qJG2qqBfq7XlaqkBADAB8bW5x/6BFcKC7EmYJ8CMQiuFtaY2NwKPzt7LS+Vj6Ph+cmT8Uzk6agsUsADotFM8DukshkePzYYSRUVQzaJxCL8VHcDUhkMrwwVTEpL5XJ8OzJY7gwoNWoUCrBwexMxJaVYN+6TfCw0k5FImEuTM1csP5CGMYZgrVBwXC3tMK3SbdxpbQYMrkcJlwulvkH4umoyX2zQHWtXtCJDft/RWlri8L2us5OfBYfh9tVlfhxxZp7KimU29iABoFAY1xjlwA/pSajQyQCl83GNHd3RDm76mGFY4vWE2K9bQstlfzBamXVU1IqEAj6/rulpWVQXO+23piB+Hw++GNk2B0hhBBCCCGEEHI/4nE4+DZmFb5IuIU9GalovDv6gMtm4wEfP/xx+swhVfpIZTLcrqpES3c3nMzNEXE38TAS4+0ccAK5GuOC7B1GfC2imQPfDM9MnoLP4uNUxszy8MQ8b/3Of8lt0Nz6cyhx2sI0UasqjsNm40FffzyoxzZnbBYLDnyzQdsLmxqxOyMN+Y2NMOJwMNvTC2uCgmGu4mF5VWRyOSrb2iCTy+FiYaG3ipWROFeYrzQZ1t9XiQl4KDQCdv3uh+7NTB+UDOuvrrMTr1+6gJ9Wr9PaWgkzS/wC8M61y2i+20lNlYdCI/S0Iu2Y4uqGKa5u6BCJ0C4UwsbUBCZcnl7X8M61y4OSYf3dLC/Dj8lJeFpLs0YNgUTK7OEdAIrzIW8BIQ6O+OTBpXpLWI4FWk+IeXl5ITY2Vmmiq7m5pwzbwsICFhY9vbaLioowY8YMhbji4mKwWCx4eGi3lJwQQgghhBBCCCH6Y8Th4OVpM/D7KdFIr6uBRCqDj40tHMwG3xBXZ0fKHXx353bfPDEA8LOxxYvRM7DUP2DY61sfHIL/xMdprEjaEho+7GuMBRVtrahoa4OZkRFCHBzBGsX2dS9MnQ5jDhdfJsajo98oDQ6LhdkeXngyajIEYnFfoqRNKMSh7EzcqakCCyxMcXXDqsAgmA0xkaIO06SKvisSJtg74kpJscY4Q07oyuVyvBd7FT+mJClsv1RShI9v3cBXS1cwajUplkrxY0oSdqWnoqKtDQBgz+djw4RQPBU1mdEcrtGyNzNdY4xIKsXhnCyFSr9f0lM1HnejvBTFLc16bzPaSyiRILuhHhKZDH62tgqz7O5lxlwu3pu/CM+fPg6pivaJa4OCMdPDU88r0w5zI6MhJ6u1oV7QibMF+Rrj9mSk4cmoyfdMK1YfG1sYc7gq50Wqk1Ffh00H9+HIxq06rbgfS7SeEAsPD8f+/fuRkJCAxYsXw+TuEMfW1lakpaXB0dERjo6OAAAPDw8kJycjJiYG1ncHira0tCA5ORkBAQEqK8QIIYQQQgghhBAydhhxOMNu2/N+7BV8n5w0aHtBcxN+f/o43hc+gE3DbKHnwDfDH6fPVHyieoDNIWEIH+c0rPMbuuTqKnx06wbiysvQe8vWw9IKj0ZG4eHwyFFb19OTpmBbWAROF+ShtKUFybXVyKqvw+XSYlwuLYYZj4cVgUEIsnfAP69fRVe/cRzH83LwrxvX8NEDS/CAr59W1jPb0wuHc7I0xs3x9NbK9ZjaHBKGr5MSlM706cUCsCVUvy0mh+LLxIRBybBebUIhnjxxBEc3PQQfG1uV5xBLpXji+JFB7VkbBAJ8mRiPKyVF2L12IywNNClWoqSoQGlcv6qYdqEQOQ31Go+RA0iqqtR7QkwokeCz+Djsy0xHU3dPdbAxh4tl/gF4ZdpMON8tlLiXLfbzxw8r1uDfN2ORUV/Xt93OlI9HIiJVzusiqqVUVzOqjC1va0V1R/s9M0PPysQEywMCcTA7c1jHN3YJ8FVSAt6dlhzGugAAIABJREFUt1DLKxubtJ4Q4/P5WLNmDXbv3o0PP/wQ06dPh0QiQWxsLCQSCTZs2NAXu379enz22Wf4+OOPMXfuXADAlStXIJfLsXbtWm0vjRBCCCGEEEIIIWNIZl2t0mRYf29fu4zFfv7Drjx4fOIkWBgb4z/xcajuaO/bbmNigu0RUXhu8r150/J6WSkeP34YogGtmMraWvHW1UsobmnGm3Pmj9LqADMjIyzzD8TDRw4gqbpKYV+nWIw9GWkqj+0QifD708fxy5r1mOziNuK1LPELwD+vX0VdZ6fKGGMOFxtDQkd8raFwtbTEC1On4ZNbN1XGPD1pitpk0mjqlojx/Z1EtTGdYjF+SE5SO2/w++RElbMKASCroR7/unFN48zC0WLKY9Zyjs8d3m1c1elS3RBKJHjk6EHEVyq2gRRKJTiUk4W4ijLsW7/5nklWqDPb0wuzPb2QWVeLivY2mBsZYbKL2z0130qV7IZ6/JqRhqLmJphwuVjo7YsVgUGMX+/KyIfyatb3C1/HXo6egRtlpajp7NAcrMTRnCy8PmuO3ltcGiKtJ8QAYObMmTAzM8P58+dx/PhxsFgseHt7Y/v27fD19e2L8/X1xYsvvojjx4/j+PHjAAAfHx88/vjjcHMb+R8shBBCCCGEEEIIGbt2q0l69OqWSHAgK1OhldhQbQwOxbqgYNwsL0NNZwdsTEwwy8MLxsO8AW3oJDIZ/nT+zKBkWH87U5PxoK8/ot3c9bgyRd8k3R6UDGNKLJPhvwnx2LFq5PeXjDgcfL1sJX535CDaRcLB+9kcfLp4qdLZWLr2/JRpsDExxZe34xVulDqameGpqCnYHjFR72ti6lJxMVqF6mcsAcCx3Gy8O2+h0laeUpkMuxi0DjySk4W/zJhtkFViD/r6Ia9R8/y5Rf1mvVkYG8Pf1g75TY0aj5uohXmLQ/G/lDuDkmH9VXd04J2rl/H18pV6XNXoCnYch2DHcaO9DL2QymR44/IF/DqgFejF4iL8O+4GvotZNewZoKGOTuCwWCrbUPZyMjOHk7n5sK5hqJwtLLB//Wa8eeUirpQW91UGs8As99cpFqOusxMeVtY6XedYoLO/7CIjIxEZqbm83sfHBy+88IKulkEIIYQQQgghhJAxKr2ullFcRj2zOHU4bDZmeXqN+DxjwfmiAtQyeMr8l7SUUUuISWQyRrOV1IktK0FtRwfGaeHGaISTM45u2oofkpNwNCcbHWIRjDgcLPELwGORUQgZxZvdD4VFYFNIGOIqytAoEMDWlI/p7h7gstmjtiYmGgSqK+766xSLIRCLlc6FK2trRVV7u5KjFHVJJEitqTbI9/im4DD8kJwEgVisMiZsnBMmuSi2nd0aGo63rl5Se+5pbu7wtbXTyjqZkMnl2J2hOUF5sbgQVe1tcLkPqsTUyaqvw8HsTNR1dsDaxBQxAeMxxXVsF4l8eDN2UDKsV2OXANuPHsKJLduGVSHobGGBBT6+OFdYoDZuU0gYOAb++TccrpaW+H7FalS0tSK5phpAT0Xmny+cZXT8SKrz7iX35qNOhBBCCCGEEEIIGfM4SipClMfdeze+dCmZYdVVcs3wqrO0obytlVHSTh05gDpBp1YSYgDgZW2Dd+YtxFtz5qNTLAKfZ2QwSScum41ZHl6jvYwhsWdYUWfG44Gv4kaujME8oV6aqkpGi7OFBb5cugLPnDyqMA+vl7e1Db5aumLQ9s0hYbhUXKSyXaSdqSne0fPMoLrODlS0tWmMk8rlSKmpuW8TYl1iMV46ewrnihQTO7vSUzHJxRVfL1sBW1P+KK1u+NqE3fgpLUVtTKuwGz+nJuPVmXOGdY03Zs9DWk2NytaBkU7OeGIEFeNjgZulFdwsrQD0tJ59N/YK2oSDq5f7i3RyHpUqZkNkGN/ahBBCCCGEEEIIIQMwrU6aNopt/e5tzBKShszGxETr5+Sw2bA0NhmUDEupqcY/rl7CC2dO4t1rV5BdX6f1a99L5nt7w8pY8+9nRWCQ0naJQM+NYWsGv2Mum40ge4chr1FfZnt64fTW3+GxyCg4m1vA3MgIAXb2eH3WXBzZuBXOFhaDjuFxOPg2ZhWenxINu37JEx6bjeX+gTiwfovBzo8DACbPO3SIRPgpNRmbD+5FzJ6f8dSJI7hQVNDXLm6sUpYM65VYVYlHjx2GdAjJXkNxuiAf3UqSugMdzske9jVcLSxxYMNmrAgcDyP2b7PYLIyMsT1iIn5evf6+qoQy4fKwJSRcY9xjkVF6WM3YQBVihBBCCCGEEEIIMUhbQ8PxQ3ISJGpuDNrebTNFmJvs4obvk5M0xg1s0aZPbhaWcOCboZ5hWz1lIp2c+56i16U2YTeeO3UcN8rLFLb/mJKEB3x88cmDy1RWON3PTLg8PD5xEj6Ku64yxozHU3sj15jLxbqgYI2v54XevlqrFNQVDytr/G3WXPxt1lzGxxhxOHgpegaemxyNzLpaiGUy+NjYwp4/OtVFjmbm8LC0Qllbq9o4DouFiHHq50hl1tVi+7FDaBAIfttWX4fzRYWY7OKK72JWG+RMOE0y6mpVJsN6pdXW4FJxER7w9dPTqrSj/+9KncYuZnGquFhY4tMHl6FxlgB5jQ3gsNkIcRx3337OvhQ9HYXNjThfVKh0/3OTp2Kpf6CeV2W4qEKMEEIIIYQQQgghBsnN0grvzFsItopSAhMuF58tWQZjLj3vOxTzvX3goqTiZKBtYRF6WI1yPA4Hm0JCR3SOp6OmaGk1qsnlcjx+/MigZFiv80WF+MPpEzpfx1j17KQpeCwySmktopWxCb6LWa2xyum5ydHwVzMny9HMDK/NGl57trHCiMNBpLMLpri6jVoyDADYLBa2hGquVlng46u06q1XS3cXHjl6SGWC5XZVJV4+e2rY6xxNB7MztRpnSJi+9rT1GrXj8zHN3QNTXN3u22QY0PN9+eXSFfh88XJEu7rD3MgIVsYmWOIXgN1rNuCVaTNHe4kGhf5iJIQQQgghhBBCiMHaGBwKN0tLfJt0G9fLSiFHT0uwRb5+eGbSVExwcBztJY45HDYbHy9aiu1HDyqdWQQAT0ZNHtUKMQB4KmoKrpWWILW2Rul+K2NjtCqZm8JmsfD6rLl6qa64VlqCxKpKtTGXSoqQWlONcCfVFTE1He0QiMVwMre4r27sslgs/G3WXGwOCcPu9DTkNzXAiMPBXC8frAoMgpmRkcZzWJmYYO+6jXgv9ipO5OVCKO15TXNYLCzw8cXrs+bqpVKQ9HgkYiKulpYgrkJ5ktjFwgJvzp6v9hz7szI0VhFdKilCbmMDAu3sh73W0VDbwWw2oqoZWQOJpFKcK8xHflNjz3vH0xvBjuNGssRhW+Lnj7evXlL5vdJr1fgJelqRIolMZjBzH7WNw2ZjWUAglgVQJZgmlBAjhBBCCCGEEEKIQZvh7okZ7p5o6hKgTSiErSl/TLbKMiRTXN2wb90m/CchDpeKiyC9O5MnwM4ej0VGYf2EkFFeIcDn8bBrzQZ8eusG9mdlolXYDaCnMjAmYDz+OH0m2rq7sSs9Fck11WCBhcmurtgaGg4PK2u9rJFpFcf+7EylCbEjOVn4MeUOMupqAfT8m2MCxuO5yVPvqySOj40tXp89d9jHW5uY4sMHFuO1mXOQUlsNmVyOYAdHOJlrroQk2mXE4eDHFavx39vx2JOR1pfYMr37vn0peobG9pXHc3MYXet4bg4Cp4+t6hcmM+8AZvMPj+Vm451rVxSShx/F3cAkF1d8+uBSuFhYDnudTDV3daG4pRmcu3P6toVH4tuk2yrjrYxN8LAeq48Tqyrxv5Q7uFhUCJFMCg9LK2wKCcOW0PBR+ztCLpernItIdI8ll4/xKYQG6gH2+tFeAhmDzsv2w+c/H432MsgYU/SHV0Z7CURP6LuFDMd52f7RXgIhhBAtqG5vR25jA7hsNiKcnGHOoGqCEKbqOztR1dEOcx4Pvmpaz42mbokY2fX1kEEOf1s7WBozu6msaxsO/KqxQgwA5nv54PsVqxW2fXD9Kr69k6g03s7UFHvWboSfgf4+CGFCJJUit7EBMpkM3ja2jBMQs3d8h4q2No1xW0LD8e68hSNdpl7FlZdh62HN/z/avx9YjDVBwSr3n8rPxfOnT0DVjX0PSysc3rgVNqamw1ypepVtbfgo7jpOFeRBJJUC6GmFuCk4FHWdndiXlTHoGDtTPr6PWaW2WlabdqbewdtXLyv9GfnZ2GLX2g1w4JvpZS0dIhF+zUjDr5lpKG5uhjGXiwXePngkYiKinEe3Gvt+QxVihBBCCCGEEEKIgSpuacYH168qVPCY84ywJmgC/jR9FqN2YoRo4mBmBgcz/dwUHC4TLg+Rzi6jvYxBrBkm5gZWhVwvK1WZDAOAxq4uvHz2FI5t3jai9REymow4HIQOo33fODNzRgkxJzP1lWaGaJq7ByY6OeNOTbXKGA9LKyzzV936TiaX45/Xr6lMhgFAWVsrdqYm48Xo6SNYrYpzt7Zgw4FfUdfZqbC9QSDAF7fjMcvDE8c3PYQD2Zkoam6CCZeLBd6+WBE4HiZc/bSETampVpkMA4CC5ib86dwZ7Fi1VudraRQI8NDh/chtbOjb1i2R4GR+Hk7l5+HNOfPxcHikztdBetybTTMJIYQQQgghhJAxrqi5Cev37cH5osK+ZBgAdIhF+CktBdsOH0CXWKzzdTR1CXCrohwJlRUQ6OF6hIwV3RIx7Pl8RrExAeMV/vfPackaj8mor0NyddWw1kbIWLZOTWVULzaLhdVBozOLaqS+Xr4KISrmX3pYWmHHqrUw5qquY7leVorKds0Jw72Z6cNeozp/v3xxUDKsv9iyUtyuqsSbc+Zj56p1+Gb5KmwIDtVbMgwAdqTeUZswBIBrZSUobGrU+Vr+fOGsQjKsPzmAf1y9RJ/1ekQVYoQQQgghhBBCiAH6x9VLaOruUrk/pbYaP6bcwXOTp+rk+jUd7fi/G7EK7ZDMjYywLigYL0+bSW0byX3tYnEh/njuTN9cM3WCHRwx29NLYVtcRTmj69ysKDfIyjhCdCWvsQHpdbUw5nAgvPvdo8zG4FC46mFGli7Y8/k4tHErzhcV4GBWJuo6O2BtYoqV44Ow3D9QbTIMAEpamhldp7azA90SsVYTUSUtzYgtK9EY90t6Ch6JmKi16w7VpeIiRnGXS4p12iq4uKUZV0rUr0UOYEdqMn3W6wklxAghhBBCCCGEEANT0tKM62WlGuP2ZKTimUlTwB7hcPbW7m6k1dUA6Ll5L5RIsf7AHlS1tyvEdYhE2JGajDs11di9ZgP4PP097U2IoUisqsSzJ49BLJNpjA2wtcN3MavAGvAelck11S70xmm+xlBVtLUit6EBHDYbE51dGM91IvenirZW/JKWgrOFBegQieBpZYUNwaFYGRikMXEzVB/ejMVXiQlqY1gA1k4Ixltz5mv12vrGZbOxxC8AS/wChnysKcPvXi6bDSOOdn9HKTXVGiuvAKCouRnNXV06m2E20K2KcpwuyEO7UAhXS0sIJaqTqf11SyQ6Xdel4iJGP69LxYU6XQf5DSXECCGEEEIIIYQQA5NeV8voBkpVezvW7duDstYWGHM5mOfti4fDIhBgZ8/oOk1dAnxw4xpO5OX23RQy4nDgwOcPSob1l1Zbg2+SEvBS9AxG1yHkXvKf+DiNyTBLY2O8PXcBFvsFwIjDGbQ/xGEcEqoqNF4r1NFp2OscqKCpEe/FXsW10uK+zxc+j4dV4yfg1RmzqeqTDHK5pAjPnTqukDRo7BLgTk01fklLwc5V67SW8Pg5LUVtMowFYHnAeLwwdRp8bGy1cs2xaq6XN4zYHIhk6pM+8719RvzAjKGr7ejAUyePIq22ZljH++mwOgwAhAwTbuqqIYl20QwxQgghhBBCCCHEwLDB/AZWSm01mrq7UN3Rgd3pqVi+52cczM7UeFxLdxc2HtiLA1mZCjc7RVIpKtUkw3r9mpEOMd3AIfeZ6vZ23CjXXL3ZJhQibJyT0mQYAGwNC9d4Dg9Lq0GtFocrv7ERGw78iqv9kmEAIBCLsTs9FdsO79fLTEIydpS1tgxKhvWXUV+Hl86eYnSuRoEAnyfEYd7OHzDhy88w48dv8MH1q6hs65mDJZXJ8E2S+sowOYA2Yfd9nwwDAAe+GWICx6uNYQF4NCJK69eOdHJh9BeKr42tzqvDuiViPHzkwLCTYY5mZljo46vlVSnytWX2eqXXtf5QhRghhBBCCCGEEGJgolxcwGWzIWHQkm0giUyGVy+chb+tHcLGqa4u+Sw+DoXNTcNeY72gE5XtbfCythn2OfShSyzG7apKCCUS+Nra0k0nMiK1nR2MqjcBoKajQ+X7Y5l/IE7m5eJcUYHS/UYcDt5fsEhr1R1vXb2Ilm7V885Sa2vwv5Q7eFZHMwkNlVQmw9nCfOxKT0NOQx24bA5menji4bAIhDs5j/byRtWu9FSN7eSulZUgr7FBbVVyQVMjth0+gNrOjr5t1R0d+PZOInZnpOH7mNWQy+Vqq5L7rldaotc2fIbsH3MXoKKtFfGVgytNWQDenDMfU1zdtH5dT2trzPb0wtXSErVxDzFI+gNARl0tfkpLxs3yMshkckxwcMSW0HDM8/Ie1Gp2oKO5OchvamS6dAUsAK/PmgsuW7f1Qgu8fTHOzFzh9a/MlpAwna6D/IYSYoQQQgghhBBCiIFxMrfAAz5+OF2QN6zjpXI5dqTcwccPLlW6XyAWM6oi04Q1hEo2fRNKJPj41g38mpGOdpGwb/tUVze8OnMOwtUkC0mPNqEQp/NzUdvZCWsTEyzxC4CDmdloL2tUDWXelpWaWDaLhS+WxuDTWzexOyNVIVkV5eyCv8yYjUkuriNaa6/CpkbEVZRrjNuTkYantTCTcKwQSaV49uQxXCopUth+OCcLR3Ky8OrM2Xhi4uRRWt3oO1OQzyjubGG+yoSYVCbDE8ePqEwGdIhEeOrEUfx99lxG15IDaBV2U0IMPe1Od65ahxN5Odidnoq8pkYYcziY6+WDbWERah+IGar6zk5k1NeCDRbCxjnh7bkLsf7AHtR1diqNn+3hhS0hmhNi3ybdxr9uXFN4yKCmswOXSooQEzAeHy9aAo6ahNWBrAxG62cBCtdwsbDAazPnYKl/IKPjR4LLZuNvs+bghTMnVT5MEezgiPUTQnS+FtKDEmKEEEIIIYQQQogBemvOfGTV16G0tWVYx58tVH0zs6i5CR0i0XCXBgBwNreAm6XliM6hKxKZDE+dOIprZSWD9sVXVmDLwb34afU6RDlrJ+Fwr5HL5fg0/iZ+SE6CoF8bvfdir2DdhBD8ffY8GHPvz1tKPja2CHZwRGZ9ndo4f1s7BDk4qo3hstn44/SZeH5KNBKqKtAtlsDL2gb+dtqdaZNep36tvSrb29DU1QV7Pl+r1zdUH96MHZQM6yUH8M/r1xBga485Xt7oEIlgxOGobIF5L+pk+B2hLu58UaHG77BWYTfS62oZXYvLZsPW9P54fTJhxOFgTVAw1gQF6+T81e3teC/2Cs4XFfTNTTTmcLE8IBA/rliNH5Pv4GR+HoTSnkpCB74ZNoeE4dnJU8HT8F65VFyED25cU7n/eF4OvKyt1c4qrelQX3XVa9X4IEQ5u6JLIoG3tQ3meHqpTbRp2/KA8WCBhfevX0F1vzWzWSw84OOHDxYsgimPp7f13O/uz79eCCGEEEIIIYQQA+dgZoYD6zfjv4nxOJSdiTZhT5WTlbEJWoWqW5/16pJIIJPLlVZ7aKMCZEtomF5vKA3FwexMpcmwXl0SCf528TzOPPSIztZQ0daKX9JTcb6wAF1iMbxtbLAxOBRL/AI03igcbR/cuIbv7iQO2i6WybAnIw2NAgG+Xr5yFFZmGJ6OmoLnz5xQG/NkFPPKImMuF7M8vEa4KtU4bObv98ePHUJpaytMuFzM9/bBw+GRCFTTDm+s6hCJsDcjXWPcm1cuQiSVoqazAywAsz29sD0iSmuz3QyZu5UVmrq7NMa5WVqp3HdBRUvQgbLq6+BjY4Oi5ma1cQu9fYdUpXkvE0ml6BKLYW5kpJPv4ur2dqzbvwfVHYqtLIVSCQ5mZyK1phr71m/C67Pnoqi5GVw2G0H2Doy/375X8h0z0C9pKXh20lSVD2BYGRujUnOnTTibW2JLKLMWjtoglcnQ1NUFLpvdV824LCAQi/38caWkGIXNTTDhcrHA2xeuBvpg0b2MEmKEEEIIIYQQQoiBsuPz8ffZ8/Dn6TNR0dYGLpuN7Pp6PHf6uMZj3S2tVCa+fG1sYWdqisYuzTc7lZnq6obHIycN61h92JWeqjEmr6kRCZUVOpmxcr6wAH84c7LvqXmgpw1UXEU5fklPxQ8xq2FhoDd1K9va8ENyktqYc0UFuFVRjmg3dz2tyrAsCwhEeVsrPrwZq7QF1gtTp2Gtjio2hmOyiyvjmYRpdyt1WoU9LRT3Z2XgnwsWGdS/Rxuul5WiQ6y5AqqsrbXvv+UArpaW4GppCV6ZNhPP3ePz1jYEhyK1tkZtjCmXixWBQSr3d2mYQdarWyrFC1On44UzJ1XGGHO4eOYe/5kzkVRdie/vJOFicSEkMhmsjE2wJmgCHouMgouF9pIr/7p5bVAyrL+C5ib8J+EW/j57HiY6D62FZXNXF25Vam7j2tzdjbiKcsz18la6f3nAeGQ11Gs8z/IA3bdGBHraDP+QnIi9mel97SRDHMfhd+GRWBsUDA6bjQU+vlgAX72sR5M2oRDdEjFsTEwN/kEdbaKEGCGEEEIIIYQQYuBMuDz42fa0UXO1sIQ9n48GgUDtMRuCQ1XuM+ZysSE4FF8lJqg9R6CdPSrb2/raKzrwzbApJFTtE9ujTSaXI5Nh+630ulqtJ8SKmpvw/JkTEEmlSvcnVlXirxfP4YulMVq9rrbsy0qHTK5q0slv9mSk3bcJMQB4etIULPD2xa70FCRWVUIOINLZBQ+FhmO8vcNoL0/BSGYSSmQyvHrhLALs7BHqOE4HqxsdAgbJMHU+iruOKGeXe/o9sHp8EH5JS0G2moTDc5Oj1VZseVlbM7qWl7U1YgLGo7mrC+/HXoVIpvj5aWlsjP8sXj6s12BLd9fd5G4mqtvbYWlsjGUBgfhdWCQ8Ga7PUOzPysBfL55T+IxuFXbjfyl3cCw3B7vWrFc5z20oGgUCnMnXPEPuUHYm/jx9Jky4Q2v31zmE95+6lpwbgkPw/Z1EtZWMc7289fKZ3NQlwJaD+5DX1KiwPaOuFn86fwa3KyvwwcIHdb4OJs4V5uPH5DtIqKoAAFibmGBtUDCenDj5vpgTaph/vRJCCCGEEEIIIUQpHoeDP02fhb9cOKsyxtPKGg9paA/0+8nRSKisQFJ1ldL9wQ6O2L1mAzhsNoqbm8Bms+FnY2vwTxGzAHAYVsNoo3XkQDtTk1Umw3qdKcxHRVur2lZjo6WouYlRXHGL+tZm9wN/Ozu8NXeBzs5f1tqCXempuFpaArFUivH2DtgcEoYZ7h5gDfG1+9bc+chuqEfJMH5vUrkcO1Lu4KNFS4Z8rKHSRiJkZ2ryPZ0QM+Hy8NOqdXjx7EncKC9T2GfK5eLZydF4VkPF1sbgUHydmKC0krK/zcFhAICHwyOxzD8Q+7LSkVFXBw6bhWluHlgZGAT+MGYslba04KHD+1HZ3ta3rV4gwY6UO9iXmY6vlq7ArDHS/rKouQmvDUiG9dfYJcCzp47h/EPbh/z5MFBOY/2gpKQybUIhSlpahpxwsufzYcrlMqogdLNS/T1pa8rHjyvX4LFjh5RWvEc5u+DTB5cOaW3D9daVS4OSYf3ty8rAFFc3nc17Y+qTWzfwecIthW0t3d34ITkJp/LzsHfdRoP820SbKCFGCCGEEEIIIYSMMesnhEAqk+GDG9f6Zov1inJ2wWeLl8HKxETtOUx5PTc7v05KwK8Z6agX9LT3sTM1xboJIXhucjTMjYwAAMFjqDKExWJhqqvboBu4ykzTwc1sJlU4MrkcZwry8fhEw2s7acrwprOpgVYIMiUQi3EiLwdFzU0wvjvLJWyc02gvq8+RnGz85cIZiPsldotbmnG6IA/L/QPx8YNLwR3C3CAHvhkOrt+MrxLjcSA7Ey3dPXMImbZOPVeYD8AwE2IiqRRtQiHMeDzGr98oZ1f429ohX80NbE2ulhYP+9ixwo7Px8+r1yO7oR5nC/IhEIvgZmmFVeODYGms/jsGADysrPG7iInYkXJHZcwiHz9M7fdZbMfn45lJI2+NKJfL8fTJowrJsP4EYjGeOXUMlx9+bExUxfySngqphurdouZmxJaVjnjGHRvME2rDebDEhMvDisAg7M1UP8cvyN4B4Ro+l8PGOeHiw4/iYHYWTuXnokMkgquFJTYEh2CBt69eZp3WdXbgTKHmirqf0lJGNSF2s7xsUDKsv+qOdrx87jT2rdukx1Xp39j+64UQQgghhBBCCF5//XXY2dnhpZdeUruNjC3dEjHaRSJYGhkrbU+4KSQMKwODcDI/FwXNTTDhcDHf26fvpn5FWyv2Z2WgvLUVZkZGWOznjxnungrnMOXx8FL0DPx+cjRKW1sglwMeVlYG2w6RqYfDIjUmxCa7uOqkjZK69k79tYuEmoNGwUJvXxzIytQY94CPnx5Woxu701PxrxuxCr+DzxNuIcrZBf9ZvBzOFhajuDoguboKfzp/WuXN7xP5uXCxtMSrM2YP6bw2pqZ4bdZcvDJtJqo72sFls3EiLwf/d/O6xmMFYvGQrqUPpS0t+PbObRzLzUanWAwOi4V5Xj54fOIkRq1Q/zJjNp48cYRRi1BlxBoqQbWhqUuA0pYWGHE4CLR3GFISVJuC7B0QNMzPy9dnzQWfy8OPKUno7lcRxGOzsTYoGG/Oma+tZSq4Xl6K3MYGtTECsRi/Zqbh+SnTdLIGbbrGMAF7tbRYY0JMKJHgZH4uDmT7wOjBAAAgAElEQVRlorrjbhtJ/0BsCA6BXA4kVJWDBWis7HM0M4OPjS2jdQ30dNQUnC3M70vOD8RhsfDH6TMZncvS2ATbIyZie8TEYa1lpG5VlDOb0VhbgzahUG2bUV36KTVZY0xiVSWy6uswwcFRDysaHWP7L1xCCCGEEEIIIVqVl5eHvLw8zJ8/H3w+f7SXc19KqanGt0m3caG4EBKZDKZcLmICxuPJqMmDbjyZ8nhYNyFEYZtMLsc71y7j57QUhRu9u9JTMcHeAd/ErIKrhaXCMTwOp29G2b3gAV8/PBQajl/SU5XuH2dmjg8fWKyTa7tZWjGqOnE30JZEC7x94WlljdLWFpUxFkbGWDdhdNs+DdfejDS8fvmC0n1J1VXYcmgfjmzcqrHCUpe+T07SWAmyOy0Vz0+OhtndKs6hMOZy4WVtAwDwvPt/NfGwMqxZS+l1tXj48AG0Cn+7mS6Vy3GhuBCXSorwwYJFgz4bB5rv7YPPlyzHm1cuapzJqIy/illNrd3d2JuZjoPZmajr7IS1iQliAsZja2g4xpmbMzp3WWsLPoq7gbMF+X2t65zMzPFQWASejJo8aomx4WDfTWw8MXESTubn9v1MlvoHwNGM2c9jOC4UFTKKO19UOCYSYhKp5oQLoDlRWy/oxCNHDg6aDZdeV4uvEuPBZrHRrGYmV3+bQ8KG/Vr0tLbGrtXr8fvTJwa14LU1McXb8xZinpfPsM6tb6Utqr8vB5IySJzpyo3yUkZx18tKKSFGCCGEEEIIIWRsefPNN4c1QyIvLw+nTp3CtGnTKCE2Ck7k5eDlc6cVnjTukkiwLysDpwry8L+VaxDl7Kr2HP+8fhU7VTwFnNVQj22HD+DYpof62iHeiyrb27DI1x/mxka4UlyMnLtVAuY8I6waH4RnJk3VWRXQ+gkheP/6VbUxFkbGWOofqJPrjxSHzcZ3Mauw7fAB1HZ2DNpvzjPCN8tXwtrEdBRWNzIiqRT/jruhNqa0tQW7M1K10rJtOMRSKc4XFWiM6xCLcKWkGMsCRvY6WuDtC3s+X2NCaGNw6Iiuo01SmQzPnjymkAzrTyaX47VL5zHF1U1jIm+JXwAWevviXGEBshvqwWGzMMPdE6+cO62y1V6vLSFhg7YVtzRj2+H9qGpv79vWKuzGF7dv4ee0FPywYjUmOruoPW9RcxM2Hvh1UCvLms4O/DvuOtLqavDl0hU6mYGoS1YmJtiiYbalNjGtauw2wOpHZQLt7VHW1sogTn0l37Mnjw1KhvVqFTKvXJ7m5o6no6YwjlcmyMERF7Ztx/WyUtwoL4VULkewgyOW+AWMiUr11u5u/OHMCcSWMUs0OZtbjOrDFmKGyTgm1W5jmeG/sgghhBBCCCGEDBlvGMPnyeiq6WjHH8+dUXkjokMkwrMnjyN2+xMw4nCUxtQLOjW2xClpacah7Ew8HB454jUbmuyGevzfjVjElpX0VcfZmfKxPWIiNoeEwc3SEiZc3b43NgaHYk9G2qAn3vt7fko0+Ab8HvWztcOJzduwOyMVB7MyUdPZARsTU8QEBGJbWCTcrQyzuk2TC0WFaOzSXAm0NyN91BJiXRIx45uRbVpou2nE4eBP02fhLxfOqozxtrbBltDByZ/RcqG4UGOySiKTYVd6Kv46c47G8/E4HCwLCFRILr41dz6ePnFUZaVepJMz1iupzn3i+GGFZFh/rcJuPHH8MK787nFYqGmZ9trF82rnup0rLMCBrAxsYJikLGlpRkJlBeRyOcLGOSFIC5UfXWIxqjvaYcLlwmVAxbGh8GZY/ejFMG4oMupqcbmkCEKJFH62dlji5z/iBM/mkHCc11D1Zs4zwsrAIJX7k6orkVRdNaJ1WJuY4LHIKDweOUkrSSsWi4VZnl6YNcK5Z/omkcnw6LFDSK6pZnzMltCwUU1kB9k7ILW2RnOcg/bbSRsSSogRQgghhBBCyBjR1NSEQ4cOISsrCwDg5+eH9evXK41VNkOssLAQp0+fRkVFBQQCAczMzODm5oalS5fC29sbP/30E27d6hm2/cYbb/Qdt3TpUixfvhwtLS24ePEicnNz0djYCLFYDHt7e0RHR2PhwoVg92ubExcXh59//hkvvPACysrKEBsbi5aWFtja2mLx4sWIjo4etObc3FxcuHABJSUlEAqFsLKyQkBAAFavXg3zfm2mEhMTcfXqVVRUVEAmk8HV1RULFy7ExImjMztCW/ZkpPW1xlKlXtCJ0wV5Km94Hc3JZvQE8IF7MCGW/v/s3WdAVAfWN/D/FJih9957B0ERxYYFu8beE9OTJ2VTNnlTnt1kE5PNk2x2N1mz2ZRNVRMbKvaOJaiISu+9S+9l+vsBIcC0C8wMoOf3Kblz5t6DSPGee86pq8XWuP3oEA3e4dXY3YUfUu+gqq0NXy5bqfU8THg87F69Hi+eOoY7Q26U8blcvDh1Gp6MmKL1PEbLytAQL06dPiFGiTFV0cZsrFVFWytkMtmIumxHy1ifB1MeD20MOjUcjTXT5bg+MBgSqRQfJ16V67qa6uiMzxYvhSlv7LoahmLajXG1vAxvjfAa8z288M2KVfjw6iUUN/9e3NZnc7Dc1w9/iZkvVwxIKCkeFKtIc08PDuVmYXuY4p9XeY0NuFldqTa/3empagti1e1t+N+L53ClrHTQLqjJDo7YMXfBiHYoVre34d/JSf172wAg0NoGj4VPxtqA8TVGdX1gMD5Puqb2Z+ImBZ1+I1XT3o6XzpzAreqqQcd3XOHj7Vkxo/ozmuPmjsVePjhdVKA05u1Zc1R2f58oyB/x9ftMcXDE85Hyv8M9aM4VFw6rGBZobaP0615XtoSEqS2IOZuaYo6bh44yGhtUECOEEEIIIYSQCaCrqwv//Oc/0dzcjFmzZsHe3h4FBQX47LPPIGIw7qe2thY7d+6EqakpYmJiYGpqira2NhQVFaGyshIeHh6YOXMmuru7kZaWhnXr1sHIyAgA4OTUO6KvqqoKqampCAsLg42NDSQSCbKzs3HkyBE0NDRgy5YtcteNj4+HSCTCzJkzweVycfXqVfz888+wsbGBl5dXf9zVq1exd+9emJmZYdasWbC0tERzczMyMjLQ3NzcXxA7evQoTp8+jcDAQKxYsQIsFgupqan473//i40bN2LOHPXdAOPVb0xv8paVKi2I3e2QH3GnOE5xB8NE9sb5M3LFsIHOFhciPjcHqwMCtZ6Lg4kJDm7YgtS7NThbVIgesQhu5uZY5Rc4puOSHjSNXV04mJOJnIZ6cFlsMK1vGerpjUkxDOjdt7TGPxA/qun0dDA2wUxXN41dd1NwKFb5B+BkQT6KmpvA53Ix38NrXO6REavZkTTcOGXmuntirrsnblRW9P+ZzHZzh42hkcJ4VYWKgc4UFiq9MX57SCFFmcz6OgjEYqUdOnWdHdhwcK/CbrXbNdXYFLcP+9dtgq+SPWiKlLQ0Y9PBfajv6hx0PLuhHq+fO42sulq8M2ce4/Npm42REZ6dMhU7b95QGjPL1Q0x7pq5+d/S040th/Yr3L/Y3NOD18+dBpfNVtnBpQqLxcLni5fhb9eu4tfM9P6CJAA4mZjij9NnYJW/6p9v7cMYiahMl1g86nPcDw5kZzKK47BYeMgvAO/MmTvmo6pX+QXgaF4OEivKFb6ux2ZjR8yCCTeOdbh0UhATCoXYsWMHGhsbMWfOHGzcuHHQ67W1tTh8+DAKCgogkUjg4uKC5cuXw89vfM7TJoQQQgghhBBdO3fuHBobG/Hwww9j+vTejo05c+bgwIEDSEhIUPv+7OxsCIVCPP7443B3d1cY4+npCScnJ6SlpSEsLAxWVlaDXvfx8cH7778/6EbxvHnz8OOPPyIxMRHLli2D2ZBRamKxGG+88Qa4927aRURE4J133sHly5f7C2LNzc04cOAA7Ozs8Nprrw3aXbZixQpI7z3dXV5ejtOnT2PRokV46KGH+mPmzp2Lr776CvHx8YiKigJ/SMGhq6sL3UPGT7HZbFhYaH5M0mgw3e2gKs6Mr3wM10Dm46jbQxNuVVchV8lOlIH2ZKSOqCB2t6Mdx/Pz0NjdBRtDI6zw9YeNkeKb4gNNsnfAJHuHYV+PjN4PqXfwceIVCEdQFFns7auFjJh7InwKjubloqlH+di8V6fPAGdAV64m8Ll6WDPOunwUYTrOayQdUIpMc3bBNGcXtXFMd1Z1qijca8rOmzeUjm4EgDaBAP+XeAXfr1zD+Jyvnz0lVwwb6Me0FMxwdcN8Dy+lMbr2yrQZ0Odw8NWtm4MKSH1Fih1zNXfzf1d6qsJi2ECfJF7Bch+/EX/t6nE4eHtWDF6cOh2Xy0rQIRTCycQUM1zdGH0cjhrYnelqOjFH5mqaqq+vgRZ6euPThUu0nA0z3WIRXpk2A1YGhjhfUjToe1agtQ3emjUHM1w096DFeKWTgtixY8fQoeQptfr6enz66adgs9mIjY2FgYEBEhMTsXPnTrzwwgvw9/fXRYqEEEIIIYQQMq6lpaXB1NQUUVGD99osXLiQUUHMwMCg/zxOTk4j2jGmP+DJVrFYDIFAAJlMhoCAANy8eRNlZWUIDR08emj27Nn9xTAAMDc3h62tLerq6vqP3blzB2KxGEuXLh1UDOvTN4oxOTkZLBYL06ZNk/s3ZmhoKNLT01FcXIzAwMEFj4sXL+LkyZODjllaWuKDDz4Y5p+AdgVY2yCzrpZRnDLLfPzw2Y1rULz15nfLfe+vf2unMhxblFZ7d1ij8ARiMd69dAGHcrMH7XX6OPEKtoSE4X9nxYCr4aIEGb24nCzsuKL++6IiXDYbj47xOFEnU1PsWrMez504KneD3YDLxRszZo+78XS6tNo/CJ8kXkW3mk6VrSFhOsqol5uZ+ajjIhwcGZ0jyMZWaXdYt0iE+Nwctee4UlaKqrY2OJmq3wGWWVcrNwJWkd3pqeOqIAYAz0dOw/awCJwsyENNRztMeXws8faBvYZGjvbZm5mhNqamowOXykpG/WdkwuON6Of42oAg/Ds5qX/H5khs1OCIyYnMhGG3F5OHZ7Stsq0Vn924hhMF+RBIer9vOpmYYrGXD6JdXOFlaYUwO/sxzlJ3tF4QKy8vR0JCAlavXo24uDi51+Pj49HV1YU333wTLi69T1tERUXhgw8+wN69e/Huu++OWZs6IYQQQgghhIwXDQ0NcHNzG7SnCwDMzMz6i12qTJ48GTdv3sSZM2dw8eJFeHh4IDAwEJMnT5brBFNGIpHg7NmzSEpKQn19PWRDbqh0dXXJvcfaWn4ck5GREZqamvr/v6841vdvQmXu3u0tZrz33ntKY9oVPLE7b968/q66PkP/HMeDLSFhakfw6LM5WB8YrPR1TwtLxHp542xRodIYMx4fm4JV756ZaJg+4c9isYZ1j+HlMydxRsEYNJFUip/SUtApEuKTBYsZn49on1Qmw+dJ10b0Xi6bjU8WLEKQrZ2Gsxq+AGsbXHjkcSSUFuNKWSlEEgn8rK2x2j9wXO3zGgumPB7enhWDPyecVxqzxj8QUQy6ujRpQ1Awvr59U+0DCRtVfP/1t7bBFEcnuR1UQ20LnaT0tcq2NpXjY/tIZTIUNDUyKojdqKxQGwMA1xnG6Zqxvr7anWujIZJIUMNwFHFZC7NdhiOV19iA88VF6BaJ4GlhgaU+vuBzex+CcjUzx8agEPyamT6icy/z8X2gCieqLPPxw+2aarVxS33GdgJeSUszNh7ci4Yhv6NXtbfhUG42hFIJVqsZtXm/0WpBTCqVYs+ePQgMDMSkSZPkCmICgQDp6enw9fUd9A8fPp+P6OhoHD9+HGVlZUrHeRBCCCGEEEIIYUZPTw9/+MMfUFpaiuzsbBQWFuL48eM4ceIEHnvsMUyapPzmWp+4uDhcunQJkydPxuLFi2FiYgIOh4Py8nIcOXJErkAGKC88KYpVp6+z5/nnn1d6XgcH+fF0hoaGCjvPxpswO3tsCQnDLxlpSmNei54JKzUfy6exS/B0zxHcqJK/MWnO5+O/K1bD1sh41PmOJ5FOzsziHJ0Yn/N2TZXCYthAB7Oz8ET4FPgNYw8P0a7rFeWobGtTG6fHZiPUzh6lLc3g3duX9XDoJHhbMntAQBfYLBbme3iNu46b8WBrSBiM9PTxzxuJqGhr7T9uxuPjkbBJeCkqWme5SGUysFksuJtbYGtIGHar+B4e4+6hdiTZR/NisfHgPqUjMxd4eGGdig5BPQ7zBz6YxkpkzEb6jqbzaCLjstnQZ3MglKof0Wo4gg59Juq7OvHqmZNy+6F2XLmE16NnYsu9jsn3YuaDzWLh18x0uc/XNGcXOBmbID4/d1BXNIfFwrrAYLwXM18ruU9Ea+512zV2yz8M1ifc3gFTGf5+oi1vnT8rVwwb6Hh+Hua5ew7aP9ctEiHlbg3EUil8LK3goIFRm+OJVgtiFy5cQG1tLZ5++mmFr1dVVUEsFsPDQ355Yd8xRQWxiTL/nRBCCCGEEEI0xdraGvX19ZBKpYOKQa2trXL/PlLF3d29/99YTU1N+Oijj3Ds2LH+gpiq7pmkpCR4e3vjiSeeGHR84PjDkbCz6+3GqKys7P9vRWxtbZGdnQ0LCwuFha/7wY6Y+XAwNsb3KbfR3NPTf9zRxAQvRUWr7A7rY6yvj91r1iOhpBj7sjJQ0dYKY319LPLywfrAYJjx77/ukhBbO0yyc0BqreqRXltD1Bd+++zLUj/+qi/undlzGZ+XaM+B7Ex8eu0qo1iRVIovlqyAnbH64vD1inIkVpRDLJMiyMYWi7x8oM/hjDZdMkqr/AOw0s8fNyorUN3eBlMeD7Pd3Pu7YbSpubsbu9JTsT87A9Xt7TDU08NiLx9sDwsHn8vFz+mpg/bXsQDYGRuDBeDL5CRsCAqBtZKHG7wsrXBww2b843oizhQV9O+NtDMyxtaQMDw7ZarKHVRuZuZwN7dAaUuzyo/BlMdDhD2zEY0htsy6gkLGQXflWGCxWIj18sKJgnyVcXpstlYK3B1CIR4+dAD5TY1yr7UKevCnhPNgsVjYHBwKLpuNHXMX4NnJUxGXk4WajnaY3RvBGHzv8/fH6Jk4lp+Lxq4uWBoYYoWv/31XFBktUx4P3z+0Bo/Hx6FRwe/hvlbW+M+ylWOQ2e/yGhtws7pSbdyu9FTM8/BESXMzfs1Mx6nCArQLBQB6i6Fz3T3x5szZ8LSw1HbKOqG1glhDQwNOnDiBpUuXwsrKCo2NCr4gW3uf4DA3l5+d23esRUEb6USZ/04IIYQQQgghmhIaGto/rnDg+L+zZ88yen9HRweMh9z4tbCwgLGxMTo7O/uP8Xg8AEBnZ6fcKEVFXVkCgQAXL15k/HEoEh4ejiNHjuDkyZMIDAyUGwHZ1xk2depUXLp0CUePHsVTTz0ll09bWxtMGYx+Gs9YLBaej5yGJ8On4LfyMrQIemBrZIRoZ1eVN0CHYrNYmO/phfmeD05nyd9iF2FT3D6FN6YAYF1gEJb6+DI+H9OxVuVaHn9FmPnsxjX86+Z1xvEcFgsm977fKZPf2IA/nD6B/MaGQcetDQ3xfswCLPb2GVGuRHPYLBaiXVx1es3KtlZsObR/UCdil0iEQ7nZOJqfi09jFyPxsadxLD8XV8tLcaWsFBKZDHc7OnC3owMJpSX4V9J1vDd3PjYqGePnbm6Bfy1ZjsauLpS0NEOfw0GgjS2jnYUsFguPhE7C+2r26K0PDIYBw26laBdXeFlYoqi5SWWcrve2jSePTZqMU4UFKrvkVvj6a2Wn1P6sDIXFsIE+vXYVa/wD+3fPOZma4g9R0xXG2hub4KmISI3nOdHVtLejsbsLVgaGcDAxQYitHc49/BgOZmfhWH4uWnt64GBignWBwVju46d0z5+uJFepL4YBQMrdGkz55j8QK+gElchkOF9ShFs1Vdi3dhN8GI5ZH8+09ln59ddfYW1tjfnzlbdSCoW982y5Cv5y9C147osZaKLMfyeEEEIIIYQQTVm4cCFu3bqFPXv2oLy8HA4ODsjPz0dJSYlcoUuRU6dOIScnB8HBwbC2toZMJkNGRgZqa2sRGxvbH9c3rePIkSOIjIyEnp4eHB0d4ejoiPDwcPz222/473//C39/f7S3t+PatWswGuXNHQsLC6xbtw779u3Dhx9+iKioKFhaWqKlpQXp6enYtm0bXFxc4O7ujmXLluHEiRP461//ioiICJiZmaG1tRXl5eXIysrCzp07R5XLeMHjch+oYpYmeFlaIW7DFvwr6fqgxfEe5hbYHhaOh1Xs3BlIJpMhPi8XeUOKIMoY6mu/G4WoltfYMKxiGADM9/RSObqsqq0NWw/tV1hgbejqwounjuHbFasR4y4/9Yhoj0AsxomCPFwuK4VYKoWflTU2BoUw6vTTlOdOHlM6llMsleL1c6dxaut2eFlY4oOrlxQWSIRSCd6+cBbWhoYqO4asDA3VjskdqrWnB2wWC96WlihsUlzAmubkgtemzxzWef86Pxbbj8ShRyxW+HqMuwdW+gUM65z3kwgHR+yYuwB/Tjiv8HM+1dEZ789doJVr72fQ0dzc04MzRQUP9OdopH4rL8OXyUmDRlFHOTnjucgozHJ1x5MRU/BkxJQxzFCx4QwwVVQMG6ilpwfvXDqPX9duHF1S44BWCmJJSUnIzc3FK6+8Ao6KFnJ9fX0AgFjBN1KRSDQoZqCJMv+dEEIIIWSsCcTiMX8yjUw89PdmfDI0NMSrr76KuLg4JCUlAQC8vb3x8ssv4/PPP1f7/rCwMLS2tuLOnTtob2+Hnp4ebG1tsXXrVkRH/75rxcvLC6tWrcLVq1exZ88eSKVSLF26FI6Ojli3bh34fD7u3LmD9PR0WFhYYObMmXBzc8O//vWvUX18s2fPhrW1Nc6fP4+EhASIxWKYmZnB399/0Hj8ZcuWwdXVFZcuXcLFixchFAphbGwMR0dHrF+/flQ5kInP1cwcny5cgnfmzEVlWxv4XC48zC1UjgIdSCqT4ZUzJ3EsP5fxNRd5UpfQWNuTnjqseC6bjafU3Lz86vZNpd2GQO9T859cu0oFMR26XVOF/zlxdNA+nFOF+fgi+QZeipqO5yOnjfoaYqkUZ4sKcaaoAJ1CIVzNzLAxOLR/T2BydSUy62pVnkMklWJPRhoy6mpVdgvJAHxx84bGRujJZDJ8lnQN3965pbRo5WVhia0hYdgcHDrs3/UiHZ3xy5oN+DjxCpIGdJ6Y8fjYFByCV6bNYNTBdj/bHByKMDt77EpPxcWSYggkYvhYWmFzcCiW+/prbdRqKcNO5bJW6mgeriO5OXjt3Cm5r+WkqkokV1fhbwsWY3VAoJJ3j61we82OF0+qqkRBY+OE7xLT+L9yRSIR4uLiEBQUBFNT0/5Z8n2jD7u7u1FXVwdjY2OYmZkNem2gvmOKxikSQgghhBBmeFwuPP/197FOg0wwxX/441inQJSwtLTEU089JXdc0fj4ocd8fX3h68tsXNzChQuxcOFCueP6+vpYs2YN1qxZI/fal19+Oej/p0+fLjfZo88rr7yi8HhgYCACA9XfVAgJCUFIiOIxU4QAgCmPj0Ab5bvSRBIJ9BTcmPw+5fawimEupmZY6OU9ohxHo283Ee2x6pV6V/XuuIH02Rx8HLsIkx2clMYIxGLE5+aoPVduQz1S79ZgkoZvOhJ5JS3NeCz+EDoUTJISS6X4+/VEmOjz8EhY+IivUdzchCeOHpYrGvyYloK1AUH4aP5CXCwpZnSuM0UFqG5vVxuXVnsXxc1NGtnN88m1q/j6drLS16c6OmHPmg3DGr871CR7B/y6diOKmhpR0tIMHoeLSCcnnextmygCbWzx0Xz536G0yVBfD8Ieido4A/o8DUtjVxfeunBWaWFbKpPhrYtnMcvNXelOwLEUzHC/6nCk192lgthQIpEIHR0dyMzMRGZmptzrN2/exM2bN7F69WrMmjULXC4XJSUlcnF9x1xddTsHmBBCCCGEEEIIIeNfj1iEitY2cDlsuJmZo7GrCx0iIWwNjWCkYNpMY1cXfk5PwYGsTNzt7IA+h4PF3j54bNJkhNnZQyqT4ef0FMbXtzMyxncrVyssrGmDWCpFXHYmdmekIau+9+HjSXYO2BYahtX+gYy74MYTqUyGq2WlKG1thgFXD3M9PGFjOIIRrAw/dj8ra3y/cg0cTExUxjV29/5dYqKirZUKYjrw3zu3FBbDBvryVhI2B4eO6GuytacHDx8+gJqODoWvx+VkwYDLBZfhuXtEiju0FKnv7FRbEKvr7MCvmem4UFyELrEIHuYW2BQUirkenmCzWKjt6MB3KbdVnuNmdRUSSouxwHP0RXwvSyt4WU7sm+L3k1hPbxzIlr8PPxALGJMHOCay/dkZ/eOXlRFKJNiXlYHnI6N0lNXw/HV+LDbH7UeroEcj52Nj4v2uMZTGC2I8Hg9PPvmk3PGOjg7s3bsXgYGBiI6OhpOTE/h8PkJCQpCamorKyko4OzsDAHp6enDt2jXY2trC3d1d0ykSQgghhBBCCCFkgmrs6sK/k28gLicb7UIBgN5Oqb6uKT6Xi2U+fnguMgoe5r3jNitaW7Hl0H5Utf++90cokeBoXi5O5Ofh4wWLEGRrp3Qv0FBbQ8Lw6rQZsDAw0PBHp5hIIsGzJ44ioXRwd0pqbQ1Sz9XgUmkJPlu8DOwJUBSr6+xAQkkxbtVU41JpCRq7fx9/p8/mYIWfP96Lma9yv9dQUx2d1Y6xA4DtYeFqi2EAYKQnX1BVZjh5kpERS6U4mqe+Y6+usxNXyktHNIJwX1aG0mJYn71ZGXh12gxG53O3MEdTjfKRmwNZGqjuLLlcWoLnTx1D1731MgBQ3NyMCyXFmO3qjq+Wr8TBnCyIpap3AAHA3swMjRTEyPjyaFg4Dudmq/w7EOvpDVczmsSmSn1nJ47kZaOmvR2mPD6uV5Yzel9yVSUwTgti/tY2OLB+E/527SoulhRDcq/bzXWzKwEAACAASURBVIDLRbeS0arKsFksTHFU3l09UWi8IMbhcBARESF3vLGxEQBgY2Mz6PWHHnoIeXl52LlzJ+bNmwc+n4/ExES0tLTgueeem5BPOBFCCCGEEEIIIUTz6jo7sPHgPrmRZn3FMADoEYsRl5OF88VF2L16HYJs7fD8qWODimEDSWQyvHnhLD5btJRxHst8/HRWDAOAL5JvyBXDBjpekIdQO3s8qWYv1ljqEArxTsJ5HC/IU3rTViiVIC4nC+WtLdi1ej3jkZBbQ8PwY9odlfuaTHk8POQXwOh8Znw+pju74Hplhco4Ux4P0c402UjbOoVCdA4oBqlS39k5omscys1WGyOWSiGRSmGkp6c2n8cnTYZMdhspasZ5BtvYqhw/VtrSjOdOHlV64/pKeSn+nHCBaZMk7ZC6TwXY2OKTBYvw/86fUfj9NcTWDh8vWDQGmU0MUpkMH/12GT+npUDEoLA8lAzKf/aMB96WVvh6+SrUdnSguLkJ+lwO6js78dzJY8M6T4ybB1zurcCayMZ806GtrS3++Mc/wsPDA2fPnsWhQ4fA4/HwwgsvMJodTwghhBBCCCGEkAfDXy5dZHxDt1XQgz+cPoHkqkq13UNiqRRXy0oZFWA4LBY8LSwY5aAJQokEv2SkqY3blZ6qsiA0lgRiMR6Lj8ORvBxGXSzJ1VWMOoL6eJhb4O2Zc5S+rs/m4O8Ll8BgGN1cT4SrLy5uCQ4b1jnJyBjq6TEujprzR1aobmBYSGsV9OC16JkqY6JdXLHQywfPRUapHS72XOQ0la//nJaitosjfhhfK9TROLbyGxvwTsJ5LNj1Peb+9B1eOHkM1yqYdSGps8o/EMc2P4zNwaGwNTKCiT4PIbZ2+GheLPav2wQzvvK9muOdSCKBSKJ+R9pIfXAlAd+l3B5RMQzAhBmba2dsjOkurpjs4IRYT284MuiY7uNkYoodcxdoMTvd0XiHmDJWVlZyS5b7ODg44Nlnn9VVKoQQQgghhBBCCJlg7na041xx4bDeU9LSjF3pqYxir1VWYIm3r9qby/M9vWBrZDysPEYjo+4uGrvVj16raGtFYVMjfK2sdZDV8BzJzcbtmuphvefXzHSsCwxmHP94+GQ4m5ri69vJ/V05bBYLs1zd8eLUaYhwcBzW9ed5eOKNGbPwceJVha8v8vLBq9OZjc8bC10iEQqaGsFC7+40HldntwA1To/DYfS1acbjI2aEq1csDAzQ1KP+68zSwBDbwyKgz+HiH9cTB4381GOzsdIvAO/HzAeXzcZ8Dy98MC8W7166IFcI5rBY+PPsuVjs7aPyescL8tTmJJZKYcBlVuha6OWDTqEQLYIemPP4Cvct3i+SKitwuawUYqkEftY2WObjCz7DPydt+DktBe9fSRj04EJZawtOFuZjbUAQPl6waNRjb/2srPHhvFh8iNjRpjvmpDIZDuVkYXdGGtJr7wLo7XTbFjoJa/wDwWFrps+nqq0NPzP8PUERDouFTUGhGslFlzhsNj5esAhPHD08qMt+KGM9fazyD8CLU6eDzWLhy+QkJFdXQiqTIczOAZuCQ+BoYqrDzEdv4v40JIQQQgghhBBCyAPjVnVV/+6L4WDaUSaQiPHKtGj8Vl6qtABlyuPhtemqu0M0TShm/lS8qptaY+mXzPRhv6egqXHY71no5YOFXj6obGtFS08P7IyMYWNkNOzz9Hlm8lRMc3bFrrQUJFaUQyyVItDGBttCJmG+p9e43NnW0tONz25cw6HcbHQIhQAAcz4f6wKC8FJU9IQtgDwZPhmnCvIhlCr/O/7YpIgRFzwe8gvAP24kqoxhs1hY4ecPANgcHIq1AUE4X1yEyrZWGOvrI9bTW+7v2+bgUMx198DezAzcrqmCDECEvSPjm8htAgGj/K0MDBFobYPshnqlMQZcLtLu1iAi6RpEUim4bDZiPb3xzORIhNrZM7rORFDc3IQXTh1H7pA/iw+vXsI7s+dilb/uJ5JdKSvFe5cvKh2sF5eTBVczM7w4dbrWc6lqa4NQKoGjscm4LZRLpFK8cOo4zhQVDDqeUVeLN86fwYXiInyxdAW4GiiKHczJHFV39f/OimG0m1Ibmru7sS8rA/F5OWjp6YGNkRHWBgRijX8QTHg8te+f4eKGPWvW49NrvyGpqrL/uI2hIVb6BmBtYBBczcxhqKeH+LwcvHH+zKDfM66Wl+E/t5Lw5sw5eCJ8slY+Rm0Yn3/rCSGEEEIIIYQQQgYY6e0qc4ZjorwsrOBqZo69azfizQtn5TqaQu3s8dH8hfC2VL7vRxs8LSzBYbHUFgN5HC5cx+luj8IRFLf02cxG5CnibGoGZ1PN/FmE2dkjbOESjZxL21p6urHx4D65YmJLTw/+m3IbSVWV+GXNhglZFAuytcPOJcvx0pkT6FEwQnBzcChenKp6/KAqm4JD8VNayqCOr6FW+wfCaUARS5/DwVIfX7Xntjc2wcvTokeUl4OxCaOivoOJCb5evgrbDh9QGM/ncCGWSnG+pKj/mFgqxanCfFwoLsLOJcsR6+U9ohzHk9qODmw5tB91CkZgtvT04I9nT0Gfw2X0edOkb+8kq/0Z9nNaCp6OiNRKkUoqk2F3eip2paeiqLkJQO8DHmsCgvDclChYGxpq/Jqj8c2dZLli2EBniwsx4/tv8PTkSKwPDIYpg+KPMpVtiveLDqXP4QwqBvlb2+CFyCgs9fEb8bVHI7u+DtuPxA36nlXb2YHMulp8n3IHP69aBzdzc7XnmezghF/XbkRJSzPKW1pgqK+HcHvHQcXG6xXleO3sKYW/h0hkMnx49VJvEY3hns6xRgUxQgghhBBCCCGEjHvBtnZgYfiFsRW+/ki5W9PfLaPM1pDekUdellY4sH4zsuvrcKu6CjLIEG7vqPUOipyGelS3t8GUx0OEvWP/OCg7Y2PM9/DCWTXjIpf7+sGUNz53xPC4XLV7kIaKcffQUjb3r08Sr6rsrMuoq8UXyTfwxozZOsxKc2K9vHHl0aewPysDV8pKIZJK4GdljS0hYQi2tRvVua0NDfHjQ2vwxLHDCospsZ5e+EAL+3NyGurxS0YaMupqwWWxMM3ZFZtDQvsLb+sDg/Hp9d9UnsNYTx9LvX1hpK+Po5u24UB2Jg5kZ6K6vR1mfB4WeHphf1YmekSK9yMJpRK8fOYEEh9/esQ72MaL71JuKfz89ZEB+Nu1q1ji7QOWjjo8W3t6GO0Ja+zuxo3KCszR8Pc+mUyGV8+exNG83EHH2wQC/Jh6BxeLi7Bv3SbYGetuFLAqEqkUuxmMMKzv6sSHVy/hx9Q72LV6HdzNR7bb05jhAwKB1rb4cH4sGro6Yaynh7zGRlwpK8Vv5WWY6uSMJd6+Ouu46xaJ8PjRQ0oL+BVtrXjy2GGc2fYo405mD3MLeCj5M/z3rSS1D+X8OzmJCmKEEEIIIYQQQgghmuJhboEZLm74raKM8Xus7z2xLJRI8KeE80rjZri4YqHX4F0+gTa2CLSxHXG+TF0uLcHfbyQis662/5iDsQmejJiCxyZFAABej56JpKpKtAp6FJ7D2tAQL0eNrANFF2LcPHBEzf6ngVgAHgkL115C96E2gUDtji0AOJCViZejosftqDR1rA0N8VxkFJ6LjNL4uYNs7ZDwyBM4mp+L04UF6BIJ4Wpmjg1BwYh0dGZ0jrsd7ZBIZbA1MoIeR3WX48eJV/D17eRBx+7crcE3d5Lx4bxYrA8MxpaQUPyamY6qduVdLM9Omdrf9WfC4+Hx8Ml4fMD4sh9T76BLJFKZS7dYjAPZmXgqIlLdhzhuSaRSHMzJUhtX1tqC65UViHZx1UFWQIdQyPhBDlUPblS2tSK7vg4cFhsRDo6wMGBWvDyYkyVXDBuovK0Vf044j29WrGKYpXblNtSjpqODcXxVexueOHoYZ7c9OqK9You9fPBTWorauCU+PgiwtsHJgma8cPI42oW/jzPdm5WBD69ewj8XLcNMV7f+4xWtrdidkYqzRYXoEArhZmaGjcGhWOnrP6LvwVKZDGwWC/F5OSoLvwBQ1NyEhJJizPf0GvZ1Bqrr7MB1BgXdgqZGZNTVImSUDyfowsT86UcIIYQQQgghhJAHzl9i5mHDgb1o6lG842sgLpuND+fGQp/DwZaQMOhxOPj79d8G3UTSZ3Owyj8Af4mZp5FdJMN1Ij8PL505Ibe/pKajHTuuJKC8tQXvzpkHL0sr7F23EW9fOIuUuzWDYqOcnPHX+QvhZDp+l9pvnxSB+LwcRjeFWQDenTPvvtpppAvZ9XWMuvCaerpR3NKMAGsbHWQ18Rjo6WFjUAg2BoUwfo9UJsOejDTsSktB4b1xdNaGhtgQGIKnJ0cqHOf2c1qKXDGsj1gqxVsXzsLJxBTRLq7Ys2Y9njkej7zGhkFx+mwOnpkSqbY4mMjwIYLE8vIJXRBrFwrQ0qP4oYGhKtpatZzN7ywNDMDnchWO+hxK0ffxkpZm7LiSgCtlpf0/K3gcLpb7+uFPs2JgpmYs8C4GxZ6LpcWoamsbFz9HRrILs6SlGRdLikc09jPK2QWhdvZIr72rNMaMx8e6gGBcLSvFS6ePK+yWauzuxlPHjmDfuo0ItbPHhZIivHjq+KDPe2N3F+7crcGu9FT8vGoto47MmvZ2/JR2B3E52Wjs7oIpjwcjPWa7Ek8W5o+6INbY3c24oNvYpXzk7HhCBTFCCCGEEEIIIYRMCJ4WljiwYTM+/u0KLpQU9d+UGjpKMcTWDq9Hzxr0pPb6wGCs9g9EQkkxKtpaYaSvj/keXmO2O6VbJMLbF8/JFcMG+iktBSt8/RHh4Ag/K2vEbdiCrLpapNytAYvFwhRHJ/hZWesw65EJs7PHO3Pm4v3LCUpvrHFYLCzw9MZjkyIw1YlZNw4ZITWjr4ZLJJHgclkJajs7YcHnI8bdE4YMb9hOdFKZDC+dPo4TBfmDjjd0deHLW0k4X1yIX9duHNTNI5FKlRbDBp73m9vJiHZxhauZOU5t3Y7EijKcKypEt1gMD3MLrA0Mgo2hkdocxVJmn2+xTPFIxYnCgKsHNoul8ntqH2M93e3RM9DTw3JfPxzMVt295mtphUn2DoOOlbQ0Y/3+X+UeAhFIxIjLyUJmXS32rdukdIdWh1CIzPo6tTlKZTIkVVVgjWmQ2lhtczM3hz6bA6F0eIWx00UFI96D99WylXj48MH+/WoDmfH4+G7lalgYGOCfN66pHB0okIjx7+QbeHtmjFwxbKDMulq8cuYUfnhojcq8supqsf1I3KDPf5tAgDaBQMW7ftepZlQ0E1YGBozHVY+3XXTKUEGMEEIIIYQQQgh5wBQ3NyEuJwu1HR0w4/Ox0tcfYUNuxI1XHuYW+Gr5Q6hpb0d+YwO4HDaCrG2RWV+HLpEQLqZmCFAy6pDLZo/4hpmmHc3LGTRySZk9GWmIcHDs//8gWzsEjXIkUWNXF36rKEOPWAxvS0tMdnAa1fmY2B4WgQBrW/yQegcXSooglkrhYGyCzcGh2BAYDGsjI8a7Toi8AGsbRl0o5nw+PC0sNXbdn9NS8EXyDTQM6Aww0efh0UnheCkq+r7/nP6SkSZXDBsov6kRO64k4B+LlvYfu11TjZqOdrXnvlpeiqbuLlga9N5knuHihhkubmreJS/IxhaXy0rUxgVO8K5BHpeLue4euFBSrDLOUE8Ps93cdZPUPc9OnoqzRYVKCxksAH+cPlPu+I4rCSo7ovMaG/DN7WS8Fi3/XgCQDqPIqW5HlK5YGhhiobc3jufnDet9oyn+2BubIH7TNhzJzcaB7EzUdLTDjMfHcl8/bAoKhY2REQoaG5FaW6P2XBdLimFjaKz2e/HlshIUNjXC29JK4esiiQRPHz/CqCNeGU10/NkaGSPaxRWJasYm+lpajXqXo65QQYwQQgghhBBCCHlACMRivHXhrNz4uh9S72C6syv+vXQ5oxE+44GDiQkcTEz6/39gN9hEkD5gZ5jKOBVjnIarQyjEe5cv4lhe7qCn732trPHn2TEjutk+HFOdnDHVyRkymQwiqRT6anYsEebM+Hys8PXHgexMlXHrA4M1tj/s38k38PfriXLH24UC7Lx5A7UdHfi/BYs0cq3xand6qtqYkwX5+N9ZMbC61z3RzPAGtwxAq0DQXxAbqc3Bofjq9k2VnVMsAFtCwkZ1nfHgifApuFhSrLKbZUNQCEyUdFRpi6eFJX5etQ4vnDqGyrbB++BM9Hl4L2a+3MMalW2tuFJWqvbc+7Iy8FLUdIU760x5fLiZmaOstUXtecbT7qc/TpuJa+XlwyoGOZuajeqahnp62BISpvTr4C6DIjbQW1g8X1LIKPZMUYHSgtjpooJh7VJTZDijX1V5PnIablRWqCyavjB1mkaupQu6H5BNCCGEEEIIIYSQMfHGhTM4omSX0/XKcjwefxgS6cQemzUcMpkMlW2tKGlphoDBfhdNYjHsnNFUh41ALMb2IwcRl5MlN4oqv7EBj8cfwlUGN181gcViUTFMC96YMQse5hZKXw+yscWLU6dr5Fq1HR34POm6ypj92ZlIu6u+o2Kiqu/sRH5To9o4oVSC5Oqq/v+3MzJmdH49NhtWoyyGAb1dIq9Mi1YZ88LUaXA1M0dKTTUulZYgt6F+1NcdC9OcXfCXmPlKv2/O9/DEmzNm6zirXqF29ri0/Ul8s/whbA8Lx7aQMHw0LxY3nngGq/wD5OKz6+sYjX9s7O7CXRWFky0hoWrPMdnBEf7jqEPQzdwc+9ZtxOQB3dHqaKr4o8xwiqhM9sUBvQ+pKHNRTaejOmv8A+GroZHK05xd8I9FS8HjyD9MwWGx8OfZc7Hc118j19IF6hAjhBBCCCGEEEIeAAWNjTial6syJrW2BhdKirDQy0dHWY0NiVSKn9JSsCs9tf/JeXM+H2sDgvDclKhB+360ZZqTM37JSFMbF6WhfVr7sjKQoqI4IZJK8e7li7jw8GOMi3VkfLE0MMT+dZvwjxuJiM/LQZdIBKC3A2VNQCBenTYDxvqa2Z10IDsTYgbF8z2ZaRNmHOtwMfn4+wwcXTfJ3gFeFpYK9xUNFOvprXQ31HA9HzkNlgaG+HfyDVS3/97p4mBsjGenRIHDYmHeT9+hvK21/7VgWzu8HBWNeR6eGslBVx4OnYQpjk7YnZ6Ky2UlEEul8LOyxpaQMMR6eo/pGE/2vV2JCzzVj+7lsJj3sbDZyj+mR0LDcbaoELdrqhW+bqLPw/tzFzC+1ki1CXpwvbICArEYPpZWSkcb9/GytMKB9ZuR01CPV06fUFl8XhsQBB8rxZ1WmhJqZw9XU7NBXyOKTLJzgEQmRQaDLnAXFV1tI31Ih8NiYUNQCP4yZ96I3q/MCl9/zHBxxYHsTCRVVUIm690Puik4BPbGJupPMI5QQYwQQgghhBBCCHkAHMxRPUqtz4HszDEpiNV1dmB3ehoO52ajvqsTVgYGWOkXgIdDJ8HRZPR7MPpIpFK8cOo4zhQVDDre0tOD71JuI6G0GHvXbtLKcvh2gQB3OzpgqKeHRV4+sDUyQl1np9J4FoBtoZM0cm0mxbfSlmYkVpRPuPGT5HdWhob4cF4s3pwxG/lNDWCBBX9rGxjq6Wn0OvmNDQzj1HdQTVS2RkZqv4aB3q/joTf/X46Kxounjyt9D5/Lxf9MmaqJNPv17etLqqpEQ1cnrAwNMc3JBf+4kYj/3LopF59ZV4unjh3G3xcuVdjBNJ4FWNvgw3mxY53GqEQ4OILH4UIgUV0YcTe3gKOKggSPy8VPq9bhk8QriMvJQue9QjkLwCxXd7w1aw78NNRJpEiXSIQPr17CkdxsdA8o8oTZ2eNPs2PU7rAMsLbBkU1b8eaFszienzeoa47LZmNjUAje1XDxRxE2i4UnI6bgnUsXVMY9MyUSjV1dagtihnp6WKGiq8rb0goY8nuKIou8vBFgbYuWnm5YGxphtX/goHHSmmRpYIhnJk/FM5M1+71J16ggRgghhBBCCCGEPABq2pntv1A1eklbsuvrsP3IQTR2/74vpKajA1/fTsbezAx8v3I1wocxOkmVXzLT5YphAxU3N+P9yxfxryXLNXI9oLfQ9MXNGzhRkN9/czPQ2gZrA4LwU1pKfyfPUG/NnKORkUdSmQwFDEa7AUBuQ/0DXRCTyWT3RYecCY+n9kbzaCjaV6Qwjn3/bmvh3LsZv/PmDZVx0S6ucqMsl/n6oVXQgx1XLskVPMz5fHy+eBmCtLDTicNmI9rFtf//c+rrFBbD+sgAvHr2JA7nZuGJ8CmY7eau8ZyIYhYGBlju64e4nCyVcdtCwtR+zzLU08NfYubjtehZuF1dBZFUAl8ra7iamWsyZTkCsRiPxccNGhnaJ632LrYdOoifVq3FVDWd0HyuHj5btAyvTpuBY/m5aOruhq2RER7yC9Bpd9K20EmobGvFN3duyb3GAvDmzNlY5OWDbpEIuzPSVI4efT4ySuUYxo1BIfjyVpLasZlPR0Rq7PejBwUVxAghhBBCCCGEkAcA0/0XmhrRxZRQIsFTxw4PKoYN1CrowVPHjuDyo0/CSAPj3nanp6qNOVNUgLrODtgy3PWjSm5DPbYe2o/mnp5Bx7Mb6pHdUI81/oHoEYtxrrgQonsj2KY4OuHpiCmMxmoxwWaxwGGzGY14exB3eyVXV+LH1BQklBZDKJHA09wCm0PCsDEoROOdVfeL2W7uOJybrTYuxt1DB9mMnaciInGhuAjZSm58m/H4eGe24u6VLSFhWOLti4M5mcioqwWHxcZ0Zxes9PMHn6ubv3e7GXSOAsDV8jJcLS/D/0yZitejZ2k5K9LnT7NikFlXizwlHZkLPLywPSyc8fmM9fUxR4dfk/uyMhQWw/oIJGK8k3Aep7c9yuh8rmbmeD5ymoayG5k3Z87BUh8/7M5IRfrdu2CxWIh0dMK20En9D7AY6Onh51Xr8PKZE7hWUT7o/QZcLp6PnIb/mRKl8jpOpqZ4KmIKvr6drDTmIb8AKoaNABXECCGEEEIIIYSQB8AKX3/8mpmuNk7Xi9FPFuSjRk1XWlNPN+LzcrAlJGxU12ro6mLUKSWSSnGrugpLffxGdT0AeOXMSbli2ECHcrOxa/U6fDgvFnWdnTDl8WBnPPpC3FAzXd1wqbREZQzrXtyD5LuU2/jr1UsY+Ax+YXMTdlxJwKGcLOxevR5mfP6Y5TdeLfH2xf/9dgW1ncq/dvlcLjYEhegwK90z1tfHL2s3YMeVSzien9ff7dX3tfSnWXNV7jayMDDAUxGROspWXnrt3WHF/+fWTYTa2WPRfb5ncrww4/Oxb90mfHM7GfuyMtDY3QWgd0zitpAwbA8LB2ccd2H+wuB3jvymRtysqlTbJTaehNrZ4xO7xSpjrA0NsXv1euQ01ONMYQG6REK4mpljpV8A4weP3pgxG8b6PHxzOxntQkH/cT6Xi60hYXhzxuxRfRwPKiqIEUIIIYQQQgghD4Bpzi6YZOeA1NoapTEOxsZ4yE+3u2LOFxcyijtXXDTqgphUpr5Dqo9EzZgiJm5WVSp9sn+gXemp+GrZQ1otvDwaFqG2IDbbzR2eFpZay2G8uVlVKVcMGyirvg5vXTiLL5et1GleE4E+h4Ovlq3E9vg4tAkE8q+zOfh80TLYGBqNQXa6Zcrj42+xi/H2zDm4VV0FiUyGAGsbuJlrdxydJoxkOOgPKXeoIKZDpjweXoueiZeipuNuRwfYbBYcjU3G/WhXsVTKeNdgTkPdhCqIDUeAtQ0CrG2G9Z47NdXYlZ6KaxXlkEilCLG1hZ+1DeyMjGFhYIBFXt4w5dGDGiNFBTFCCCGEEEIIIeQB8fXyh7A9Pk7hXgt7I2P88NBanY+I6xIr3p81VA/DOFWsDY3gYGyCmg7V+9RYAII1sL8nqaqCWVxl5bDPLZJIcKowH8fz89Ai6IG9kTHWBgRhtpu7whuls93c8ULkNHyRrHjfkbu5BT5esGjYeUxkP6TeUVoM63O2uBBVbW1wMjXVSU4TSZi9A45u2obvUm7jcE42OkRC6HM4WObjhyfCJyPQxnasU+zXKRTiSF4OUu/2PhAw1ckZK3z9NDqa0MLAALFemhlzqiuRTs7IrK8b1ntuVleiTSDQ+XjdB50ehwMXM7OxToMxNosFNouldgcWAHBY47fLTdf+nXwDf7+eOOjYtcoKXKuswBJvX3y+eBm447grcCKgghghhBBCCCGEEPKAsDEywpGNW3GqMB9x2Vmo7eyAGZ+PFb7+WO0fCGMN7OgaLg9zC1yC6s6lvrjRYrNY2Bwcin/cSFQZN8PFTSPXY3IjcDhxfSpaW/FofBxKWpoHHT9ekIdIRyd8u2KVwqfHX50+A6F2dvghNQXXK3v3mtgaGWFjUAgemxQBc77BsPKY6BJKitXGSGUyJJQWY1voJB1kNPG4mpnjvZj5eHfOPHQKhTDU0xt3I9xO5OfhrYtn0SEU9h+Ly8nCR79dxj8WLr3v95ypsi10En5KSxn296De0ZBUECPKsVksTHd2QeKQHVpDsQDMGMNRva09PbhZVQmRVAp/a+sx7ZI+W1QgVwwb6FRhPjyTLPDH6TN1mNX9hwpihBBCCCGEEELIA0Sfw8FDfgE6H42ozObgUPyQekdt3MbgUI1c7/HwyThfUqR0d44Fn49358zVyLVC7ewZxjHvRhOIxXjkyEGUtbYofD25ugrPnzyGXavXK3x9gac3Fnh6o1skgkAihimPD/Y4H72lDRKpFEKphFFsj1is5WwmPjaLBZNx2DF0tawUL585oXAEaktPD549EY9f12xAuIPjGGQ39jzMLfDn2TF473IC4/dYGRjA4gErnpOR2R4WrrYg5mVpOSYP43QIhfjr1Us4kpcz6Hv8NCcX/Hl2DALGoMP1u5TbamP2ZKTh+cgojXa3PmjG1yMbhBBCCCGEEEIIeaB4W1phs5pi10o/f4QxLC6pY6inh92r12NzcOig8ZBsFgtz1I64ugAAIABJREFU3T1xYP1meFlaaeRac9w84Mxg1N7WEObdR8fyc5UWw/okVpT3j4ZTxkBPD+Z8gweyGAYAHDYb7gy7AL0sH5y9avebf964pnIfoFAiwU4lY0QfFNvDIvDt8lWYzLAouD4whEa2EUYWeHrjsUkRKmMKm5ow8/tv8Mb5MxoZjcxEt0iEhw8fwN6sDLkHHm5UVWDjwX3IqqvVSS59mrq7kFxdpTaupacHN0YwZpn8jr57EUIIIYQQQgghZEztmLsAT0dMAZ87eJCNPoeD7WHh+DR2iUavZ6yvjw/nxeLa48/gh5Vr8O2KVbi8/Ul8t3K1RsclsVksfDx/EfQ5HKUxy3x8sWgYe4fi83I0Gvcg2xwcojbGycQUc9we3JF6E1lRUyNSa1UXhgHgSlkp6js7dZDR+DXf0wsH1m/GvxYvV1nscjE1wxPhk3WYGZno/jx7Lj5fvAzh9g5KY0RSKQ5kZ+LpY/GQSKVaz+mntBSkKekSB4AOkRDvXrqg9TwGKm9tZRzbJRKqDyJK0chEQgghhBBCCCGEjCk2i4U3Z87Bs1Om4mRBPhq6umBhYICl3r6wMjTU2nVNeTzM0fL+oOkurvhlzQZ8kngVN6t/f6rbysAQD4dOwvORUWANo0urububUVxjd9ewc33QbA4Ow6GcbOQ1Nih8nc1i4X9nxTywXXQTXU1HB6M4qUyG2s4O2BgZaTmj8W+5rx/M+Xy8c+kCSofsKIx2ccXfFizW6vfk8UYokeB6RTlaBT2wNzZBpKPTsL5fk14rfP0R4+aBad99hW4VI2h/qyjD2eJCLPH21VouMpkMv2amq427c7cGOfV1Wh+dKJXJ8HHiFXzPYFxiH1czcy1mdP+jghghhBBCCCGEEELGBXO+AbaEhI11GhoX4eCIves2ori5CaUtLTDU00OEg6PKzjEA6BQKcSg3G0dys1Hf1QlLA0MIJMz2XtkY0s19dYz19bFnzXq8cf4MLpYUY+BgPQdjE/x59lws9vYZs/zI6Jjx+cxjecxj73czXd1w4eHHkFhRjpyGOnDZHMxydYO3hkbJTgQymQxf307Gdym3Bz1c4GZmjlenz8AKX/8xzG5iis/LUVkM6/NrRrpWC2JtAgEq2ph1Y2XpoCD2SeIVfHvnFuP4YFs7BNsy3ztK5FFBjBBCCCGEEEIIIUQHPC0sGY9kLGtpwSNHDg66cVfZ1sb4Wmv8A4ed34PI0sAQ365YjbKWFlwqK0aPWAxvCyvEuHuAQ3uSJrRgG1u4m1vIdToNFWZnDxczMx1lNTGwWCzMdHXDTFe3sU5lTLx76QJ2Z6TJHS9rbcFLp0+gTSDATBc3nC0uQIdQCDczcyzx9oXBgL2UZLCi5iaNxo3UcL6vc1ja/RlQ39mJH1LvMI7nstl4PXqmFjN6MFBBjBBCCCGEEEIIIWQckUileOLYIcZPsQ8138MTQfQE+bC4mZtju3nEWKdBNIjFYuGZiCl46+I5lXHPTJ6qo4zIRJB6t0ZhMWygdy9dgEwmG9RV+v6VBLwyLRrbw+j7iCI8NR3R/XFc7ZYrjPX1EWpnj3QVO8QAgMNiYZqzi1ZzOZybDRHDnWm2Rkb4YO4CzHJ112pODwIqiBFCCCGEEEIIIYSMI+eKi1DcrLqrRZkYdw98vni5hjMiZGLaGByKqvZ2fJF8Q+HrS719YcDlQiKVUkfgBJNVV4sf01JwoaQI3SIxPCwssCkoBOsCg2E4zE4tsVSKDqEAxvo87FFTDAN69z4N1SYQ4L3LCZBIZXg8fPKwrv8gmOfhhW8YjAac5+6p9VweCZ2E186dVhkz39MLDiYmWs2jup1Z17c5n4+rjz4FPYZFRaIaFcQIIYQQQgghhJAJoqm7C/uyMnC6sACdIiFcTM2xISgYsZ7e4NLN3PvGycI8RnFhdvbwtLBES08P7I2NsT4wGJPsHbScHSETy6vTZ2CJtw92Z6QhpaYaDV1daBMKIJRIcLIwHycL8+FgbIw/TJ2OjcGhY50uYeBAdibevnAWkgGFqdyGevzl8kXsz8rArtXrYWFgoPY8BY2N+DYlGcfz89AjFoPP5YLHGd3t8s9uXMPGoBAY6euP6jz3m6lOzgi2sUVmfZ3SGH02B9tCtb9HdLV/IK5XViAuJ0vh6x7mFtgxd4HW8zDW5zGKszU0omKYBmm8IFZbW4ubN28iJycHDQ0NEIlEsLGxQXh4OObNmwcejycXf/jwYRQUFEAikcDFxQXLly+Hn5+fplMjhBBCCCGEEEImrOTqSjx17AjaBIL+Y8XNzbhcVoIpjk7474rVMOUxu7lCxrcOgZBRHJ/Lxd8XLtFyNkRXhBIJThbkY19WOkpbWmCgp4eFXt7YFhIGZ1PacTUaATa2+HBeLN44fwYHsjPlXq/p6MBbF8+hXSjEkxFTxiBDwlRWXa1cMWyg7IZ6/L/zp/HtitUqz3OtohxPHTuMbrG4/1iPWIyeAf8/Eh0iIY7l52ITFVflfLF0BbYeOoAqBZ1Remw2/rZwMdzNLbSeB4vFwicLFmGKgyN+Sk9FbkM9AMDKwADrAoPxdEQko4LqaC3z8cWXt5LUxi31oTqJJmm8IHb9+nVcvnwZoaGhiIyMBIfDQX5+Po4dO4Y7d+7g9ddfh/69Cnl9fT0+/fRTsNlsxMbGwsDAAImJidi5cydeeOEF+Pv7azo9QgghhBBCCCFkwqnt6MCTR4+gXShQ+Pqt6iq8fu4Uvl6+SseZEU0SS6X4KS0Ft2qqGMU7mZhqOSOiK20CAR6Pj8OduzWDjn9zOxk/pabgiyXLMd/Ta4yyuz/crqlSWAwb6NPrv2FNQCAsDQx1lBUZrh/TUpQWw/pcLClGaUuz0uJKh1CI504eHVQM06TS1hatnHeiczUzR/ymrdidnob92Rmobm+HoZ4elnj74rFJEQi0sdVZLiwWCxuDQ7ExOBT1nZ0QSiU678QKsLHFTBc3/FZRpjTGRJ+HzVRc1SiNF8TCw8OxaNEiGAyoos6ePRtHjx7F6dOnce3aNcTExAAA4uPj0dXVhTfffBMuLr1L6qKiovDBBx9g7969ePfdd8FisTSdIiGEEEIIIYQQMqH8kpmmtBjW51xxEYqaGuFlaaWjrIgmiaVSPHM8HgmlxYzfsyEoRIsZEV36f+dPyxXD+ggkYrxw6jhObn0EHjronrhf/ZKRrjZGKJHgYHYWnp4cqYOMyEhcKClSGyMDcL64SGm33+Hc7EHd1ppmwKUtRcpYGhjiD1HT8Yeo6ZDKZGCPg3v/NkZGY3btzxcvw6Pxccioq5V7zUSfh29XrBrT/O5HGh8w7ubmNqgY1mfy5N5lgtXV1QAAgUCA9PR0+Pr69hfDAIDP5yM6Ohp1dXUoK1NeHSWEEEIIIYQQQh4UJwqY7ZQ6UZCv5UyItvyQentYxbA5bh6Y6uSsxYyIrpS2NONcUaHKGIFEjF3pqTrKSD2BWIzbNVW4WVWJpu6usU6Hkbx7Y9HUyWUYR8ZGl0jEKK5brDzualmphrJRbKGnt1bPf78YD8WwsWZhYIAD6zfjHwuXYrqzC5xNTeFvbYNXp83A+Uceo5/zWqCzcnVLS2+rqKlpbzt/VVUVxGIxPDw85GL7jpWVlcHd3V1XKRJCCCGEEEIIIeMS0yfZtfnEO9EeqUyG3elpjGJZABZ7++JvsYu1m9QDrLy1BQeyM1HR1gojPX0s8fbFDBdXrU0xOlNUANUD4HqdKsjHO7PnaiWHoRq6urAvKwMJJUUQSiTwtLTEluAwhNrZ4bOk69iflYGWnh4AgD6bgyU+vngteua4HuPJZTgKTZcj0whzZS0t+PuN3yCSSBjFq+qmFEmZncOCz0erQACpTAYeh4s5bu5IKC2GSCpV+p7pzi4I0OHoPzLx6XM4WOUfgFX+AWOdygNBJwUxqVSKkydPgs1mIzKyt+W4tbUVAGBubi4X33esr4g2VFdXF7q7uwcdY7PZsLCgtnFCCCGEEEIIIfcfB2MTNHSp78JwMDHRQTZE0ypaW1HR1soo9q/zFmJjMI1K1AaJVIq/XL6IXzLSBhWofs1MR6C1Db5esUorBZ9OIbOOlw41Y1M15VJpCV44dWxQJ05mfR2O5uXCkm+App7B9+SEUgni83Jwo7ICB9dvhpPp+CyKzXZ1R3rtXUZxZGSq2tsQl52FyvZWGOvzsNjLRyMdLkVNjdh4cJ/c3z1lrAwMsNDLR+nrflbWuMygS2yVfyBejopGu1AASwMD8Ll6OFmQh1fPnIJQQVHNx9IKny1axihHQsjY0ElB7MCBAygpKcHKlSthZ2cHABAKhb0JKJipqqenNyhmqIsXL+LkyZODjllaWuKDDz7QZNqEEEIIIYQQQsi4sD4wWOF+iYH02Rys8qOniyciiUx5t8FQE22XyI3KCvySkYb8pkbos9mY4+6BzcGhcByHnUQf/nYZezIUd+plN9Tj4cMHcWzTNhjp62v0ukwLSM6mZv3/3S4Q4HBuNpKqKiGVyRBia4cNQSGwNjQcVS6FTY147uRR9IjFCl9XVZCo7ezAXy5fwLcrVjO6VkVrK04X5aNdIISTiQmW+vjBhMcbUd5MbA4OxTe3kxUWMvrYGxlj4f9v796jmjrzvYF/wzVATLgLgtwEBMtFRBEUFVFRvLZV67Rap+10enrUZW1rZzrnbU/ntHa63nU6tdVO7ZnpvDNjjzOdqq0KglrlUgHlIgIVrFDk6oUKCMgtJCTvHzYZYxIISAiG72ct//DZTza/PD7uvdm/5zKFy90NlUKpxK5vM/F5WQn6lf9KJ/+1pBjTJ3pi34rVmCgSDfv8v8n4xuBkGAD8au582Aww0+9nYRH4U3HRgDMzBQA2hkdigq2tRr9cHjQVQc6u2F92ESerq9Atk2GyWIKfhYVjXWjYiF8fiGhkGT0hlpKSguzsbMTHx2PZsn9N57f56eIg13GDlf00AsVGzwUkMTERcXFxGmUWFiO+HRoRERERERHRmPB46CP4W+lFVN9u1VvnmagZcHnAl+FkGl4TxHASCnH7pyXo9LEUCBDq6jZKUT0YuUKBnd+k49iV7zXKL936EX8qLsIHSclYHjTVRNFp+7GrEwcG2aOrtu02vvq+Ak9HTB/Rn70iaCre/TYLnTLdA8NVNjxyd2bg6as/4JWT6Rr1T1ZXYU/+Ofyf+QkPFN9fSor1JsMMkVlbg2sdHQMm+Tr7+vCbMyeR/kMVFPckT3adzcKLM2OwdVbssH/+QDwnTMDvk5Lx8qk0yHUseSe2tcW+Fau5ZOIwvJeTjb+WXtR5rKTpBjYfOYQjGzbC7qdJEENxufkWiq5fM6iui50dfjV3PtZPCxuwnp+jE16cGYN9RQV66zw/YyYCnJx1HgtyccE7CxfjnYWLDYqLiMYOo2aRUlNTkZ6ejri4ODz55JMaxySSu6NadC2LqCrTtZwiANjb28PFxUXjD5dLJCIiIiIiInNlb22Nzx9bh4iJHlrHLAUCPDc9Gr+aM88EkdFIsLWywrpBXuACwCL/KQ/Nspjv553VSoap9PX34+WTaYPOehxNR69cHnBfIJVDFZdG/GeLbGywNWb2gHWmODlj3bQwXLxxHVvTUnQmz/oU/Xgr6wyOV14Zdiwpev7NDKVQKlHadEPvcblCgV+mfI3jVZUayTAA6JLJ8PtzufgoP++BYhjIiuCpOLjuZ1gRFAzrnwbXi6xtsDE8Ekc3bEKkh6fRfra5utXVhf16kmEqVa0tOHrl8rDOX3zjusF1MzY/N2gyTOW1OfPwH/EL4GKnOZDEWWiH1+fOx2/iFwwpTiJ6OBhthlhqairS0tIQGxuLjRs3am08OmnSJFhZWaGmpkbrs6oyHx8fY4VHRERERERE9FDxEE3AkQ0bUXCtESd+qESXTAZvsRjrQsMemiQJ6bdl5mxk1dagqrVF53EXO/uH5gVtZ1+f3qUHVWQKBf7fxQvYvXT5KEU1sBudnQbVa+oyrN5Q/Vt0DBRKJfYWnNeaoRXtOQkfJ6+CyMYGfyjMHzRxt7fgHFYED332nVyhGHSWmmEEeo+cqv4B+dcaB/z0vqICbAqfbrQZr5EentibvApSuRzdMhkm2NrCiitPDdvX31cYlEw+WHEJPwuLMGosNpZDe9X9/IyZ2BwZhW/ratDc3Q0XO3vM9/WDrY4tfojIPBjlf3daWhrS0tIQExODTZs26VzOUCgUIjw8HCUlJWhsbIS3990NFnt7e5GXlwd3d3f4+fkZIzwiIiIiIiKih1aMlzdivLxNHQaNMIlQiC/WbsCus1k4XnUFff139zmyEAiQ4OuPN+cvhK+elXTGmszaq+j6aTuMgaT/UIn3lyyD5RhIRjjaCg2qJzGw3nD8+8zZeCosEkevXEZtexvsrKyQNCUIkT/NDG3t6UZWnfbA8vtVtragrOmmzhmlA7GysICLnT1aerqHFT9wd8Zq1ACzrP5ZXjboOfr6+/H19xV4fsbMYcdhCFsrKyY+RsCNzjuG1btjWL37zfCcZFC9MPeJw/r3tLG0xOIA7htHNF6M+FU/OzsbqampcHZ2RkhICAoLCzWOi8VihIbe3eR3zZo1uHLlCvbu3YvExEQIhULk5uaira0NW7Zs0ZpVRkRERERERERkrpzs7PD7pGT8R/wCXLx5Hf1KJcLcJg64H9NY1D7IXmgqff396JHLIdKzh/xoWhk8FR/l50E5SL1VwSFGjUMiFGJzZJTOYy3dPVrLDOpzq7trWD9/7bRH8McLhYNX1GNxQOCAM1brdGydoktN2+1hx0CjS2xrO6L17hfq6oaZk7wG3UdspPf2IyLzNOIJsbq6OgBAa2sr9u/fr3U8KChInRBzd3fHq6++iqNHj+LUqVOQy+Xw8fHBtm3bEBJi3AcMIiIiIiIiIqKxyMXe/qGeseApMmwJT7GtLRysrY0cjWECnJyxZEogTlX/oLeOo1CIDWHhoxjVfT/fTggBMGjSDgCchHbD+hnPREbhcMUltPT0DPmzXhPEeGvBwgHr2Bn4720/RvoFDW5F0FTsLTg/eL1hLOOp8l7iEmw49E+09urul0kBgXg8ZNqwz09E48eIJ8Q2b96MzZs3G1zf09MTL7744kiHQUREREREREREJjDf1w/uDg74sWvgWUprQx8ZU6sDvb8kGS/0HsH5aw1ax5yEQvx59eNws3cwQWR3udk7IG6yD/Ia6ges5ytxHHDZwoF4iCZg/6Pr8G/Hj6Kxo0PjmJWFBV6YMRNWFpb4x6Uy9Sw0e2trrA4OwY7YOXB3EA14/qQpgbjS0jxoHElTHt6E8HgT7OKKhX4ByKy9qreOxFaInz0y/P3Dpji74NATT+KD87k4+UOVes8yDwcRNkZE4t+iY8bE0qtENPZxoVwiIiIiIiIiIhox1paWeGn2HPyfjG/01nEW2uG5qOhRjGpwIhsb/O/j65FZexUHyy+hoaMdDjY2SA4MxtrQaRAbcf8wQ/37zBicb2wYcOnEF2fGPFCiMdTNHRmbf4HTV6uRUVuNXrkcQc4ueGJaOCaK7ia8ts6ajarWFsgVCvg7OmGCgcvhPRkWgT9fvIDuAfaYi5jogVmTuE/iw2T30mT84tjXuHDjutYxia0Qn61+FG4OD5ZM9nN0wp5lK9HS3Y269jZYW1oi1NUNVkyEEdEQMCFGREREREREREQj6smwCHT19eH9czno6+/XODZZLMGnK1bDa8LY2xvNQiDAIv8pWOQ/xdSh6DR3si/+7+Kl+I8zp9SzZFQEAHbEzsGGRx58WUcrCwssCwzCssAgncetLS0xzc19yOf1EE3AH5JXYUvaMfTI5VrHfSWO+GT5qiGfl0xLbCvEP9ZuwDdXf8CX5d+hoaMdIhtbLA8MxvppYXCyG94Snrq42NvDxd5+xM5HROMLE2JERERERERERDTinp8xE4+HTsNXlytwpaUZNpaWSPD1R6J/AJc3ewBrQx9B/GRf/ONSGfKv3Z0tFu7ugY0RkfB3dDJ1eINa4OePtKd+js/LSpD+wxXckfZh0oQJWP9IONZPC4PYwNlmNLZYWVggOTAYyYHBpg6FiEgvJsSIiIiIiIiIiMgonO3s8fyMmaYOw+xMFImwI3aOqcMYNl9HR7wxPwFvzE8wdShERDSOcDgOERERERERERERERERmTUmxIiIiIiIiIiIiIiIiMisMSFGREREREREREREREREZo0JMSIiIiIiIiIiIiIiIjJrTIgRERERERERERERERGRWWNCjIiIiIiIiIiIiIiIiMwaE2JERERERERERERERERk1pgQIyIiIiIiIiIiIiIiIrPGhBgRERERERERERERERGZNSbEiIiIiIiIiIiIiIiIyKwxIUZERERERERERERERERmjQkxIiIiIiIiIiIiIiIiMmtMiBEREREREREREREREZFZY0KMiIiIiIiIiIiIiIiIzBoTYkRERERERERERERERGTWmBAjIiIiIiIiIiIiIiIis8aEGBEREREREREREREREZk1JsSIiIiIiIiIiIiIiIjIrDEhRkRERERERERERERERGaNCTEiIiIiIiIiIiIiIiIya0yIERERERERERERERERkVljQoyIiIiIiIiIiIiIiIjMGhNiREREREREREREREREZNasTB2AQqFAZmYmcnJy0NLSApFIhOjoaKxcuRK2tramDo+IiIiIiIiIiIiIiIgeciZPiB06dAhZWVmIjIzEokWLcPPmTWRmZqKhoQHbt2+HhQUnsREREREREREREREREdHwmTQhdv36dWRnZ2P69Ol44YUX1OUuLi44ePAgLly4gFmzZpkwQiIiIiIiIiIiIiIiInrYmXT6VVFREZRKJRITEzXK4+PjYWNjg4KCAhNFRkRERERERERERERERObCpDPE6urqIBAI4Ovrq1FubW0Nb29v1NXV6fxcd3c3enp6NMosLCzg5ORktFiHaqKvm6lDoIeU1wSxqUMgojGK9xYaLt5biIiIiIiIiGi8M2lCrL29HSKRCNbW1lrHHB0dcfXqVcjlclhZaYaZkZGBtLQ0jbKAgADs3LnTqPEOxf/WfGLqEOghdfbZX5o6BCIao3hvoeHivYWIiIiIiIiIxjuTLpnY19enlexSUZX39fVpHUtMTMQ777yj8efZZ59Fb2+vUeMlMqbbt2/jjTfewO3bt00dChERmQneW4iIxiZen42D7Wo8bFvjYLsaB9vVONiuxsO2NQ62q3E87O1q0hliNjY2uHPnjs5jcrlcXed+9vb2sLe3N2psRKNNoVCgtbUVCoXC1KEQEZGZ4L2FiGhs4vXZONiuxsO2NQ62q3GwXY2D7Wo8bFvjYLsax8PeriadISaRSNDZ2QmZTKZ1rK2tDSKRSO8MMiIiIiIiIiIiIiIiIiJDmDQh5uvrC6VSibq6Oo1ymUyGxsZG+Pj4mCgyIiIiIiIiIiIiIiIiMhcmTYhFR0dDIBAgIyNDozwnJwd9fX2YNWuWiSIjIiIiIiIiIiIiIiIic2H529/+9rem+uFisRidnZ3Iz89HY2MjpFIpCgsLkZaWhsDAQDz++OMQCASmCo9o1FlZWSE4OBjW1tamDoWIiMwE7y1ERGMTr8/GwXY1HratcbBdjYPtahxsV+Nh2xoH29U4HuZ2FSiVSqUpA1AoFMjIyEBOTg5aW1vh4OCA6OhorFy5EkKh0JShERERERERERERERERkRkweUKMiIiIiIiIiIiIiIiIyJhMuocYERERERERERERERERkbFZmToAorHojTfegIuLC15++eUBy4iIiEZbamoq0tLS8M4778DFxUVd3tDQgK+++gr19fXo6enB8uXLsXLlShNGSkREREREREQ0djAhRmRklZWVqKysRGJiIuzt7U0dDhERmaH+/n786U9/Qn9/P1atWgU7Ozt4eXmZOiwiIrqPQqFAZmYmcnJy0NLSApFIpN5D29bW1tThjWlNTU0oKCjA5cuX0dzcDJlMBjc3N0RFRSExMVGj/VSDR3R57LHHsGTJktEK+6GxZcsWneW2trbYvXu3RllTUxO+/vprVFVVob+/H5MnT8bKlSsxderU0Qj1oTFQPwQACwsLfPzxx4PWHc999sSJE2hoaEB9fT1aWlrg7OyMXbt26a1fU1ODY8eOoba2FgKBAAEBAVizZg0mT56sVbetrQ1HjhxBRUUFpFIpPD09kZSUhBkzZhjzK40JhrarTCZDfn4+Ll26hMbGRty5cwdisRj+/v5ITk6Gp6enRv2Wlha8+eabOn+mp6en3mPmYij9df/+/Th//rzOY88//7xWP5TJZDhx4gQKCgrQ3t4OR0dHxMXFISkpCZaWliP+XcYaQ9t2oD6o8swzzyAmJmbQ+ubeZ4fyXKWqb+i9v6enB8eOHUNJSQm6urrg5uaGBQsWYN68eRAIBKP1FfViQozIQG+99daw/tNWVlYiLS0NcXFxTIgREdEDS05OxtKlS2Fl9a/HuObmZjQ3N2Pt2rVISEgwXXBERDSgQ4cOISsrC5GRkVi0aBFu3ryJzMxMNDQ0YPv27bCw4K4G+pw7dw7Z2dmIiIjArFmzYGlpicrKSqSkpKC4uBivvfYabGxsND6zbt06ODg4aJT5+PiMZtgPlcDAQMydO1ej7P4Xrbdu3cL7778PCwsLLFmyBHZ2dsjNzcXevXuxbds2hISEjGbIY9r06dPh5uamVX7t2jWcPn0a4eHhWsfYZzUdO3YMDg4OmDx5Mnp6egasW1NTg927d8PR0VG9SkJ2djY++OAD7Ny5U2OwWFdXF37/+9+js7MTiYmJcHJyQmFhIT777DM8/fTTiIuLM+r3MjVD27WlpQV///vfMWXKFMyZMwcSiQTNzc04e/YsSkpKsHXrVp0vwyMjIzF9+nSNsvHwPmwo/VXl5z//uVaZn5+fVtmf//xnlJWVIS4uDgEBAbh69SpSUlJw69YtbN68+UFDH/MMbVuRSKSzTQHgyy+/hEzbmJ+iAAAWdElEQVQmw7Rp07SOjcc+O5TnqqHc++VyOfbs2YOGhgYkJCTAw8MD5eXl+OKLL9DR0TEmVrFhQozIQNbW1qYOgYiICJaWllovpzo6OgCM/EO7UqmEVCqFUCgc0fMSEY1H169fR3Z2NqZPn44XXnhBXe7i4oKDBw/iwoULmDVrlgkjHNuioqKwdOlS2NnZqcvmz5+PY8eO4cSJE8jLy9MaFBIZGamxvDANzNXVFbNnzx6wztGjR9Hd3Y3XX39dPetm9uzZ2LVrF7744othDyQ1R97e3vD29tYq//vf/w4AmDNnjtYx9llNb7/9NlxdXQEA77zzDqRSqd66X375JaysrPDKK6/A0dERABAdHY23334bhw8fxvbt29V1T548iZaWFrz44ouIiIgAcPff47//+7/x1VdfISoqyqyffw1tV5FIhN/85jdaM+xiYmLw3nvv4euvv8brr7+u9TkvL69BryXmaCj9VcWQdrp06RLKysqwaNEirF27FgAwd+5c2Nvb48yZM5g7dy6mTJnyYMGPcYa2ra2trc42vXr1Knp6ehAVFQWRSKR1fDz22aE8Vw3l3p+bm4u6ujqsX78eCxcuBADEx8fjj3/8I06ePIm4uDiT3+eYEKNxrbW1FV999RUqKioA3B0Rt379ep11de0hVl1djfT0dDQ2NqK7uxsODg7w9vbG8uXL4e/vrzEF+t5ptqp9Xdra2nDmzBlcuXIFLS0tkMlkcHV1RWxsLBYvXqwxQvTcuXP4/PPP8dJLL6G+vh5nz55FW1sbnJ2dsWzZMsTGxmrFfOXKFZw+fRq1tbWQSqWQSCQIDg7GY489pnEDKCoqQnZ2NhobG6FQKODl5YXFixePi6UCiIiMRd9eX4D2PWXLli2IjY1FfHw8jhw5gvr6elhbWyMyMhLr1q3T+IX8/vPu3r0bVVVVAIDPP/8cn3/+OQCoj0ulUqSnp6O4uBhtbW2wt7dHSEgIVq1apRFXZWUlPvzwQzz99NPo6+tDdnY2mpubkZSUhJUrV2L37t1oaWnByy+/jEOHDqGyshLA3Rc3TzzxBGxsbHDq1Cnk5uaivb0dnp6eeOKJJ8z+lzMiIkMVFRVBqVQiMTFRozw+Ph5Hjx5FQUEBE2ID8PX11VkeHR2NEydO4Pr16zqP9/T0wMbGZlwsKTUS5HI55HK5zmSAVCpFWVkZgoODNV6QC4VCzJkzB6mpqairq9M5u4HukkqlKCoqgqOjIx555BGdddhn/0X1AnwwP/74I+rq6hAXF6dOhgGAo6MjoqKicP78ebS3t0MikQC4ez12c3NTJ8OAu0tYJiQk4G9/+xvKy8sRHR09sl9mDDG0XUUikc7kgaenJzw9PfVed4G7S/wplUqtmbvmzNB2vZdSqURvby9sbW31zhIvLCwEAHVyQWXhwoU4c+YMCgoKzP53ruG07b1yc3MBQGsW9L3GW5819LlqqPf+oqIi2NjYID4+XuO8iYmJKCkpwYULF5CUlGScL2UgJsRo3Oru7sbu3btx+/ZtzJs3Dx4eHqiqqsKHH34ImUw26Oebmpqwd+9eiMViJCQkQCwWo6OjA9XV1WhsbIS/vz/i4+PR09OD0tJSjaUPVFP1r127hpKSEkRGRsLNzQ39/f2oqKjAkSNH0NzcjKeeekrr5x49ehQymQzx8fGwsrLC2bNnsX//fri5uWncAM+ePYsvvvgCEokE8+bNg7OzM27fvo3vvvsOt2/fVj/UqDL/06ZNw6pVqyAQCFBSUoLPPvsMGzZswIIFC0aiuYmIaBCNjY3Yt28fYmNjMWvWLFRVVSEvLw8CgQAbN27U+7lly5YhICAAJ0+eRHx8vPpeIBKJ0N/fj48//hjV1dWIiorCokWLcOvWLXz77bf4/vvv8etf/xpOTk4a58vMzERnZyfi4+MhFos1jvf19eHDDz9EUFAQ1qxZg7q6Opw7dw4ymQwODg6ora1FQkIC+vv7cfr0aezbtw+7du0y6xG2RESGqqurg0Ag0HoBYW1tDW9vb9TV1ZkosodbW1sbAEAsFmsde/fdd9Hb2wsLCwv4+vpi+fLlepMQBFy8eBEFBQVQKBTq/e1Wr16tHj1+7do1yOVy+Pv7a31WVcaE2MCKi4vR29uLhQsX6nz5zT47PKrrZ0BAgNYxf39/nDt3DvX19QgPD0d7ezva2tp0DkC4tx+bc0LsQSkUCnR0dOi87gLAmTNnkJ6eDqVSqd7ratmyZVx5SYdXX30Vvb29sLKyQmBgIFatWqV1ja2rq4OjoyOcnZ01yp2dnSGRSPj8MIje3l4UFxfD2dlZ77K+7LP/cv9z1VDu/QqFAvX19fDx8dFqO19fXwgEgjHRX5kQo3Hrm2++QUtLi8b60AsWLMDBgweRmZk56OcrKirQ19eH5557Tu8Df0BAALy8vFBaWqpz6YOgoCC8/fbbGktKJCYm4q9//Styc3OxYsUK9QgmFblcjl//+tfqvWNmzJiB//zP/0R2drb6Jejt27dx8OBBTJw4ETt37tRYQmvVqlVQKBQAgPr6epw4cQJLly7FmjVr1HUWLlyITz/9FEePHsXs2bP5IpOIaBRcu3YNO3fuVD9Uzps3Dz09PTh37hzWrl2r91ocGhoKS0tLnDx5Ev7+/hpLPeTk5KC6uhqLFy/G448/ri6fOnUq9u3bh6NHj+KZZ57ROF9rayveeustTJgwQetndXZ2YsmSJRobu3d3d6O4uBiTJ0/Ga6+9ph7N7OHhgU8//RSFhYWYN2/esNuFiMhctLe3QyQS6Xy54ujoiKtXr0Iul2vsEUkDUygUSEtLg4WFhcbLbXt7e8THxyMgIAD29vZoampCRkYGPvnkE2zatMns9wcaDj8/P0RFRcHNzQ29vb0oLy9HdnY2qqqqsHPnTgiFQrS3twOAxgwcFVWZ6kUa6aYa7HR/H2SffTCG9E1VHfbjB3f27Fm0t7cjOTlZo1wgEGDq1KmIiIiAi4sL7ty5g+LiYqSnp6Ompgbbtm3jXpk/EYvFSExMhI+PD2xsbHDt2jVkZGTggw8+wNatWzUSN6rVN3RxdHRkfx3EhQsXIJVKtVbiAthn76fruWoo18zu7m7IZDKtd9nA3QFgDg4OY6K/8kmbxq3S0lKIxWKtNWKTkpIMSoipRsmVlpbCy8trWKMG7p2GK5fLIZVKoVQqERoaioKCAtTV1WlM4Qfurud67y/Jjo6OcHd3x48//qguKy4uhlwux/Lly3XuJ6O6mBcWFkIgECA2NhadnZ0adSIiIlBWVoarV6/q3HCSiIhGlr+/v9aoq6lTp6K8vBytra2YNGnSkM9ZWloKgUCAZcuWaZSHh4fD29sbZWVlUCgUGg/5s2fP1pkMA/61lMy9AgMDUVpainnz5mks7RMYGAgAGvcnIqLxrK+vT2+yS1U+UB3SdvDgQdTU1GD16tWYOHGiuvz+ZSkBIC4uDrt27cKhQ4fMfn+g4fjVr36l8ffY2Fh4eXnh2LFjyMzMRHJyMvr6+gBAZx9V/T6sqkPampqaUF1djalTp2ot/8U++2CG0jcHqnvvtZh0q66uxuHDh+Ht7a31O4azszNeeukljbK5c+fiwIEDyM3NRVFREWJiYkYz3DHr0Ucf1fj79OnTMWvWLPzud7/DP/7xD/zXf/2X+thAzwbW1tbsr4PQNxABYJ+9n67nqpG6vqrqG7Iqm7HxSZvGrebmZvj6+mpl+iUSicaGgvpER0ejoKAAJ0+eREZGBvz9/TFt2jRER0cbvDlgf38/Tp06hfz8fNy6dQtKpVLjeHd3t9ZndK2b6+DggNbWVvXfVS8f79/49H43b96EUqnUuNHe786dOwOeg4iIRoa+6zsArUELhmpuboZEItE5OMLT0xONjY3o6urSSIC5u7vrPZ9YLNYaAKI69/33PlV5V1fXsGInIjI3NjY2ep+t5XK5ug4ZJiUlBdnZ2YiPj9d6KauLSCTCvHnzcPz4cQ76M9CSJUuQlpaGS5cuITk5Wd0/Vf31XqoXXOzD+hmyh8292GcNN5S+OVBdXosHVl9fj08++QQSiQRbtmwxeGD4smXLkJubi0uXLo2r5MJQubu7Y8aMGTh//jyamprUCQkbGxud/RW427/ZX/W7ceMGampqMG3aNK0lJwcyHvusvueqkbq+quqPhWUomRAjGiZra2ts374dtbW1qKiowA8//IDU1FQcP34czz77LKZPnz7oOQ4fPoysrCxER0dj2bJlmDBhAiwtLVFfX48jR45oJcgA6J2qq6vuYJRKJQQCAbZu3ar3vPqmZRMR0cDuXQ73fqqla+81VpZiGOgXqoFiHCvxExGNVRKJBDdu3ND5MqCtrQ0ikYizwwyUmpqK9PR0xMXF4cknnzT4c6rBGxysYRhLS0tIJBJ1e6mWQNK13JGqTNeSSnR3MGxBQQEcHBwQGRlp8OfYZw1jSN9U1WE/Hp76+nrs2bMHdnZ22LFjx5DayMnJCRYWFsMe5DeeqP7Pd3Z2qhNiEolE7zJzbW1tOpeno7vy8vIAAHPmzBnS58Zbnx3ouWoo10x7e3tYW1url1m8l0wmQ1dXF4KCgkY6/CHj0zaNW66urrh165bWUlHt7e3o6ekx+Dx+fn7qPcRaW1vx3nvvISUlRZ0QG+iFaH5+PgIDA/GLX/xCo/xBl5dS3TQbGxs1lg65n7u7OyoqKuDk5MTEFxHRCLt3htS9s6dkMhna29vh5uZm9BhcXV1RUVGB7u5urVliN2/ehFAoVM9CIyIi4/L19cXly5dRV1enXlYWuHtfaGxs1Cgj/VJTU5GWlobY2Fhs3LhxwN+37qf6PUvf0sCkSSaT4fbt2+olnSdNmgQrKyvU1NRo1VWV+fj4jGqMD4vvvvsOHR0dWLhw4ZBGx7PPGsbX1xcAcPXqVa0ZeDU1NRAIBOq+KZFI4OjoiNraWq3zsB/rpkqGCYVC7Nixw+BVkVSam5uhUCggFouNFKH5UP2fv7etfH19UVhYiNbWVo1ZTq2trWhvb9faaoXuksvlyM/Ph0gkGtJABGB89dnBnquGcu+3sLCAj48PGhoatAaA1dXVQalUqq/XpsShvDRuRUREoKOjA/n5+Rrlp06dMujzukYJODk5QSQSaYzesrW1BaB7RJeu0fRSqRQZGRkGxaBPVFQUrKyskJaWpjO5p5pNppr2e+zYMZ2zFTo6Oh4oDiKi8Uw1IOH777/XKD9z5sywZvUOR2RkJJRKpda9rby8HA0NDYiIiODMLiKiURIdHQ2BQKD1rJ+Tk4O+vj715uWkX1paGtLS0hATE4NNmzbpvIf19/fr/B2otbUVZ8+ehYODAwICAkYj3IeGvhHwKSkpUCgUCA8PBwAIhUKEh4ejsrISjY2N6nq9vb3Iy8uDu7u7erAoaRpolgL77INzd3eHj48PLl68qDGLoa2tDRcvXkRwcLDGLJqZM2fi1q1bKCsrU5cpFApkZWXBzs4OYWFhoxr/WNbQ0IC9e/fC1tYWO3bs0LnMu4qua4lCoUBKSgoAqK8l451UKtW5j1JDQwMuXrwIDw8PjcGTM2fOBABkZmZq1Ff9nc8PupWVlaGzsxOzZ8/W2Ov6XuO9zxryXDXUe//MmTPR19eHnJwcjfNkZGTAwsIC0dHRRvs+huIMMRq3kpKSUFRUhAMHDqC+vh6enp6orKxETU0NRCLRoJ9PT0/H5cuXERYWBldXVyiVSnz33XdoamrCkiVL1PVUo+mOHDmCWbNmwdraGpMmTcKkSZMQFRWFnJwcfPbZZwgJCcGdO3eQl5f3wKP1nZycsG7dOvzzn//Eu+++i9mzZ8PZ2RltbW0oKyvDpk2bMHnyZPj5+WHFihU4fvw4fve732HGjBmQSCRob29HfX09ysvLsXfv3geKhYhovAoJCcHEiRORmpqqniVWXV2N2tpag+4zIyEuLg75+fk4deoUWlpaEBgYiFu3buHbb7+FWCzG6tWrRyUOIiICvLy8MH/+fGRnZ+N//ud/EBYWhps3byIzMxNBQUF8oTWI7OxspKamwtnZGSEhISgsLNQ4LhaLERoaCqlUijfffBORkZHw8PCAvb09mpqakJeXB6lUiueee477rdznxIkTqKmpQXBwMJycnCCVSlFeXo7Kykr4+fkhISFBXXfNmjW4cuUK9u7di8TERAiFQuTm5qKtrQ1btmwZ0oy98aKtrQ0VFRXw8/ODl5eX1nH2Wf3y8/PV+6V3dnZCLpcjPT0dAODs7IzZs2er665fvx4fffQRPvjgA3WfzcrKglKpxNq1azXOm5SUhOLiYvzlL3/BokWL4OjoiMLCQtTV1WHjxo0QCoWj8wVNxNB2bWlpwZ49e9Dd3Y2EhARUV1ejurpa41zTp09XDwQ/cOAAent7ERAQACcnJ3R2dqKkpAT19fWIiIhAVFTUKH7L0Wdou/7444/4wx/+gMjISLi7u8PGxgaNjY04d+4cBAIBnnrqKY3zhoeHIywsDGfOnEFPTw/8/f1RU1ODvLw8xMTEjIsZ5kO5FqgYslzieO6zhj5XAUO798+dOxfnzp3D4cOH0draCg8PD1y6dAmlpaVITk4e8gxTY2BCjMYte3t7vPLKKzh8+LB6llhgYCB27NiBjz76aNDPR0ZGor29HcXFxbhz5w6sra3h7u6OjRs3alxsp0yZgkcffRRnz57FgQMHoFAosHz5ckyaNAnr1q2DUChEcXExysrK4OTkhPj4ePj6+mLPnj0P9P3mz58PV1dXnD59GpmZmZDL5ZBIJAgJCYGTk5O63ooVK+Dj44OsrCxkZGSgr68PIpEIkyZNwvr16x8oBiKi8czCwgIvvvgiDh48iKysLFhZWSE0NBQvv/wy3n///VGJwdLSEtu2bUN6ejouXLiAkpIS2NnZYcaMGVi1atWQNhYmIqIHt379eri4uCAnJwfl5eVwcHBAQkICVq5cyRm7g6irqwNwd9bM/v37tY4HBQUhNDQU1tbWiIqKQm1tLUpLSyGVSiESiRASEoIlS5ZwBpMOQUFBuHHjBs6fP4+uri5YWFjA3d0dq1evxqJFizSWPHJ3d8err76Ko0eP4tSpU5DL5fDx8cG2bdsQEhJiwm8xdp0/fx4KhULvS1n2Wf3y8vJQVVWlUaaauREUFKTxEnzKlCnYsWMHUlJS1HUCAgLw/PPPw9vbW+McIpEIO3fuxJEjR5CdnQ2pVApPT08899xz6tk45szQdm1paVGvdnT8+HGd5woMDFQnxMLCwlBQUIDc3Fx0dXXBysoKnp6e2LBhA+bNm2f29zlD21UsFiMkJASVlZUoLCxEX18fJBIJoqOjsXTpUnh4eGid+5e//CXS09NRUFCAgoICSCQSrFy5EkuXLjX+FxsDhnItAO4+K1y+fBkBAQEDbhEznvusoc9VwNDu/VZWVti+fTtSUlJQVFSErq4uuLq64oknnsCCBQuM/8UMIFCO1po9RERERERERERERERERCZgvmlOIiIiIiIiIiIiIiIiIjAhRkRERERERERERERERGaOCTEiIiIiIiIiIiIiIiIya0yIERERERERERERERERkVljQoyIiIiIiIiIiIiIiIjMGhNiREREREREREREREREZNaYECMiIiIiIiIiIiIiIiKzxoQYERERERERERERERERmTUmxIiIiIiIiIiIiIiIiMisMSFGREREREREREREREREZu3/A8ADvKjxTkPJAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 2160x453.6 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# generate parameter selection panels for each KNeighborsClassifier parameter\n",
    "mlmachine_titanic_machine.model_param_plot(\n",
    "    bayes_optim_summary=bayes_optim_summary,\n",
    "    estimator_class=\"KNeighborsClassifier\",\n",
    "    estimator_parameter_space=estimator_parameter_space,\n",
    "    n_iter=200,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "<br><br>\n",
    "The built-in method `model_param_plot()` cycles through of the estimator's parameters and presents the appropriate panel given each parameter's type. Let's look at a numeric parameter and categorical parameter separately.\n",
    "<br><br>\n",
    "\n",
    "First, we'll review the panel for the numeric parameter `n_neighbors`:\n",
    "<br><br>\n",
    "\n",
    "<br><br>\n",
    "![alt text](images/p5_param_panel.jpeg \"EDA Panel\")\n",
    "<br><br>\n",
    "\n",
    "On the left, we can see two overlapping kernel density plots summarizing the actual parameter selections and the theoretical parameter distribution. The purple line corresponds to the theoretical distribution, and, as expected, this curve is smooth and evenly distributed. The teal line corresponds to the actual parameter selections, and it's clearly evident that hyperopt prefers values between 5 and 10. \n",
    "<br><br>\n",
    "\n",
    "On the right, the scatter plot visualizes the `n_neighbors` value selections over the iterations. There is a slight downward slope to the line of best fit, as the Bayesian optimization process hones in on values around 7.\n",
    "<br><br>\n",
    "\n",
    "Next, we'll review the panel for the categorical parameter `algorithm`:\n",
    "<br><br>\n",
    "\n",
    "<br><br>\n",
    "![alt text](images/p5_param_panel_2.jpeg \"EDA Panel\")\n",
    "<br><br>\n",
    "\n",
    "On the left, we see a bar chart displaying the counts of parameter selections, faceted by actual parameter selections and selections from the theoretical distribution . The purple bars, representing selections from the theoretical distribution, are more even than the teal bars, representing the actual selection. \n",
    "<br><br>\n",
    "\n",
    "On the right, the scatter plot again visualizes the algorithm value selection over the iterations. There is a clear decrease in selection of \"ball_tree\" and \"auto\" in favor of \"kd_tree\" and \"brute\" over the the iterations.\n",
    "<br><br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "# Model-Reinstantiation\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id = 'Model-Reinstantiation'></a>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "## Top Model Identification\n",
    "---\n",
    "<br><br>\n",
    "Our `Machine()` object has a built-in method called `top_bayes_optim_models()`, which identifies the best model for each estimator type based on the results in our Bayesian optimization log.\n",
    "<br><br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id = 'Top-Model-Identification'></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-03T05:12:37.861876Z",
     "start_time": "2020-04-03T05:12:37.844230Z"
    }
   },
   "outputs": [],
   "source": [
    "# identify top performing model for each estimator\n",
    "top_models = mlmachine_titanic_machine.top_bayes_optim_models(\n",
    "    bayes_optim_summary=bayes_optim_summary,\n",
    "    num_models=1,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "<br><br>\n",
    "With this method, we can identify the top N models for each estimator based on mean cross-validation score. In this experiment, `top_bayes_optim_models()` returns the dictionary below, which tells us that `LogisticRegression()` identified its top model on iteration 30, `XGBClassifier()` on iteration 61, `RandomForestClassifier()` on iteration 46, and `KNeighborsClassifier()` on iteration 109.\n",
    "<br><br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-03T05:12:37.868091Z",
     "start_time": "2020-04-03T05:12:37.863877Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'LogisticRegression': [30],\n",
       " 'XGBClassifier': [61],\n",
       " 'RandomForestClassifier': [46],\n",
       " 'KNeighborsClassifier': [109]}"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top_models"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "## Putting the Models to Use\n",
    "---\n",
    "<br><br>\n",
    "To reinstantiate a model, we leverage our `Machine()` object's built-in method `BayesOptimClassifierBuilder()`. To use this method, we pass in our results log, specify an estimator class and iteration number. This will instantiate a model object with the parameters stored on that record of the log:\n",
    "<br><br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id = 'Putting-the-Models-to-Use'></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-03T05:12:37.958563Z",
     "start_time": "2020-04-03T05:12:37.870251Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RandomForestClassifier(bootstrap=False, ccp_alpha=0.0, class_weight=None,\n",
      "                       criterion='gini', max_depth=19, max_features='auto',\n",
      "                       max_leaf_nodes=None, max_samples=None,\n",
      "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
      "                       min_samples_leaf=4, min_samples_split=7,\n",
      "                       min_weight_fraction_leaf=0.0, n_estimators=4019,\n",
      "                       n_jobs=4, oob_score=False, random_state=0, verbose=0,\n",
      "                       warm_start=False)\n"
     ]
    }
   ],
   "source": [
    "# reinstantiate top performing RandomForestClassifier\n",
    "model = mlmachine_titanic_machine.BayesOptimClassifierBuilder(\n",
    "    bayes_optim_summary=bayes_optim_summary,\n",
    "    estimator_class=\"RandomForestClassifier\",\n",
    "    model_iter=46\n",
    ")\n",
    "print(model.custom_model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "<br><br>\n",
    "The models instantiated with `BayesOptimClassifierBuilder()` use `.fit()` and `.predict()` in a way that should feel quite familiar.\n",
    "<br><br>\n",
    "\n",
    "Let's finish this article with a very basic model performance evaluation. We will fit this `RandomForestClassifier()` on the training data and labels, generate predictions with the training data, and evaluate the model's performance by comparing these predictions to the ground-truth:\n",
    "<br><br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-03T05:12:44.004493Z",
     "start_time": "2020-04-03T05:12:37.960766Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RandomForestClassifier, iter = 46 \n",
      "Training accuracy: 94.16%\n"
     ]
    }
   ],
   "source": [
    "# fit the model\n",
    "model.fit(mlmachine_titanic_machine.training_features, mlmachine_titanic_machine.training_target)\n",
    "\n",
    "# generate predictions\n",
    "y_pred_train = model.predict(mlmachine_titanic_machine.training_features)\n",
    "\n",
    "# summarize results\n",
    "training_accuracy = sum(y_pred_train == mlmachine_titanic_machine.training_target) / len(y_pred_train)\n",
    "print(\"RandomForestClassifier, iter = 46 \\nTraining accuracy: {:.2%}\".format(training_accuracy))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "<br><br>\n",
    "Star the [GitHub repository](https://github.com/petersontylerd/mlmachine), and stay tuned for additional notebooks.\n",
    "<br><br>"
   ]
  }
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