hyperparameterization.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Perform Bayesian optimization of CrabNet hyperparameters using Ax"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### Created January 8, 2022"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Description"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We use [(my fork of) CrabNet](https://github.com/sgbaird/CrabNet) to adjust various hyperparameters for the experimental band gap matbench task (`matbench_expt_gap`). We chose this task because `CrabNet` is currently (2021-01-08) listed at the top of this leaderboard (with `MODNet` just marginally worse) which is likely related to it being a composition-only dataset (`CrabNet` is a composition-only model).\n",
    "\n",
    "The question we're asking in this additional `matbench` submission is:\n",
    "**When a model whose defaults already produce state-of-the-art property prediction performance, to what extent can it benefit from hyperparameter optimization (i.e. tuning parameters such as Neural Network dimensions, learning rates, etc.)?**\n",
    "\n",
    "Eventually, I plan to incorporate this into (my fork of) `CrabNet`, but for now this can serve as an illustrative example of hyperparameter optimization using Bayesian adaptive design and could certainly be adapted to other models, especially expensive-to-train models (e.g. neural networks) that have not undergone much by way of parameter tuning.\n",
    "\n",
    "[facebook/Ax](https://github.com/facebook/Ax) is used as the backend for performing Bayesian adaptive design.\n",
    "\n",
    "For additional files related to this `matbench` submission, see the [crabnet-hyperparameter](https://github.com/sparks-baird/crabnet-hyperparameter) repository."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Benchmark name\n",
    "Matbench v0.1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Package versions\n",
    "- ax_platform==0.2.3\n",
    "- crabnet==1.2.1\n",
    "- scikit_learn==1.0.2\n",
    "- matbench==0.5\n",
    "- kaleido==0.2.1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Algorithm description\n",
    "Use Ax Bayesian adaptive design to simultaneously optimize 23 hyperparameters of CrabNet. `100` sequential design iterations were used, and parameters were chosen based on a combination of intuition and algorithm/data constraints (e.g. elemental featurizers which were missing elements contained in the dataset were removed). The first `46` iterations (`23*2` parameters) were based on SOBOL sampling to create a rough initial model, while the remaining `56` iterations were Bayesian adaptive design iterations. For the inner loops (where hyperparameter optimization is performed), the average MAE across each of the *five inner folds* was used as Ax's objective to minimize. The best parameter set was then trained on all the inner fold data and used to predict on the test set (unknown during hyperparameter optimization). This is nested cross-validation (CV), and is computationally expensive. See [automatminer: running a benchmark](https://hackingmaterials.lbl.gov/automatminer/advanced.html#running-a-benchmark) for more information on nested CV."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pprint\n",
    "from os.path import join\n",
    "from pathlib import Path\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "import plotly.graph_objects as go\n",
    "\n",
    "import gc\n",
    "import torch\n",
    "\n",
    "from ax.storage.json_store.save import save_experiment\n",
    "from ax.plot.trace import optimization_trace_single_method\n",
    "from ax.service.managed_loop import optimize\n",
    "import crabnet\n",
    "from crabnet.train_crabnet import get_model\n",
    "from sklearn.metrics import mean_absolute_error\n",
    "from sklearn.model_selection import KFold\n",
    "\n",
    "from matbench.bench import MatbenchBenchmark"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Setup"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`dummy` lets you swap between a fast run and a more comprehensive run. The more comprehensive run was used for this matbench submission."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "dummy = False\n",
    "if dummy:\n",
    "    n_splits = 2\n",
    "    total_trials = 2\n",
    "else:\n",
    "    n_splits = 5\n",
    "    total_trials = 100"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Specify directories where you want to save things and make sure they exist."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create dir https://stackoverflow.com/a/273227/13697228\n",
    "experiment_dir = \"experiments\"\n",
    "figure_dir = \"figures\"\n",
    "Path(experiment_dir).mkdir(parents=True, exist_ok=True)\n",
    "Path(figure_dir).mkdir(parents=True, exist_ok=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Helper Functions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### matplotlibify"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following code makes a Plotly figure look more like a matplotlib figure to make it easier to include in a manuscript."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def matplotlibify(fig, size=24, width_inches=3.5, height_inches=3.5, dpi=142):\n",
    "    # make it look more like matplotlib\n",
    "    # modified from: https://medium.com/swlh/formatting-a-plotly-figure-with-matplotlib-style-fa56ddd97539)\n",
    "    font_dict = dict(family=\"Arial\", size=size, color=\"black\")\n",
    "\n",
    "    fig.update_layout(\n",
    "        font=font_dict,\n",
    "        plot_bgcolor=\"white\",\n",
    "        width=width_inches * dpi,\n",
    "        height=height_inches * dpi,\n",
    "        margin=dict(r=40, t=20, b=10),\n",
    "    )\n",
    "\n",
    "    fig.update_yaxes(\n",
    "        showline=True,  # add line at x=0\n",
    "        linecolor=\"black\",  # line color\n",
    "        linewidth=2.4,  # line size\n",
    "        ticks=\"inside\",  # ticks outside axis\n",
    "        tickfont=font_dict,  # tick label font\n",
    "        mirror=\"allticks\",  # add ticks to top/right axes\n",
    "        tickwidth=2.4,  # tick width\n",
    "        tickcolor=\"black\",  # tick color\n",
    "    )\n",
    "\n",
    "    fig.update_xaxes(\n",
    "        showline=True,\n",
    "        showticklabels=True,\n",
    "        linecolor=\"black\",\n",
    "        linewidth=2.4,\n",
    "        ticks=\"inside\",\n",
    "        tickfont=font_dict,\n",
    "        mirror=\"allticks\",\n",
    "        tickwidth=2.4,\n",
    "        tickcolor=\"black\",\n",
    "    )\n",
    "    fig.update(layout_coloraxis_showscale=False)\n",
    "\n",
    "    width_default_px = fig.layout.width\n",
    "    targ_dpi = 300\n",
    "    scale = width_inches / (width_default_px / dpi) * (targ_dpi / dpi)\n",
    "\n",
    "    return fig, scale"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### correct_parameterization"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following function is very important for interfacing Ax with CrabNet. The information in `parameterization` (a Python `dict`) as an input and `parameterization` as an output (please excuse the faux pas) is essentially the same, but Ax needs a representation for the parameter space and CrabNet has a certain API for the parameters. `correct_parameterization` just converts from the Ax representation of CrabNet parameters to the CrabNet API for parameters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def correct_parameterization(parameterization):\n",
    "    pprint.pprint(parameterization)\n",
    "\n",
    "    parameterization[\"out_hidden\"] = [\n",
    "        parameterization.get(\"out_hidden4\") * 8,\n",
    "        parameterization.get(\"out_hidden4\") * 4,\n",
    "        parameterization.get(\"out_hidden4\") * 2,\n",
    "        parameterization.get(\"out_hidden4\"),\n",
    "    ]\n",
    "    parameterization.pop(\"out_hidden4\")\n",
    "\n",
    "    parameterization[\"betas\"] = (\n",
    "        parameterization.get(\"betas1\"),\n",
    "        parameterization.get(\"betas2\"),\n",
    "    )\n",
    "    parameterization.pop(\"betas1\")\n",
    "    parameterization.pop(\"betas2\")\n",
    "\n",
    "    d_model = parameterization[\"d_model\"]\n",
    "\n",
    "    # make heads even (unless it's 1) (because d_model must be even)\n",
    "    heads = parameterization[\"heads\"]\n",
    "    if np.mod(heads, 2) != 0:\n",
    "        heads = heads + 1\n",
    "    parameterization[\"heads\"] = heads\n",
    "\n",
    "    # NOTE: d_model must be divisible by heads\n",
    "    d_model = parameterization[\"heads\"] * round(d_model / parameterization[\"heads\"])\n",
    "\n",
    "    parameterization[\"d_model\"] = d_model\n",
    "\n",
    "    parameterization[\"pos_scaler_log\"] = (\n",
    "        1 - parameterization[\"emb_scaler\"] - parameterization[\"pos_scaler\"]\n",
    "    )\n",
    "\n",
    "    parameterization[\"epochs\"] = parameterization[\"epochs_step\"] * 4\n",
    "\n",
    "    return parameterization"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Hyperparameter Optimization (and matbench recording)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that `crabnet_mae` is defined inside of the loop so that `train_val_df` object is updated for each outer fold. This is due to a limitation of Ax where `crabnet_mae` (the objective function) can *only* take parameterization as an input (no additional parameters, kwargs, etc.). This was my workaround. Note that for the inner loop where Ax optimizes the CrabNet parameters for each outer fold, five inner folds are used, and the objective that Ax optimizes is the average MAE across the five inner folds. Additionally, hyperparameter optimization plots are produced for each of the five outer folds (Ax objective vs. iteration) using `100` sequential iterations. In other words, for a single `matbench` task (this notebook), `CrabNet` undergoes model instantiation and fitting `5*5*100 --> 2500` times. This took a few days to run on a single machine."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "mb = MatbenchBenchmark(autoload=False, subset=[\"matbench_expt_gap\"])\n",
    "kf = KFold(n_splits=n_splits, shuffle=True, random_state=18012019)\n",
    "\n",
    "task = list(mb.tasks)[0]\n",
    "task.load()\n",
    "for i, fold in enumerate(task.folds):\n",
    "    train_inputs, train_outputs = task.get_train_and_val_data(fold)\n",
    "\n",
    "    # TODO: treat train_val_df as Ax fixed_parameter\n",
    "    train_val_df = pd.DataFrame(\n",
    "        {\"formula\": train_inputs.values, \"target\": train_outputs.values}\n",
    "    )\n",
    "    if dummy:\n",
    "        train_val_df = train_val_df[:100]\n",
    "\n",
    "    def crabnet_mae(parameterization):\n",
    "        \"\"\"Compute the mean absolute error of a CrabNet model.\n",
    "        \n",
    "        Assumes that `train_df` and `val_df` are predefined.\n",
    "\n",
    "        Parameters\n",
    "        ----------\n",
    "        parameterization : dict\n",
    "            Dictionary of the parameters passed to `get_model()` after some slight\n",
    "            modification. \n",
    "\n",
    "        Returns\n",
    "        -------\n",
    "        results: dict\n",
    "            Dictionary of `{\"rmse\": rmse}` where `rmse` is the root-mean-square error of the\n",
    "            CrabNet model.\n",
    "        \"\"\"\n",
    "        parameterization = correct_parameterization(parameterization)\n",
    "\n",
    "        mae = 0.0\n",
    "        for train_index, val_index in kf.split(train_val_df):\n",
    "            train_df, val_df = (\n",
    "                train_val_df.loc[train_index],\n",
    "                train_val_df.loc[val_index],\n",
    "            )\n",
    "            crabnet_model = get_model(\n",
    "                mat_prop=\"expt_gap\",\n",
    "                train_df=train_df,\n",
    "                learningcurve=False,\n",
    "                force_cpu=False,\n",
    "                **parameterization\n",
    "            )\n",
    "            val_true, val_pred, val_formulas, val_sigma = crabnet_model.predict(val_df)\n",
    "            # rmse = mean_squared_error(val_true, val_pred, squared=False)\n",
    "            mae = mae + mean_absolute_error(val_true, val_pred)\n",
    "\n",
    "            # deallocate CUDA memory https://discuss.pytorch.org/t/how-can-we-release-gpu-memory-cache/14530/28\n",
    "            del crabnet_model\n",
    "            gc.collect()\n",
    "            torch.cuda.empty_cache()\n",
    "        mae = mae / n_splits\n",
    "        results = {\"mae\": mae}\n",
    "        return results\n",
    "\n",
    "    best_parameters, values, experiment, model = optimize(\n",
    "        parameters=[\n",
    "            {\"name\": \"batch_size\", \"type\": \"range\", \"bounds\": [32, 256]},\n",
    "            {\"name\": \"fudge\", \"type\": \"range\", \"bounds\": [0.0, 0.1]},\n",
    "            {\"name\": \"d_model\", \"type\": \"range\", \"bounds\": [100, 1024]},\n",
    "            {\"name\": \"N\", \"type\": \"range\", \"bounds\": [1, 10]},\n",
    "            {\"name\": \"heads\", \"type\": \"range\", \"bounds\": [1, 10]},\n",
    "            {\"name\": \"out_hidden4\", \"type\": \"range\", \"bounds\": [32, 512]},\n",
    "            {\"name\": \"emb_scaler\", \"type\": \"range\", \"bounds\": [0.0, 1.0]},\n",
    "            {\"name\": \"pos_scaler\", \"type\": \"range\", \"bounds\": [0.0, 1.0]},\n",
    "            {\"name\": \"bias\", \"type\": \"choice\", \"values\": [False, True]},\n",
    "            {\"name\": \"dim_feedforward\", \"type\": \"range\", \"bounds\": [1024, 4096],},\n",
    "            {\"name\": \"dropout\", \"type\": \"range\", \"bounds\": [0.0, 1.0]},\n",
    "            # jarvis and oliynyk don't have enough elements\n",
    "            # ptable contains str, which isn't a handled case\n",
    "            {\n",
    "                \"name\": \"elem_prop\",\n",
    "                \"type\": \"choice\",\n",
    "                \"values\": [\n",
    "                    \"mat2vec\",\n",
    "                    \"magpie\",\n",
    "                    \"onehot\",\n",
    "                ],  # \"jarvis\", \"oliynyk\", \"ptable\"\n",
    "            },\n",
    "            {\"name\": \"epochs_step\", \"type\": \"range\", \"bounds\": [5, 20]},\n",
    "            {\"name\": \"pe_resolution\", \"type\": \"range\", \"bounds\": [2500, 10000]},\n",
    "            {\"name\": \"ple_resolution\", \"type\": \"range\", \"bounds\": [2500, 10000],},\n",
    "            {\n",
    "                \"name\": \"criterion\",\n",
    "                \"type\": \"choice\",\n",
    "                \"values\": [\"RobustL1\", \"RobustL2\"],\n",
    "            },\n",
    "            {\"name\": \"lr\", \"type\": \"range\", \"bounds\": [0.0001, 0.006]},\n",
    "            {\"name\": \"betas1\", \"type\": \"range\", \"bounds\": [0.5, 0.9999]},\n",
    "            {\"name\": \"betas2\", \"type\": \"range\", \"bounds\": [0.5, 0.9999]},\n",
    "            {\"name\": \"eps\", \"type\": \"range\", \"bounds\": [0.0000001, 0.0001]},\n",
    "            {\"name\": \"weight_decay\", \"type\": \"range\", \"bounds\": [0.0, 1.0]},\n",
    "            # {\"name\": \"adam\", \"type\": \"choice\", \"values\": [False, True]}, # issues with onehot\n",
    "            # {\"name\": \"min_trust\", \"type\": \"range\", \"bounds\": [0.0, 1.0]}, #issues with onehot\n",
    "            {\"name\": \"alpha\", \"type\": \"range\", \"bounds\": [0.0, 1.0]},\n",
    "            {\"name\": \"k\", \"type\": \"range\", \"bounds\": [2, 10]},\n",
    "        ],\n",
    "        experiment_name=\"crabnet-hyperparameter\",\n",
    "        evaluation_function=crabnet_mae,\n",
    "        objective_name=\"mae\",\n",
    "        minimize=True,\n",
    "        parameter_constraints=[\"betas1 <= betas2\", \"emb_scaler + pos_scaler <= 1\"],\n",
    "        total_trials=total_trials,\n",
    "    )\n",
    "    print(best_parameters)\n",
    "    print(values)\n",
    "\n",
    "    experiment_fpath = join(experiment_dir, \"experiment\" + str(i) + \".json\")\n",
    "    save_experiment(experiment, experiment_fpath)\n",
    "\n",
    "    # TODO: save plot, save experiment\n",
    "\n",
    "    test_inputs, test_outputs = task.get_test_data(fold, include_target=True)\n",
    "\n",
    "    test_df = pd.DataFrame({\"formula\": test_inputs, \"target\": test_outputs})\n",
    "\n",
    "    default_model = get_model(\n",
    "        mat_prop=\"expt_gap\",\n",
    "        train_df=train_val_df,\n",
    "        learningcurve=False,\n",
    "        force_cpu=False,\n",
    "    )\n",
    "\n",
    "    default_true, default_pred, default_formulas, default_sigma = default_model.predict(\n",
    "        test_df\n",
    "    )\n",
    "    # rmse = mean_squared_error(val_true, val_pred, squared=False)\n",
    "    default_mae = mean_absolute_error(default_true, default_pred)\n",
    "\n",
    "    # deallocate CUDA memory https://discuss.pytorch.org/t/how-can-we-release-gpu-memory-cache/14530/28\n",
    "    del default_model\n",
    "    gc.collect()\n",
    "    torch.cuda.empty_cache()\n",
    "\n",
    "    best_parameterization = correct_parameterization(best_parameters)\n",
    "    test_model = get_model(\n",
    "        mat_prop=\"expt_gap\",\n",
    "        train_df=train_val_df,\n",
    "        learningcurve=False,\n",
    "        force_cpu=False,\n",
    "        **best_parameterization\n",
    "    )\n",
    "    # TODO: update CrabNet predict function to allow for no target specified\n",
    "    test_true, test_pred, test_formulas, test_sigma = test_model.predict(test_df)\n",
    "    # rmse = mean_squared_error(val_true, val_pred, squared=False)\n",
    "    test_mae = mean_absolute_error(test_true, test_pred)\n",
    "\n",
    "    # deallocate CUDA memory https://discuss.pytorch.org/t/how-can-we-release-gpu-memory-cache/14530/28\n",
    "    del test_model\n",
    "    gc.collect()\n",
    "    torch.cuda.empty_cache()\n",
    "\n",
    "    trials = experiment.trials.values()\n",
    "\n",
    "    best_objectives = np.array([[trial.objective_mean for trial in trials]])\n",
    "\n",
    "    parameter_strs = [\n",
    "        pprint.pformat(trial.arm.parameters).replace(\"\\n\", \"<br>\") for trial in trials\n",
    "    ]\n",
    "\n",
    "    best_objective_plot = optimization_trace_single_method(\n",
    "        y=best_objectives,\n",
    "        optimization_direction=\"minimize\",\n",
    "        ylabel=\"MAE (eV)\",\n",
    "        hover_labels=parameter_strs,\n",
    "        plot_trial_points=True,\n",
    "    )\n",
    "\n",
    "    figure_fpath = join(figure_dir, \"best_objective_plot_\" + str(i))\n",
    "\n",
    "    data = best_objective_plot[0][\"data\"]\n",
    "\n",
    "    data.append(\n",
    "        go.Scatter(\n",
    "            x=(1, total_trials),\n",
    "            y=(default_mae, default_mae),\n",
    "            mode=\"lines\",\n",
    "            line={\"dash\": \"dash\"},\n",
    "            name=\"default MAE\",\n",
    "            yaxis=\"y1\",\n",
    "        )\n",
    "    )\n",
    "\n",
    "    data.append(\n",
    "        go.Scatter(\n",
    "            x=(1, total_trials),\n",
    "            y=(test_mae, test_mae),\n",
    "            mode=\"lines\",\n",
    "            line={\"dash\": \"dash\"},\n",
    "            name=\"best model test MAE\",\n",
    "            yaxis=\"y1\",\n",
    "        )\n",
    "    )\n",
    "\n",
    "    layout = best_objective_plot[0][\"layout\"]\n",
    "\n",
    "    fig = go.Figure({\"data\": data, \"layout\": layout})\n",
    "\n",
    "    fig.show()\n",
    "    fig.write_html(figure_fpath + \".html\")\n",
    "    fig.to_json(figure_fpath + \".json\")\n",
    "    fig.update_layout(\n",
    "        legend=dict(\n",
    "            font=dict(size=16),\n",
    "            yanchor=\"top\",\n",
    "            y=0.99,\n",
    "            xanchor=\"right\",\n",
    "            x=0.99,\n",
    "            bgcolor=\"rgba(0,0,0,0)\",\n",
    "        )\n",
    "    )\n",
    "    fig, scale = matplotlibify(fig)\n",
    "    fig.write_image(figure_fpath + \".png\")\n",
    "\n",
    "    task.record(fold, test_pred, params=best_parameterization)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Export matbench file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "my_metadata = {\"algorithm_version\": crabnet.__version__}\n",
    "mb.add_metadata(my_metadata)\n",
    "mb.to_file(\"expt_gap_benchmark.json.gz\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Static Plots"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For convenience (and so you don't have to run this notebook over the course of a few days), objective vs. iteration static plots are given for each of the five outer folds. If you'd like a more interactive experience, open the corresponding HTML files in your browser of choice from [`crabnet-hyperparameter/figures`](https://github.com/sparks-baird/crabnet-hyperparameter/tree/main/figures). Note that the first 46 (`2*23 parameters`) iterations are [SOBOL iterations](https://en.wikipedia.org/wiki/Sobol_sequence) (quasi-random sequence), and the remaining 54 are actual Bayesian optimization iterations.\n",
    "\n",
    "In the last four out of five folds, the hyperparameter optimization results in a slightly better test MAE (i.e. MAE for data which was unknown during the hyperparameter optimization)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Objective vs. iteration for outer fold 0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The best model test MAE is somewhat worse that the default test MAE, and both are close to the best validation MAE."
   ]
  },
  {
   "attachments": {
    "best_objective_plot_0.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![best_objective_plot_0.png](attachment:best_objective_plot_0.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Objective vs. iteration for outer fold 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The best model test MAE is slightly better than the default test MAE."
   ]
  },
  {
   "attachments": {
    "best_objective_plot_1.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![best_objective_plot_1.png](attachment:best_objective_plot_1.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Objective vs. iteration for outer fold 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The best model test MAE is somewhat better than the default test MAE."
   ]
  },
  {
   "attachments": {
    "best_objective_plot_2.png": {
     "image/png": 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uV199tfqvVVq9erUMGzZMZsyYIQMGDIgZV/C54IILpFu3bqLrcc8998T0HjGrbIiCSkpKZOzYser9et5558ncuXMlPz9+G0pRxONhZZfcgyLuEkOwGklNIF4iHov72BFxJ4y1ZMkSGTNmjLz++utKxL2cINovv/xy3MVbM6OIe7n3RFh3iniEwJjdEwR2FZfKocOVca9rq7wsyW6QVue+sRBXO42JxX0o4nZIh85DEQ/NJ6W6urq6/phZAofT2QeSlcCcLzfKzwX74968K/p1kG5tGgcV8caNG8vDDz+sfh81alSd4XU9TL18+XKV57777hMMJ+tk/l0P1TZs2LB2+BZ5TzjhhJAeLURmxIgRteXOmTOnduhcizjKXrBggfqDZMxjNZweru4ow5xHM5g3b16d+px//vmqTrr9Vl46vHd8uOjhajt1MBsH10+cONGSBf5R89Acwg2PBysPdR04cGDtfcw2Qhtg9+LiYjUMb7Z9JJ2ZnngktDyel564xw3I6lsSePu7zbJjX1nc6Zzbq7Uc3TLHUsQhFPrFrOdMkVHPk2sBuvPOO5V4mfNs2rSpzjy02fO244kjz1tvvVUr8ub7GkVr8eLFaj7anMcs4uHqjjgALWK6TLTdWN/vvvsuYDjdOCfevXt3y3l6XI8EobdTBysBN8YqBGvXkCFDaj8mcE+0JdQ8t9kTt5oqMJejP6yMH0vRdmCKeLTkPHgdRdyDRmOVwxLYWlQiB8sqwuaLdYZ2TbOlUWa6pYibX/xmwbASYGMeFBpuzjiciAcLSDOKDu6DjwiIt3EUwJhn9+7dAR8U4eqOsoyCa8XdLHTmwDbzPcL9bvT8rQLw9EdSu3btAtpprJtVu+xMN5hF3Krt5vbGcgieIh7rJ9vF5VHEXWwcVi1qAm4Uce0x6kZpETn99NNFDx2bI8Kt8mBYN5i3Fk7EgwWPGcVde73mqHDkue222+SJJ55QTdDR6cG8ZDvtMxo4nIiHEj394RGKn1WkvfZ+rYbIQ4l8OM6hBNk43G4cUsc1n3/+eUxWMFDEo351eO9Cirj3bMYahyfgVRHX867mFmrRNi5d0nmCDU9bLWUzzyHrMmIh4qHqbpzfDrYkLZyIGz3gSy+9VMUAaC/aPG8djF+wEQDjXLWep0de3AMfWeYPgEhF3DgfroUboxnGkRWKePjnmjksCFDE2S2SkYAXRNwoSsE88VC20YK+dOnS2vntcOISyhPXXnbz5s1rA8qMgmu81o4nbqy7nSHocCKO8nT7brrpJrn22mtr16nbKd9OP9dii9gFrLU3figYrw83NWD0xHXQoXnY3mpkgZ64HSsxTwABijg7RDISKNhfJmXlVXFvWvPcTMlqkFrnvuHmjE888URLwQi34YmdeWFjZSKZEzcPTQebE7dT92B5IhlOR14tfOeee66sWLGiNrgs2NB3OH5WHcToEf/tb3+rs0mPnQ8GqxgD8/SEOcCQnnjcH9fkuCFFPDnsyFa4m4CeB40kOt3oeSKC3Ry9jd+tAqjC7QwXj+h0c92DRacb6//TTz8FjU7XIwLGYfNgy+90dL9VHYy9xEr4zQKtP3rqE52uPXHcW69EMA6v6+kQiri7n2HX1o4i7lrTsGJJREAPv6JJek2y1TrgYOuo9fx2uLXGxuuNc+VmlOZ14sa8Wshuvvlmefrpp2vXiRvz2FknbmcdvDGgzDjfDzajR4+2HNbXH0RW7QvHz8zBKsbAbBfzfLtVu6z4Gndss1prDs8cc+L6o4MinkQPfKRNKSsrk/nz58usWbPUcFODBg1k0KBBgofwjDPOkNTUusN7+h4U8UhpMz8JkIBTe6eTbGwIMDo9NhzjUsrBgwdlwoQJ8vjjj0tubq706dNH3XfZsmWyf/9+GT9+vPrTqFEjy/pQxONiJt6EBJKKQDLtc55UhjnSGIq4R6yK3WfhfY8cOVIGDx6sIjURAYm0bt06taZz0aJFaiN+/G6VKOIeMTarSQIuIGAe8jZuBuOC6rEKFHFv9QGsM0QEKfbbfeedd+SUU04JaMBHH30kiOLE3NIjjzwiWVlZdRpIEfeWzVlbEiABEghHgJ54OEIu+X3Dhg0yadIkVRvsopSXlxdQMx0Eg52Vnn32WcHhC+ZEEXeJMVkNEiABEogRAYp4jEAmshjjUDuGvCD2aWl1j0ikiCfSSrw3CZAACcSeAEU89kzjWmJRUZG89NJLcv/990vXrl3lxRdflB49eljWgSIeV9PwZiRAAiTgOAGKuOOInbmBeY3kBRdcII899pgS8mDJKOJ6aN6Y995773WmsiyVBEiABEig3gSs3tGTJ09W5eL9jsBnt6WUaowVM9UhgB2d/vKXv0hVVZWsWbNGNm7cKP369VPz5f379w/riVtlIGp2NBIgARJwLwHtdVvVkCLuXruFrRnWj2O3Jew+1LNnT7XMzMojN3riFPGwWJmBBEiABFxFgCLuKnPEtjLYyW3cuHHKE3/00UfVAQvmxDnx2DJnaSRAAiSQaAKcE0+0BWJ4f71HMgR82rRpkpGREVA6RTyGsFkUCZAACbiAAEXcBUawU4UPPvhA/vrXv6pD6zFsbrWEbPbs2XLNNdeoJWb4Yx56oYjbIc08JEACJOAdAhRxj9gK5+ledtll0qxZM3n11Velc+fOATUvLi6WW2+9VSDkb731llx66aUcTveIbVlNEiABEoiWAEU8WnJxvg5z3jj8BFuq3njjjfLAAw9IixYtVC0KCgrkoYceEhxqf8kll6ilBvo3YzXpicfZaLwdCZAACThMgCLuMOBYFr9z507lbb/55puWp5hh7/Snnnoq6FpxingsrcGySIAESCDxBCjiibdBRDXAcrJ3331XLSPDfulIAwYMUIej4PSyYMeQIh9FPCLUzEwCJEACridAEXe9iWJXQYp47FiyJBIgARJwAwGKuBusEKc6UMTjBJq3IQESIIE4EaCIxwm0G25DEXeDFVgHEiABEogdAYp47Fi6viSKuOtNxAqSAAmQQEQEKOIR4fJ2Zoq4t+3H2pMACZCAmQBF3Ed9giLuI2Ozqa4igJUkY8aMkddff126detmq24lJSXqDAQ8t+edd55gW+X8/Hxb14bKhHI+//xzmT59umRnZ8uePXtk5syZcscdd6j/Nyf8Pnz4cPXPweqg64o8ulxdjrEdixcvVqtpjMn4e7B6W11XbxBJUgBFPEkMaacZFHE7lJiHBGJPIBoRh2BiKWmsxFu3yiziU6ZMka1bt9YRX51fi3iTJk0Ef8wircV9xIgRMmrUqDq/r169WnAOdl5enioymMi3a9dO7rnnntjDT/ISKeJJbmBj8yjiPjI2m+oqAskg4r1795bly5erkxKNowkQeewmqZNZpPVHwpAhQ+TOO++sMxqhPXGKeHRdliIeHTdPXkUR96TZWGkPEoBoDxw4UNX8hBNOkNGjR6sha+NwOjzUYcOGKWFEwsFG2hOF8E2cOLG25XPmzFFD2uZrkME41Iz74lqj966vmTFjhhrK1p741KlTZfz48Wq4XtfTarhfe+I4j+G7775ThzDp4XVch3v++9//VmWYPXp9LTajOv/889V1+Lvxeop4/To4Rbx+/Dx1NUXcU+ZiZe0S+HmRyIHdoXMffbpITs1ZA0FTjMrRAq7FVYso7qtFUv8bPFMImtWcsnk43SzGKA95Hn744dpyIxFx7THbHU6H+CIZ59Px/7j+N7/5jRJys4ibRyCs7kURt9vRrfNRxOvHz1NXU8Q9ZS5W1i6BBVNEdv4UOve5E0Ra9wydJwblBBMks9haiZmVx2ycEzfPZaMx5mucFvGTTz5ZbrvtttohdT2UjgOZMNJgFnFzO80fOGhDuMC2WAb12e1SXspHEfeStepZV4p4PQHycncSUB50QRhP/Nc2PfH6laOHjzEsbozCNnqkzZs3DzmsrIerQwW24TcEkumkvX6nRdw8JI56rFu3Tk0DmAXbauTA6iOHnnj9HiuKeP34eepqirinzMXKepCAlXBpj1l7sFrEFyxYYNlCPf8dbDhdz6FDuFEW5tX1fLfTIo6hf4g1EpakYQkchtnxwWIWcfOHhrGxRu+aIl6/jk4Rrx8/T11NEfeUuVhZDxKojydubq5ZxO0MwcdDxPU94H3j6GP8wfp1Y/3QFgi8VcS5+UOHIl6/jk4Rrx8/T11NEfeUuVhZDxKwMyfeoUMHS4EzfwAYRbxhw4bqGnNkuPZ2jcPp5k1lzPPQ0a4T11HlWoS7d+8uvXr1Coio13PimzZtChghMJrSHMQXSvA92AXiXmWKeNyRJ+6GFPHEseed/UMgmuh00LEajjYGtuF3lK2XjxmXm5mXoOmod/1hgKF7LfT1FfFgO7AZ6/+3v/0toK5Woww6qj7YR41/ekz9WkoRrx8/T11NEfeUuVhZDxOIZp24ebcz83C6OYob688xFw7xxJy0XmNuvrfOo4PtzCJu/Biw2t7UuNbbuP2qeTc5LeJ333233HDDDQF1MpvSuMQO68/19rLBTG5cQ+/hbuFI1SnijmB1Z6EUcXfahbUiARIggWgJUMSjJefB6yjiHjQaq0wCJEACIQhQxH3UPSjiPjI2m0oCJOALAhRxX5i5ppEUcR8Zm00lARLwBQGKuC/MTBH3kZnZVBIgAR8RoIj7yNj0xH1kbDaVBEjAFwQo4r4wMz1xH5mZTSUBEvARAYq4j4xNT9xHxmZTSYAEfEGAIu4LM9MT95GZ2VQSIAEfEaCI+8jY9MR9ZGw2lQRIwBcEKOK+MDM9cR+ZmU0lARLwEQGKuI+MTU/cR8ZmU0mABHxBgCLuCzPTE/eRmdlUEiABHxGgiPvI2PTEfWRsNpUESMAXBCjivjAzPXEfmZlNJQES8BEBiriPjE1P3EfGZlNJgAR8QYAi7gsz0xP3kZnZVBIgAR8RoIj7yNj0xH1kbDaVBEjAFwQo4r4wMz1xH5mZTSUBEvARAYq4j4xNT9xHxmZTSYAEfEGAIu4LM9MT95GZ2VQSIAEfEaCI+8jY9MR9ZGw2NWEEpkyZIlu3bpXp06dLdna2Y/XYs2ePzJw5U+644w5H72O3AUuWLJExY8bI66+/Lt26dQt7WUlJiYwdO1ZOP/10GT58uGV+p9oI21xwwQVB6zl37lwZMWKEzJkzJ2jd0N6BAwfK4sWLZcCAAQH117+NGjXKsh/o34NBCnadVX6KeNiuljwZKOLJY0u2xL0E4iXi8bqPXdJOiLgTbbRTT4j4ww8/LK1atZKJEyfWEWl8XODDY8GCBZYijnrv379fPvzwQ5kxY4alyEfywRPKBhRxuz00ynwHDx6Ud999V15++WVB54Fh+/btK5dffrlcf/310qJFi4hKNgqx1YXnnXeeoAPm5+fX+ZkiHhFqZiaBqAg4ITxWFYnXfexCsCOOxrLseOJOtNFOPfEOxTu7U6dO0q5dO7nnnnsCMOD35cuXW4r06tWrZdiwYeoj4K233rK83k4d7HKniNslFUW+nTt3yq233ipvvvmm5ObmSp8+fSQzM1PWrFkjGzdulBNOOEGefvppOe2002yVfvjwYbnrrrvU8EywRBG3hZKZSMAxAlp4GjdurF7kSFbDo/plDzFAuu+++wLEwvy7frYbNmyohqHxUY6E94jVELb2Fm+++Wb1noHXqO8zevToWk8S/2YeEjbf26r+xiFh1AFlYnjfWBc9LK1hG+8TSsT1b1ZtNHrBVmzN1xrbZ65PsOFyLeKandExQvmTJk1S3jX+a/a09bX477/+9S/1MWB2rCjijj1+sSu4oqJCGXjq1Kly4403ygMPPFDrdcM7x8M9efJk+f3vfy/PPfectGzZMuzN9+3bJzfccINs27ZNXnnlFenYsWPYa4wZ6IlHhIuZSSAqAhBxDMFqUdaigsL0PLkWyTvvvFOJqTnPpk2blDdnFAizVxrOS9Vit2PHjlph1SJmFH49dKzFV4uzFjir+pvng3V70EZdjrncYG0ONSdubhXPY3AAACAASURBVKNuEwRUe8fIg/rgfvoDx+g9mwXTjoBqIX7iiSfktttuU/fS895ox/PPPy9XXXWVXHvttQE20qz0/XWbzUJvpw52Ox89cbukIswHTxtGrqyslFdffVU6d+4cUEJxcbHy0mfPnq2GZM4555ywd1i7dq1cccUV0rNnT9Vx4N1HkijikdBiXq8QmLi9TFaWVoWs7uQ2mXJcVmrIPLEqxygqelrL/DK3EmBjHlQ03JypXRE3Cp5ZSHEf431PPPFE5eWbh5Dt5DGKdvPmzdXHydVXXx0QGGb0UrXgRiLixus1Wy3suNf5559veV+j4e0IqPE+GF1A0h8N+A3p5JNPrvOhZbaz1QcQrg0X2BYqoM7ciSniDr2pvv32W/Uw9OrVSx599FHL6FH9xW7XYEuXLpVzzz1XbrnlFsG1aWlpEdWeIh4RLmb2CIFL15fI0oOVIWv7v52z5VeNQj8vsSoHz6bxpY+/G4eOgwmNVR4MgQd7P9gVcaOQasEze5ba6+/evbulCBo9TD0UbyxDC5P+8Ni9e7flRwhEDp4tPNwOHTqEjU43thH3sPrAwL8bmev3aqjI8HAfSMGGxHEvvH/vvfdeZeNwoyVmLjpq386HhN3HjyJul1SM88FDx0Pw4IMPhlzGYLwtvPZrrrlGXnzxRWnfvr1MmzZNFi5cqJZJYFgnXKAcRTzGRmRxriDw39IqKa6sDlmXXtlpkhfaEZdYlWNXxPUctbni5mFsPS+MfMY5ZSdF3CzQRhFHUK5ZvFA3o0BDxLH8yirpofxoRdzIw1i+MabAPPdt/M2OgBpFHG3RHx74O+a4MS1invIwz9UHs2swYY/2YaKIR0uuntetX79errzySsGwOgLf4LGHStXV1WoOHX/OPPNMWbVqlRLv9PR0FSVZWFgop556qhpmP+mkkyyLoojX02i8nARsELAS8UiHfM230SKK0Tg95+ykiJuHwa1GCaLxxI3tijQ6HdcG88RDmcW85jtSETcO+69bt066dOmiRivMQ+ehyjXbyk4dbHQ1lYUibpdUDPMhsG3cuHEqknP8+PFKmCHGoRKWpmEICF+BF154oRqOQmdCKioqkoceekh55vht1qxZ0rp16zrFGUX8jDPOqPP7p59+GsNWsigS8CeBcPPdweadrYa6jQTNvzsh4rGaE0e9rbx1iBfqbQxCi2RO3KrNdj8GUCd8eNgRUPPcO+6LURAkvHvhQNmJc9D2MwcC2qmD1dMDB86cPvvsM/VPeL+PHDnSdQ9dSjVc0CRKEHBErOPP4MGDFXhsKBAuFRQUCCJZETAH8e/Ro0fAJcZAOYg4htbNKdwa8yRDHQ4pfycBRwhEE52OihgF6rvvvqszp2wlLKF2hjN6/3pHtHBz4giCcyo63Vwfu+JrbKNVdLqRCziaA+oi8Zh1hzCzttqBzVguAvnw0aJXG5g7lrne0Yq49rqtOi5F3JHHObBQDHnjSxAiDI8ZQ9+RLhMLVU09bITAk0ceeUSysrICstMTj4OReQvfE9DD6QCBpWZI5jXg+Ldwa7HNEczm9eDG6622/oxWxK3qZlX/aNaJG4P07Ii4VRvNc8/mvTGs5qat7ov3oVW70H6ziFuxNIr4hg0b1LLhUFvOGlct/PTTT0FjBnD/YGv/6Ykn8PUCDxrzOW+//bYMHTpUBUZgGUcsU7j9ejknHkvaLIsESIAEEk+Ac+JxsMGXX34p2PkHAWhYG46NXyJd441qYsc2/MnJybGs9aJFiwRz3cGWVlDE42Bs3oIESIAE4kiAIu4gbMwxf/LJJ3Ldddep6HGINwIPsPVqpOm9995Tc+jYse3xxx9XOxOZkxZpDNtMmDChNmpR56OIR0qd+UmABEjA3QQo4g7aB1GDWNeNyPInn3xS7baWmhpmsWqQ+qxcuVINwyO99tpr0rt374Cc2FoRAg9vHEP2gwYNCiry+IFBbA4ankWTAAmQQJwIUMQdAo1ocgxrf/zxx/LMM8+oNeGhIgvDVcO4F7t5iRkEHEvWsPwMuwlhuZnVOcb0xMNR5u8kQAIk4C0CFHGH7GXeMSjUbYyRk6HOqcWHwe23367EWp+KhnKXLVumvH1s0ICo9GDHm1LEHTI2iyUBEiCBBBGgiDsA3s6Rocbb2hVxXFNWVibz589XG7ogGh0JazsxlH7RRReFnG+niDtgbBZJAiRAAgkkkLQiXlVVJTi6E0PNONdbJ2ysgt3M8vLyop6fTqC96nVrini98PFiEiABEnAdgaQScexa9vnnn6sF9x988IGKCA+WsMkKFs7juFBs+2c1h+w6a9WzQhTxegLk5SRAAiTgMgJJIeI4TQa7oD333HMhhTsY+2bNmsmf/vQndRIYTtZJ1kQRT1bLsl0kQAJ+JeBpEUegFwK5IOAI7EKCIGN5Fbzrnj17Stu2baVly5a19t21a5ds2bJFvv/+e+W1f/HFF7XCj2tvuukm+Z//+Z+gwWFe7igUcS9bj3UnARIggboEPCnimO+eN2+e3H333Wr/YQyN48CPIUOGyLHHHhv2RDAjBgShIbr7lVdeUVHfGILHCTWTJk1S67LDnS7mpU5FEfeStVhXEiABEghPwHMijk3z77//fnUK2PHHH6+WXF122WXSqFGj8K0NkwNz6tgoBd79Dz/8oDxy3CdZ5ssp4vXuIiyABEiABFxFwHMijnXU2AUNQ+bYwjQW4m22CI4LffbZZwU7rj3//POSn5/vKqNFWxmKeLTkeB0JkAAJuJOA50Qc66QPHDgQF2HFfXCcZ7IMqVPE3fkQslYkQAIkEC0Bz4l4tA3ldSIUcfYCEiABEkguAp4TcQS1lZeXR3USWHKZLvLWUMQjZ8YrSIAESMDNBDwn4npOvFOnTupQkVNPPTVphrud7igUcacJs3wSIAESiC8BT4r48OHDZcGCBYoUItQR4Hb55ZcHrAePL0Zv3I0i7g07sZYkQAIkYJeA50QcwWYPPvig4JSwjRs31rYTp3rhABDsuuaXbVTtGlnno4hHSoz5SYAESMDdBDwn4hon1ot/9dVX8uKLL6q13XrHNvyuN3/B0Zz4e33O8Xa3+SKrHUU8Ml7MTQIkQAJuJ+BZETeCLSoqkn/+85/yzDPPqK1UjQneOYbfL7jgAmncuLHb7eFo/SjijuJl4SRAAiQQdwJJIeKaWnV1tRpif+ONNwRndGPXNZ2wLzo8c5y7jT3VU1NT4w470TekiCfaArw/CZAACcSWQFKJuBFNRUWFOuQEc+d6T3T9O+bMR4wYIRdffLGvguEo4rF9eFgaCZAACSSaQNKKuBEstlFdtGiROu1s/vz5tT8hGO6SSy5RJ5dhqVqye+cU8UQ/brw/CZAACcSWgC9E3IgMR5G+8847ash94cKF6qfzzjtPeezJskd6sC5CEY/tw8PSSIAESCDRBHwn4gCO08oQAAfP/P/+7/8o4onuhbw/CZAACZBAVAR8I+JYkgbhfumll9SQunFJ2nXXXSfTp09P+uh1euJRPSO8iARIgARcSyCpRRzBbVhL/uqrr6o/hYWFtYbQO73hLPJWrVr5Yi05Rdy1zyErRgIkQAJREUg6EccBKatWrZLXXntNLTMz7uqGZWaDBw+W66+/3pd7rlPEo3pGeBEJkAAJuJZAUoh4qPXhII8lZYhAx8Yvft7whSLu2ueQFSMBEiCBqAh4WsR1pDk8bvNObXrrVQyXd+vWLemXj9mxPkXcDiXmIQESIAHvEPCciJeVlcl7770ns2fPDljzDeRY9z1o0CB1qtmZZ54p2dnZ3rFEHGpKEY8DZN6CBEiABOJIwHMijvPEjUeRghWPI7XXYyji9jgxFwmQAAl4hYBnRfzrr79We6FD0Pv27Svp6eleYZ6welLEE4aeNyYBEiABRwh4TsRxnviSJUvktNNO83WQWjS9gSIeDTVeQwIkQALuJeA5EY8GJTZ6KS0tlby8PF8HuFHEo+k9vIYESIAE3EsgKUUca8WXLVumtlX9+OOP1VpxvT86gt8mTZqkTi9DAFyjRo3ca50Y14wiHmOgLI4ESIAEEkwg6UQcJ5ZNmzZNHn/88YCtVbWII2L9T3/6k8yaNUvOPfdceeqpp6Rr164JNkN8bk8Rjw9n3oUESIAE4kUgqUQc26w+8sgjctdddyl+WG523HHHqRPLevfurU4qa9CggYwdO1ZeeOEFlWfo0KECcWvatGm8mCfsPhTxhKHnjUmABEjAEQJJJeL/+c9/5OKLLxYEv2Eo/aqrrpI1a9bIsGHDpHXr1rXHjWK4/a233pI777xTDbXj75deeqkjgN1UKEXcTdZgXUiABEig/gSSRsQrKytl8uTJMmXKFBk/frz6O5adrV69uo6IAxvyP/TQQyovPHMMwWdkZNSfqItLoIi72DisGgmQAAlEQSBpRBxHi44ZM0Zefvll+eyzz+TXv/61whFMxPEblqr99re/VXlxXbIPqVPEo3hCeAkJkAAJuJhA0oi43sltx44d8vrrr6v90sOJeCiBd7HNoq4aRTxqdLyQBEiABFxJIOlEfMWKFfL+++/LSSedFFbEly5dqiLU6Ym7sm+yUiRAAiRAAmEIJI2IYzOX22+/XQW0YfkYzgwP5Ynj+NLp06era0aPHq2i2rOyspK6w9ATT2rzsnEkQAI+JJA0Ig7bPf/883LDDTfIWWedJS+99JJ06NAh6Jw45s2vueYaFZ1uFP1k7gMU8WS2LttGAiTgRwJJJeI4X/zGG29UR5VimHzChAkqWA0HpeglZqmpqfLOO+/IX//6VyXwEHwEtbVr184T9sdmNu+++66qMwLzENCHA2Auv/xyNfrQokWLoO2giHvCxKwkCZAACdgmkFQijlYvWrRIbacKgQ6XOnbsKP/4xz/kjDPOCJfVFb/v3LlTbr31VnnzzTfV2el9+vSRzMxMtRYeIwonnHCCPP300+pwGKtEEXeFGVkJEiABEogZgaQTcZD56aef1EYu8+fPDwrqoosuUmvKIYReSNiNDnu+T506VY02PPDAA7VeN7zzhx9+WK2N//3vfy/PPfec2hvenCjiXrA060gCJEAC9gkkpYij+diVDR7q4sWLlVe+fPlyOeaYY6RXr17Sv39/tQ2rl84gh6eNHeiwSc2rr74qnTt3DrBycXGx8tJnz54tH374oZxzzjkUcfvPAXOSAAmQgCcJJK2Ie9IaISr97bffqp3l8BHy6KOPCg5yMSeMLEycOFHmzJkjw4cPp4gnWydge0iABEjARIAiniRdAh76PffcIw8++CBFPElsymaQAAmQQDgCnhPxoqIiNWSMoeVQkdjhGh7u94KCAnnllVfkD3/4gye2Y12/fr1ceeWVgmF1BL7BYzcnzomHszp/JwESIAFvEfCkiGPJ2Jdffil33323WlbVuHHjmFGHCGK9Oc4jxzGmXthTHYFt48aNUxvdGA9/CSXiVhH5n376acw4siASIAESIIHYEjjzzDPrFIg9T5DgpGFllttSSjW2VjOldevWyR133CFvv/22YJkYArowB9yqVSvRXyWRNARBcBs2bFDD0BDvwsJCQfQ6Ir67d+8eSVFxzwsBR8Q6/gwePFgZEhysktETt/rdAnXc28MbkgAJkAAJWBMIpW+eEnE0r6ysTJ0Dft9999WuCR80aJAS39NPP10dgJKTk2NJAmIFj3vt2rWycOFCmTdvnnz11VcqL67D8rQRI0aoNdhuTvjYwDw4PPALL7xQZsyYoT5qgiWjiGO5mjnde++9bm4u60YCJEACviZg9Y7G0mIkz4m4tiTmrlF57IMOUTMmvSGKMZK7pKREli1bpnY6M6ZmzZqp6O9Ro0Y5Otceqx6IJWeoL0Yjhg4dqtofbtc5zonHij7LIQESIAF3EPDcnHgwbDiKFOunMSSuvWo7iOG133TTTcqDj+Xcup17R5sH8QA333yzWvuOqQRs/IIPlnCJIh6OEH8nARIgAW8RSBoR19gxVI7tSeFtf/fdd/L111/LoUOHaq0Cjxt7jWOnNhxXiv+PZh49EWZG2z755BO57rrr1KgDxBuBDHaH/SniibAa70kCJEACzhFIOhF3DlXiS9Ynr2Eq4Mknn5QrrrhCcKCL3UQRt0uK+UiABEjAGwQo4t6wk2DuH/P1H3/8sTzzzDNqTXikIwgUcY8Ym9UkARIgAZsEKOI2QSU629y5c1XEvJ3EbVftUGIeEiABEvA+AYq4B2x4+PBhueuuu1QEup1EEbdDiXlIgARIwPsEKOLet6HtFnA43TYqZiQBEiABTxCgiHvCTLGpJEU8NhxZCgmQAAm4hQBF3C2WiEM9KOJxgMxbkAAJkEAcCVDE4wg70beiiCfaArw/CZAACcSWAEU8tjxdXRpF3NXmYeVIgARIIGICFPGIkXn3Aoq4d23HmpMACZCAFQGKuI/6BUXcR8ZmU0mABHxBwHMijiNIv//+eyktLZXjjjtOmjdvHrWhlixZIgMHDpTzzjtPsJlKfn5+1GV54UKKuBesxDqSAAmQgH0CnhNxnFY2fPhwWbBggSxevFgGDBhQp7VFRUUybtw42bRpk0ydOlUddGKVKOL2OwpzkgAJkAAJuI9AUoq4HaGHKSji7uuQrBEJkAAJkIB9AhRxDqfb7y3MSQIkQAIk4CoCFHGKuKs6JCtDAiRAAiRgnwBFnCJuv7cwJwmQAAmQgKsIUMQp4q7qkKwMCZAACZCAfQIUcYq4/d7CnCRAAiRAAq4iQBGniLuqQ7IyJEACJEAC9glQxCni9nsLc5IACZAACbiKAEWcIu6qDsnKkAAJkAAJ2CdAEaeI2+8tzEkCJEACJOAqAp4W8X/+85/Sr1+/OkCx7epNN90kCxculGB5cNHXX38tv/3tb7l3uqu6JCtDAiRAAiRgl4CnRdxuI8Pl4wEo4QjxdxIgARIgATcSoIiL0BN3Y89knUiABEiABMIS8JyIHzhwQObMmSP79u0L2zi7GfLy8mTEiBGSk5Nj9xJP5uNRpJ40GytNAiRAAkEJeE7EacvoCVDEo2fHK0mABEjAjQQo4iJSVVUlAKFhuNFQsagTRTwWFFkGCZAACbiHgK9FfNeuXfLGG2/I4sWLZcaMGZKfn+8eyzhQE4q4A1BZJAmQAAkkkIDvRLyiokK++uorefbZZ+Xtt9+W/fv3M7AtgR2QtyYBEiABEoiegG9EXHvdEO8ffvghgBiXmEXfgXglCZAACZBA4ggktYhbed1G1BdddJEMHz5cLrjgAmncuHHirBCnO3M4PU6geRsSIAESiBOBpBTx4uJimTt3rjz99NN1vO5TTz1VLSe77LLLpFWrVkkfzGbsRxTxOD1VvA0JkAAJxIlA0og4IsxXrVols2bNkpdfflkKCwsDEHbt2lVmz54tp5xyiqSmpsYJr7tuQxF3lz1YGxIgARKoLwHPizi87vnz58szzzwjn3/+eS2P3NxcwXD57373O+WRI9/rr78u3bp1qy8zz15PEfes6VhxEiABErAk4EkRD+V1H3/88TJy5Ei5/PLLpWXLlrJnzx41771jxw6K+N//rg6GQaquruYjQQIkQAIk4HECnhNxnFA2atQoefPNN2vRN2vWTK688kr1B3Pe6enptb9RxH/pofTEPf60svokQAIkYCLgORHXorxgwQIZOnSo3HDDDTJgwABp1KiRpXEp4hRxPvUkQAIkkKwEPC3iMMqgQYNkyJAhav67ffv2daLNKeIU8WR9eNkuEiABEvCciGPt98KFC+Wpp55SAW3GBEG/7rrrlKDrdd8UcYo4H3MSIAESSFYCnhNxbQgEZm3ZskXmzZsnzz//fMB6cMyRX3311XL99der4LZrrrmGgW0iwjnxZH2M2S4SIAG/EvCsiBsNpndmg5i/++67AWvEjzrqKDl48KDgv1xixuh0vz7obDcJkEByEkgKETeaJti6cT1/Dq/8nHPO8d1ubWg/PfHkfIjZKhIgAf8SSDoRNw63b9y4Ue3eBg8dfzfPn0PQcfgJhtzjlVasWCFXXHGFTJgwQa1fjzQZhdjq2lCHuVDEI6XN/OEIlJZUSGlpuaSnp0pObma47PydBEggxgSSVsSNnEpKStTxoy+++GLt8aPG37G+HOeJN23aNMZ4A4tbs2aN3HLLLfLhhx/KnDlzIhbxw4cPy1133SXTp08PWk+KuKMmZOEGAmtWF8iWTXtr/6VJ02zpe3J7MiIBEogjAV+IuJEnjiR95513lIjqbVqdPooUQXhffPGFjBkzRpYvX66qE42I79u3T62L37Ztm7zyyivSsWPHiLoKPfGIcDFzEALwvosKD8pPq3bVydGhU1Np176JZGX/suESQZIACThHwHcirlHqrVtfeOEFWbdunRpyz8/Pjznp7du3y+OPPy4zZ85UZWdnZws+JKIR8bVr16qh+J49e6qRA+wPH0miiEdCi3mDEVj/8x5Zv65QUoJk6NSlmXQ+OvbPEi1CAiRQl4BvRdyIAtHtONks1qebYRh/7NixKqCsX79+Mm3aNFm8eLFMmjQpKhFfunSpnHvuuWpIfsqUKZKWlhZRn6aIR4SLmYMQ2L6tWA2jH9hfVidHVla6dDo6X9q0bUx+JEACcSBAEXcQMkT8sccek759+8pZZ50lmZmZSnwnTpwYlYjjKFUE42FuH7vT4aMAG9/gZLZrr71WrYtv0aJF0BZRxB00ts+Krqioki8+Xy+VFVUBLT9tYGcOpfusL7C5iSXgORHHASjjxo2TTZs2xYxchw4d5KGHHnI8sA0VjlbEMa8+efJk9efMM89UZ6dDvHHYC+bZcX46Dn/BMPtJJ51kyYYiHrMuw4JEBHPj27ftk71FJSoyHd43I9TZNUggvgQ8J+LGA1BihcrpwDZjPaMV8f3796vAOCyZu/DCC+WJJ56QLl26qKLxYYOPEHjm+G3WrFnSunXrOnjCLU/j8aSx6lEshwRIgARiT0ALtlXJeL/jGG63pZRqk7KYRRxD1aGGkO00yAueeEFBgdx5551qvTuC5Hr06BHQNGxyc+uttwqG3CHiGFo3J4q4nd7APCRAAiTgTgJJKeLYJ33w4MHyhz/8QQ0nI/rbzSlaT9xOm+bOnSsjRoyQ0aNHyyOPPCJZWVkBlxlF/JNPPqlTJIbpmUiABEiABNxJ4NNPP61TMcRbIXnGE0dl4XVijTdECyeZYagZCYKOjVuwDOuUU06RjIwM11nCSRFfsmSJDBw4UEaNGqU2hDF/0HBO3HXdgRUiARIggXoR8NycuLm1EHR8nSBiG5HaWtCxEcqll16qRB1D7ggAc0Oqj4hjxzb8ycnJsWzKokWL5IwzzqCIu8HQrAMJkAAJxIGA50XcyAgBXgsWLKj10PVvEHQMMw8bNkzNJSdS0KMV8ffee09NG2DHNmwe07BhwzrdQ3vauAf2ZjfPn9ATj8MTxVuQAAmQQBwJJJWI2xF0LMuCoA8ZMkQt0Yr1Bi/hbBetiK9cuVKGDh2qin/ttdekd+/eAbfasWOHEnh442+//bYMGjQoqMjjB0aih7MUfycBEiAB9xNIWhE3osc2p/DQ//GPf6ghd52OP/54JeiXXXaZdOrUKS6CHq2IY1c57PQ2derUOkvMIOBYO4/lZ9jNDcvNrAL86Im7/4FkDUmABEggEgK+EHENBN7nzp075aOPPgoQdDetEzcuocMWrQMGDKi1J5aZ3X777UqssW96nz591G/Lli1TsQBXX321ikoPtuSOIh7Jo8G8JEACJOB+Ar4U8X//+9/y6quvqsh2JK+IOOpaVlam6o214IhGR4LQYyj9oosuUlu7BksUcfc/kKwhCZAACURCIOlFXHvfCAx74403AobTAQpry//4xz+qvcdDCWAkUN2alyLuVsuwXiRAAiQQHYGkFHEI95YtW5THCo9bnxuuEUG4sfTs4osvlqOOOiouc+HRmSe2V1HEY8uTpZEACZBAogkkjYhTuMN3JYp4eEbMQQIkQAJeIuBpEdfCPW/ePOVxf/XVVwHs4XFjw5d4Rp+72fgUcTdbh3UjARIggcgJeE7Eq6qqZPPmzfLOO+9QuCO0N0U8QmDMTgIkQAIuJ+A5Ebc6ijQR671dblfL6lHEvWg11pkESIAEghPwvIhjsxZspZqWlha1nfPy8tSmL8H2JI+6YJddSBF3mUFYHRIgARKoJwHPi3g9268uj+c68VjUN9oyKOLRkuN1JEACJOBOAhRxirg7eyZrRQIkQAIkEJaA50Q8bIuYISgBeuLsHCRAAiSQXAQo4sllz5CtoYj7yNhsKgmQgC8IUMR9YeaaRlLEfWRsNpUESMAXBCjivjAzRdxHZmZTSYAEfESAIu4jY9MT95Gx2VQSIAFfEKCI+8LM9MR9ZGY2lQRIwEcEKOI+MjY9cR8Zm00lARLwBQGKuC/MTE/cR2ZmU0mABHxEgCLuI2PTE/eRsdlUEiABXxCgiPvCzPTEfWRmNpUESMBHBCjiPjI2PXEfGZtNJQES8AUBirgvzExP3EdmZlNJgAR8RIAi7iNj0xP3kbHZVBIgAV8QoIj7wsz0xH1kZjaVBEjARwQo4j4yNj1xHxmbTSUBEvAFAYq4L8xMT9xHZmZTSYAEfESAIu4jY9MT95Gx2VQSIAFfEKCI+8LM9MR9ZGY2lQRIwEcEKOI+MjY9cR8Zm00lARLwBQGKuC/MTE/cR2ZmU0mABHxEgCLuI2PTE/eRsdlUEiABXxCgiPvCzPTEfWRmNpUESMBHBCjiPjI2PXGRiooq+WHZNtlbVKIsn5WVLj2Oay1Nmmb7qCewqSRAAslCgCKeLJa00Q6KuMiP/90pO7YXB9CCkJ92emcbBJmFBEiABNxFgCLuLns4Whu/i/j332yp9cCtQPc9uT09ckd7IAsnARKINQGKeKyJurg8ijhFvL7dc/u2YtmxrWYkIz09Vbp2aylZ2en1LZbXkwAJREmAIh4lOC9e5ncRh83W/7xHNqwrDDAf5sPhhTOFBCkyXwAAIABJREFUJrB71wH5Yfl2TkWwo5CAiwhQxF1kDKerQhGvIQwh/yWwrYF0Pjqf3mSYzrdmdYHs2nFADh+uqJMzJzdTevRqJfgvEwmQQHwJUMTjyzuhd6OIJxS/525eWlJR+3HDeALPmY8V9gkBirhPDI1mUsTrGts4vI453k5H58tRHZr4qFfUbSrmvdeuLlDL8ZCat2ikRiu2bS2WrZv3BlyQmpYqx/VuLXlNstUcORMJkEB8CVDE48s75ndbsWKFXHHFFTJhwgQZPnx4yPIp4oF4MKQOD9Oc+vXv4MuhYYh24e5DsvKHwHlv8Gl3VBNp2SpHNm8skt0FBxWyNBXY1kLatG0c837NAkmABOwRoIjb4+TKXGvWrJFbbrlFPvzwQ5kzZw5FPAIrWQW46curRaRzl2bK+/RTCvZRoxhUizRp9ksAoHGoPRwjBMRt3rRXxSEgiBAjHc1b5oS7jL+TAAnYIEARtwHJbVmqq6vliy++kDFjxsjy5ctV9SjikVmJIl6X14H9ZfLjyp2C/5pTZma6tGiVozzvSBLE/svF6+tcctrAzgwmjAQk85JAEAIUcY91je3bt8vjjz8uM2fOVDXPzs6WXbt2UcSjsKOVwGCI+JT+HX0rMBhS/8+XG6WsNDAKPZqNcPihFEWn5CUkECEBiniEwBKZvaSkRMaOHasC1Pr16yfTpk2TxYsXy6RJkyjiURpGD/VWlFdKeoM0NYTu933U8XGzZvWu2sC21m0bRzXvTRGPslPyMhKIgABFPAJYic4KEX/sscekb9++ctZZZ0lmZqZMmTJFJk6cSBFPtHF4f0sCGJr/eummOr/5NXiQ3YQEYk2AIh5ronEujyIeZ+C8XcQE4JEjsK2yoooR7RHT4wUkEJoARdzjPSRaEf/kk0/qtPzMM8/0OA1WnwRIgASSl8Cnn35ap3EYlUXCNOvIkSNd1/iUaoRiMwUlEK2IWxVI1OxoJEACJOBeAtrrtqohRdy9dgtZM4q4Rw3HatsiEMl6dFsFMhMJeJgARdzDxgtW9WhFnF63uzoDlnYhCMzvkfHaKlZbvx7fp627jMbakIALCHBO3AVGqE8VklHEIWj6pDEs/WrSrKHaZCTc3txePev6x//ulB3ba87oRmrfoUnEm6rUpw+56dpwW7+2bdfYl1viuslGrIu7CFDE3WWPiGuTjCKOYy+3bAo8aCPcmd9ePOsaQ8Vbt+yVTRuK6ti9e8+Wkts4W63X1semgkGPXq2TeiOakFu/iqiRCp79HvFrghckMQGKuMeNm2wi/slHa0QQy5iSYmmZs87pWuffQ511nZubKd1detZ1uM1QGjVsIIcOlQe0FyeKJfOwcqitX7Oy0tWe65Fu/erxR5zVJ4GQBCjiHu8gySjiWI5gLeEiViLu1bOuMfy/4ec9Umra4lR1yepqqU5JiYiDx7tybfVjufVrsjBhO0ggGAGKuMf7RrKJOMxhniPGv+XkZEi/0zpaWgvem1fPug62fzs2Ron0Y8bjXTmg+uatX3nyWTJZl22JJQGKeCxpJqCsZBRxeGI/LNtWOxcMAe9xXOuQAU245sf/7nD1Wdc6Ah0Bejm5mbW9BfPAOKcbv2dlNVD7t2dlp1t+zLRu01h6HNcqAT2NtyQBEnAjAYq4G63iUJ2wGcBNN910ZLQ2effQcePaYvOSKczv9j35qJBBahD1NT8VSFHRIWWzpk0bStfu4aP0Heo+LJYESMCFBCjiLjSKU1Vyg4gjmGt3wUHB0jF4o127tUzqaGvtfS//fptUVVYFmLZZfiM5ums+l0w51eFZLgn4gABF3AdG1k1MtIhbRWNDyHGiVbImtWTq6y1BI/W4ZCpZLc92kUB8CFDE48PZFXdJpIh7NYK8voZD0B2WwOm13sbyMDeOs7q5ZKq+lHk9CfiXAEXcR7Z3rYhXi/Tt1z6ptxy1+ojB+vU2bRv7qAeyqSRAArEmQBGPNVEXl5dIEQcWq53YMrPS5Vend3YxtdhUDXPjCG7DznKIQG/RspHauISJBEiABOpDgCJeH3oeuzbRIm5eBgYB792nLQO7PNaPWF0SIAH3EKCIu8cWjtck0SLueANdcAN8qGD+u7SkXE0PGNeDu6B6rAIJkECSEaCIJ5lBQzWHIh57Y+udxbBsDluspaalSFXVL2vw/XwiWexps0QSIAEzAYq4j/oERTz2xg4IWAuyT2rHzs2kbbu8pF4PH3uyLJEESMAOAYq4HUpJkifeIo5hZQwnhzsH3Kt41YlrNhOOz8TwuhMJy9j0JjpgrU/6SlbuTjBkmSTgVQIUca9aLop6x0vEzZu6JOvxmXVEPIgn3qFTU2nXvoljnvjXX26UAwcOB/QI7rEexQPCS0jAgwQo4h40WrRVdlrEMT9cVHhQflq1q04VnRayaJnU97pwm9g4/QETbjTA6ujW+raZ15MACbiHAEXcPbZwvCZOizg88PXrCoOegd2pSzN1QlcyJR3YVlRUoprVrGkjyW6ULqmpKZKV3cDxzVyCinh1tUhKiuX566gn6v3jyh21O8nhQJbjudwvmbom2+ITAhRxnxgazXRaxLGZyZZNewVztOYEkeh0dL7jouYjc9Y21Wo0IFxUvNWZ7bDRaT7YeMePfYRtTl4CFPHktW2dljkt4rgh1kl/8fl6qawIPLELh5y4fc006r5jW7EUFR5SXnSLljmOBaPFstsZl7mlpadKixY5IY8sVd77EU/dqh4cgo+ldVgWCThLgCLuLF9XlR4PEddDtRhaLy0tV5HpR3Vs6gkxtPJonYwqT1Tn4Dx6osjzviQQewIU8dgzdW2J8RJx1wIIUjEM/69etUuKi0vr5MjISJeWrXOS4qQxeOzr1+1Ru8mpP6UVAe3lsahe67msLwkg9CVFYcD7feTIka5DklJdjXE/plgQoIhbU1Rnfn+zJSjiWIubeQ95TDP06NXK8ekGq6VoDRtlSGqKSE5uVsgh+Fj0P5ZBAiQQewIU8dgzdW2JFHFr00BU9+0tkRXfb6uToUWrHOnUuVlMBTbeQWWhRhp4prlrH1dWjARsEaCI28KUHJmcFHEIIcQCf7x68Aei639aubPW2Dk5GdK331Ex3XEu3LpyJ+bg4z3SkBxPC1tBAt4gQBH3hp1iUkunRBxzrd9/szlgjjXcEqeYNMiBQtAWBOQhObFNaiJEXH9gLf9+m1RVBq4aaN22sRzVoUlMRxocMAuLJAESCEKAIu6jruGEiMN7xbIseHtIuyqr5FBVjVDk5WXLcZ2bybFtG/uIcvimmrel1R8M8MKdTLt3HZBVK3fWLv/DRwo2eOEe605SZ9kk4CwBirizfF1VuhMibvYs/1tWLoVHRByN79U6Vy47paOrOCS6MvCMIeR7Cw9JeUWV5OZmStduLR3bW93cXow2ZGWnJxoD708CJBADAhTxGED0ShFOiDg88W1b9knxvprlWdoTL6uulp2VVdKxaUO59vQuXkHk23rCS9+8aa9qPzxzbI/r9s15fGssNpwEDAQo4j7qDk6IOPAhmO3rpZsCSDbIzZSFu4olL7uB/M853XxE2XtNtQp8g5BjC1YOtXvPnqyxvwhQxH1kb6dEHAgxRLt9274aT65BmrQ7Kk+mvL9S/f89v+slqUc2JPARbk80FSMpG9TueoEbv6Dy8MQRoNiGMQ2esCUr6U8CFHEf2d1JEbfC+PjHq2XvoXIZ85uu0jwn00ekvdNUqyC72tpXV6tDa5Lt5DnvWIc1JYHwBCji4RklTY54i/jLX26QdQUH5KpTO0rXVrlJwzGZGoIRlK1b9sqmDUV1mtXr+DbSOC+LQXDJZHC2JekIUMSTzqTBGxRvEf9gxTb5ZkOhnH9cGzm1S3KdI55s3WbN6gJ1jCwSTkKD943140wkQALuJkARd7d9Ylq7eIv4lz/vlg9X7pBTO+fL+ce3iWlbWJgzBLD8jcFszrBlqSTgBAGKuBNUXVpmvEX8px3F8vp/NknXlrlyVX+uFXdpt2C1SIAEPEyAIu5h40Va9XiL+K79ZfL0J2skPydDbvnNsZFW15f5sVzvx5U71bI9fRZ7py7NPMECke6od4MGaWrLWie2rfUECFaSBOJIgCIeR9iJvlW8Rbyiskoe+GCV4LTbib8/LtHN98T9F33yc+22qLrC3Xu1cv0yL6uT2bxQb090ClaSBEIQoIj7qHvEW8SB9tEPf5L9pRVqwxds/MJkTcDLJ40FO9QFIwlYa+70nvDsUyTgZwIUcR9ZPxEi/tKSdbJxzyG55ledpVPzRj6iHVlTvSzin3y0xrqx1dUiKSly1jldI4PB3CRAArYJUMRto/J+xkSI+Lvfb5Vlm4vkdye0kxM7NvU+RIdb4MXhdHyAWB1zmtckS7oc05xz4w73GRbvbwIUcR/ZPxEi/vmaAvn3jztlwDHN5eyerZOGNgK4sBwLKZYBXCgXa7YhjJlZ6Wou3As7piGo7aeVO2vti7XmJ57cnoeoJE2PZ0PcSoAi7lbLOFCvRIj4yq375H+/3Sw92zSWof06BLTKuG93Vla62uLTC/t0m7cq1fO+fl9fjd3fSkvLlY3BxO88HHiEWSQJ1CFAEfdRp0iEiG/fWyLPLvpZWudlyagzjqmlbXXyGX48bWDnOtt8wuPdsa1Yeb4QiYKdB2q94NZtG0uPXq3iYkXcv2Dnfvlp1a4692t3VBNp265xgOcJUVu/bo+UlpRLVnYDtQOaF4/3NIpzVlYDbsMal97Gm5CAPQIUcXucos5VVVUln332mTz99NOycOFCKS8vlwEDBsgNN9wgF110kWRmRnYwiFGIrSp13nnnydy5cyU/v+42p4kQ8bLySpn2zx8lLTVF7r6ol6pyqEM3qkWkc5dmtUPIEJCvl26sEW38iPVqphSvpUyRBJ+hvl9+vr72YwNVhmfar39HT4kgzhnHunU9dYA24KOpecucqJ8JXkgCJBA7AhTx2LGsU1JlZaXMmTNHbr31Vtm/f7/0799fGjRoIMuWLVP/P378ePWnUSN7UduHDx+Wu+66S6ZPnx601m4TcVT0oX/+KCXllXLn+T2kYUaabRGHaELw8d+QqVqkSbNsx5cyGTdiMdcH0wEQtq7dWgimCTauL5SSQzVDy8YUj2kDCG9BwUE1AtCiZY5gtCKaoW1wxzx3SUlgO7IbZkj3ni1jGgvg4GPIokkgqQlQxB0075IlS2T48OHSunVrmTFjhpx00knqbuvWrZPbbrtNFi1aJM8++6xcccUVtmqxb98+5cFv27ZNXnnlFenYMbKtTBPhiaNhsxb9LFv3lsj1p3eR9k0bqrbaGU4PeUymiRiCy+K1HvmLz9dLmen87X79O9QOlaPe69cVWg0aqFpjBzangtUg4D8s3x5AJ1o2QZeOHSmdS8dsPbbMRAKOEqCIO4S3rKxMxo0bJ0888YS89dZbcumllwbcacWKFUq8u3fvLs8//7w0bRp++dXatWvVNT179lQfBbm5kR3vmSgRn/ftZvlh6z659MT2cnz7X07GguBA8A4cOCw5ORlK2IzDtPAEN6wvlKI9h2rZWY2oH39Cm7gO72JoefPGIvUhgjlueLvGuW5MARQVHrScO+/Qqam0a9/EkSF1RLXr2AFzt8Zyr2O7t4xoTh6buOzbVyrVVaD+S0pJTZG8vKy4fTQ59IiyWBJICgIUcYfMuHHjRrnqqqskIyNDzVG3bds24E6HDh2SP/3pT/LBBx/I+++/X+ulh6rO0qVL5dxzz5VbbrlFpkyZImlpaRHVPlEiPv/rzfLt9n3Sp2WunNEtsmFYCOZ/vtwY4PlmZKRJw0YZqu0YKnYyoh1CjRRNQJrxeE9V1zaNpcdxzgXhBds5TXcSjFREuhzOajQkViMJsC2OPy0qrPlIA2N8yEUz9B/Rg8DMJJBEBCjiDhlTC+6wYcPk8ccfl4YNa4aRjQlCPHHiRDVvjmH3cGn27NlyzTXXyIsvvijt27eXadOmqWC5bt26ybXXXivXX3+9tGjRImgxiRBxCNm36/bI6vIKaZmWJt0z0iXSQDS87PcWHpL9+8ukabOGEQtROK5Wv0O8f1i2TUqPDJtDWCCCkYo56q499vqKky4L/8XcurkuuM/Pa/ZI4Z6DAU1KTUuVE/q2jXrZF0ZMwB4pNzczZqMeVh8IzVs0kuP7BH7wRmM/XkMCfiFAEXfI0u+9954MHjxYxo4dq8QWHrk5wUMfMWKE3HfffXLPPfeErEl1dbVMnjxZ/TnzzDNl1apVSrzT09Nl+fLlUlhYKKeeemrA3Lu5wHiLuPYMi6uqZFlZuaRLtTRKTcNOnJKWVrOvNhICzs/p1VraNsl2yBqRFYth/HVrd8u+vaUBF2IEoFfvNnH5iDDXGML99Zcbaz8q8LvV8joM5f+wbKuaokDCpisItnNytCIyujW50Tf2F5dKZWXgUD1+i3YOP5p68BoS8DoBirhDFrQj0Hby6Oohmn3MmDHy8ssvy4UXXqjm2rt06aJ+Lioqkoceekh9LOC3WbNmqWC6UCI+adKkOr/fe++9MaWhA6MQ2/xlSY0nFyw1SEuRX7VqLHlHwsEwz9y+wy/z5zGtWJjCUO8gq9nUlfEO6EK0O+a6raL027XPkw6dmtWZY9c7yrl10xW3MY5n/+K9SCBaAlbvaDh2SHDSRo4cGW3Rjl2XUg0X1IPJjkDbyaObXlBQIHfeeadgrn3mzJnSo0ePACrFxcVqKRuG3CHiGFoPJeJWSGON2rin9j5DcFSjnAxp0y5PBbMh/bB1r3y3sUj9vUdGA2mRlqr+Humwe6y6idu8RCfmumPFKtpygo12NMhIk+MSNNoRbVt4HQnEi4D2uq3uRxGPsRXsCLSdPJFUS5c3evRoeeSRRyQrKyvg8nAbxcRaxHHzcHtqw2NcvWqXrNizXzYd2Ys8LxUD7Clq2D0jI12yG1ofYXpsq1xpUVmt5pyRMF8O772+c88oy2q+1unAtGC2DuWJY0i9c5d8R6LdI+l7dvPCVnrtf0ZmulSUV8rhw5XqcuwV37tPzdw9EwmQQF0CFPE49oqPPvpIRZL/5S9/CRpJrkV36tSpKl99E9alDxw4UEaNGqU2hMnODpxjjvecuG5PqD21jbugba+okjXldTdICcUlKyVFjm7QQPLTarZyg4hjDtgqQUDS09NsC97mTXsFQV1IWPqGeeVYfCBEY2cw/HLx+oBLsZUrRjNyG2cJAsK8IH5Wa+y7dm+hIvcTxTYae/AaEnALAc6JO2QJJ6LTsWMb/uTkWG95ic1jzjjjDNeJeCjEOuJaH2V5sLpaKo5MoDTLbygtWuVIdnZdT/zH1bvkv4WH5NCR2ZZGKSmSDtf9yPamtUFzKSKdG2dLSsHB2q1D8dvxJ7S1LeYOdZGIi4WQb9+2T6qqqmX71n1SXl5zippOVvvOR3wThy6IZMtah6rAYkkgKQlQxB0ya7h14jpQ7eOPP7a1TlxHu2PHtmBL1rSnjaVrEyZMEPPQS6I8cTuI4fGuWrlTKg3He2KpUTDvTAfNbaqolA3lFWFvkZmSIh3S0yU7VQ3UK++1bfvGlh8I5sIy0lNdEzmPoXWsrdZTCMa6xmNL17Cgg2SgiEdLjteFI6AdAeTz4wE9FPFwPSTK32O9Y9vKlStl6NChqjavvfaa9O7dO6BmO3bsUFuywht/++23ZdCgQXVq7mYR15WFt5mVnR6WOkQBa9AhZmXVIqXaIzcEzWGZ1f9bXSDbystlj2nXsbA3MGUYf2EvQQR9olO4rWhjtRGLU+1c9MnPtR9q+h7HdGuhTnhze+JRq+6zEJ5/BH7qA3pQw0QFxCaKDkXcQfLGvdOffPJJOfnkk5V3rPdOx25tdjd6qaioECwLw/y5eYkZBBxbvGL5GXZzw3Iz83w4mukFEY/EHHiAVyzbVrubG+aHexzXunZuGL9D6CH4B6qqZVN5hegZ93BBc7oeBftL5dDhShnev5Mc44KTu0Jt6YrjUDt0bGrrIygSzrHMa7ZZooIFI22T1WluGCmKdAe8SO/L/MEJoC/9v5921dnPAXtQ9K7H5kZeY04Rd9BiEN5nnnlGnVRm9xSzPXv2qN3bFixYIIsXL1bHltYKSkGB3H777UqssW96nz591E/6VLSrr75aRaUH27Ut2UTcrumslmjZ9f4+Xb1LPlu9S07tki/nH9fG7i0dz2fe0hViYjX9YDyLHVMT0Z5o5niDEngDCHTRkZPysCMdGBmTcamk8d+xH32XY5pTyBNku2RcehkNSop4NNQiuCbS88RDiThui2H6+fPnq7Xg8PSR7J5P7lcR1weW6M1SItlvfXPhIXlh8TppnpMpY37TNQLLO59Vb+iCO1l5hFa7vEHITzu9MyPBj5jHzt7wPM3N+b4czR1CHfiD5wGrVLywYiOathuvoYjXl6CHrveriNfHRFg7/+D/rZLyymq547zu0igz/Hx9fe4Xq2vxwWJcImcsF8vlMAft5FAwhv13FxxQc5VuXf4WbFMfvBThZeujbYN5fPgggkjE6wjcWPWNZCrHvA8F2uamKRo8B5s31Zx4iP5yVMemMX/uKOLJ1KPDtCUuIl60WaRsf+iaNO0gkmm9TK72QheV88bXm6Rwy89y9tGNQs+Lu6hd2KZ1bUFDKU+13o++NgDOAc4IKFy/dnfAvujYRrdd7+NdZffFyyuC8kE/VNvrHuGzdfM+KTiyZwB+M27LW57TTrr27hz65ewA56APmYv6oaqjw/XBBytOwsMHY9OUPdK8ceDSyzqcHK6P8X7LFi4VKd0vh1MbysH0luqnaE4TDPVCpYhTxGNLYOHDIluXhS7zN3eItO8bOo+Lyvl2Q6E0/uIx6Vrxs6fatanzdfJzaYc6dW7ZKkcQBKc88Thy3tlzlOT1+lXowLs41ufgKbfKqr1t6yzXC9j61UZ9VjS9SvbldJfTzzo6eP+wUY547LlQjWW7LG0Oz/vHlTuly8YXJL9sjezJPFZWNLtK5c3MTFf7XwTblCrSFzJFPFJiHs4fF0/8u9dFCtaEpnTiFSItjgmdx0Xl7Csplx/e+7t0qNwqHfIbBa+3C9v19doGtSeaoeKI4O93Wsdf2hBjzsZ5ejOon3PPlqN/1T+0x2pRH0xplBwqr11GVNjp99L+xJPV8CSGK3dsL1YnomHtP4ZS1RJFm+3am95OrXDQ+xPg1LeevVr9ctyqqRyr9qFdxRntQ3tZNuvjpedCNZjtsnwf6H0RuhR/LHnlW2RfxlGyLveXZb+xPKmPIu5hUY606nER8Ugr5ZH8Ty78f1J48LDc+OujXbPxi110eKGUlJSrjW2cnAdHfTBHuWl9oRw6VHf7XAzht2mbF/ESOJzrvrsg8Ix0tCUjM00OFJdJpWEPAAh7v/4dw94D9Sw7clZ88xY5UlFRs397uM1CwkVEY2terHzQmxSBvR6GP3TwsBTuOaTug987HZ0fdn28Xl0A+zVokPbLR0oY4xvXtCOrDurERxxiIuwkfY2e+7dzTazzoP2lJeVxD1DDfdFuvc+/rkPno/Nt1QXXF+4+JCt/2F4HCUbB2rZrbKscOzwp4nYoJUkeinj0hvy/H7bJ1+sL5bSOzeTXx7YMKxLR38n7V1rtzhZqT/tQLQ4ZGY4Nfo5stVtbRrUocccKhFatG0tpabk6ZAUBaHix4g9ezOYd7/r171DnpYqlZ+vXFaq82A0PJ+/hQ8QqmMp4/5TUFGmclyWHD1eoEQSVrOoqIieefJTkNc1SIwoHDtQc5IMthfbvL1MfXio40LC9LkYKuvdopdqINul2YBfCXbv2q//H/YuOfCxY3bdhQ3wApas/DRtmqA8YfCBgmLesrKJ2W199MA3qhHbjgCF85KxZvasmUKtBTR1wXWpqivpA1B+J+uMDc9XY4x+/ozzYAuXAPmpUo7xSDTvrzVqa5jdU5WFrYfyODx8kXI/13+XllUpcEWOBcpFqR18sOhLKQH/EFMme3Qdlz5Htl5s1byjNm+eo8oz1Rn58NJaWVlgeR5yRkSa9ereRyopqxRvijpEitE3bOr1Bqvpgxv/jvrV94MgqklA7UUbz9FPEo6Hm0WviIeJv76uQdWWhA0suadJAumSE3v3MTeXgBXP/dzvlu7JKwfatrdPTAh583R281i7U2ynOYIYXMF7GDRtlCF5+0fDZsK4w8GmrFvlVaYm0rqj7kjUGm32RnS070g0rCbSIGjL9qqSmHCSIbtv2eUocIN4H9h+WbVv3yecZmQHlIJ4A7VEbCBWXSUVllRjLCfZqMNcn4Lz6I3ULW051tXzRsGFNfYwFBBQmclpJibQ50q6Q9UlLq/0IMhWhLrOsj+ljxNwuCCz+QNRg+6DlqA+bI18sIhKaT02+UHx0sNiza/bKD3tK6zbbaPdDJdK60rBVc3W1+kAwjujU6T+m+uLj6FelpTX9xwrekRqgnF0ZDeSk1o3k8hZZjnz8U8Q9KsjRVDseIn71xhJZuL9maDJY+kfHbDknNy1kHreUgy/zbVuL5U8HUmS56WhXcwMeapoiA8OconlHUbV8UeNwBU0sJzSfm/cUyvGldV/UxnfpjPxmsjKMvczlNMnJkDR4i0f270ctHm7WVJZlBh7pa67d7YVFcmKZlXD8MlIQVTkmwUT7/hZhfYLpS1T1sTCLvXIK5cSymk7vVH3glTdv3lD+dDA1MnsFqZC9dh2xe5BRFmP/6VtaKk81TREMx8c6UcRjTdTF5cVDxOfZ9MSPDuOJu6UcNQdaWCJLGmXLsuoUKTuyR7uVmY85eEiaHK4Z/guW1uTmyD6jh2iRkeWEXovvJB98Wuampkhuaqoa1l7WqJHsTg/84MRxttjZDZ4mhmOP3b1Pmoc5QteqHLPp+xwqYTkhnh0v82lfWSk3tWtEEXexPnqiavEQcU+AiKCS2BUKw6uYI9tRWSU7jwRAoYi0tBQ1tIqhOKb4EMD8I0ZHKiuPnFcrNXPgiC43/ltEtUknG/fSAAAZ8ElEQVQRSUtNlf0VlVIe4iMtojKZmQQMBI5p0UiGn9bZESb0xB3B6s5CKeLR2QWi8fXSTQEXZ2aly69Od+ahjK6W/rpKL/VCYJWOBLeKYtdUzKOmmKPv2CVfLblDGYggxwY5OwpLZH9VlZRUV0uVQdARUAbvG0FZVgkBTkiYC0Ywl54PRkBcXtNsScV59+lpgqAnpM0biwKG7UNZL7dxpuTkZMrevaVScij0SI8uB8FhqDPYIBgN8X+Y8z14JGDOTm9plJOh6lxcXCrVQU4BhIDgwypswrBGkGwoA/VDXcsrqmrvhX+HnRCoh8thD6woUPPXlb/E3SDorVXrXFUFBB0GS/o+sE24eiPoD+Uq+zWp2TAJNtN2Nd+jJhYgTbDNNibwEdSIhDKaNW8knds0luPa5YXFFE0Ging01Dx6DUU8esPppUIQD0SzYvvEYGedR38XXllfAthqFgFnEEsINEZQkBCRrZe9IaoaS8HM9kOE+H+Wbgw4KjXa9bx6iVKw9uBeiPLW8+8IrMMLH8n4oWBe3oX27S08pAQDbYKwoaycxpnSokWOisYPt0xOn7+NthnXveO6oqKaJXBNmzasDcJCHkT06zYZtw5Vp+oV1eyWtn9fqezc8ctujcYzCnAvvWQNArxtyz51De6JeWJ9/LDxbHDjB5qZI+qEQ2vAx1hXjJqBkV5SaeRqXN6I+0DwER2PD6zCwkOqfugzrdvlWS79Q1u3b9un8qE9KA/9LNHvAYp4fd8aHrqeIu4hY7GqCSGAlzu8cnjW8LztrqlOSGVdelMIrB8OHnELfoq4WywRh3pQxOMAmbcgARIggTgSoIjHEXaib0URT7QFeH8SIAESiC0Binhsebq6NIq4q83DypEACZBAxAQo4hEj8+4FFHHv2o41JwESIAErAhRxH/ULiriPjM2mkgAJ+IIARdwXZq5pJEXcR8ZmU0mABHxBgCLuCzNTxH1kZjaVBEjARwQo4j4yNj1xHxmbTSUBEvAFAYq4L8xMT9xHZmZTSYAEfESAIu4jY9MT95Gx2VQSIAFfEKCI+8LM9MR9ZGY2lQRIwEcEKOI+MjY9cR8Zm00lARLwBQGKuC/MTE/cR2ZmU0mABHxEgCLuI2PTE/eRsdlUEiABXxCgiPvCzPTEfWRmNpUESMBHBCjiPjJ2rD3xb775Rvr166cI4u8nnXSSj2j+0tSFCxfK2Wefrf5h7dq1cvTRR/uSw7x582TIkCGq7QUFBdK8eXNfcnjhhRfk+uuvV20/fPiwNGjQwJccpk+fLn/+85+lYcOGcvDgQV8yQKPvvfdemTx5srRp00a2bdsWcw4U8ZgjdW+BFHFnbEMRr+FKEa/hQBGv4UARr+FAEa+urnbm1eu/UinizticIk4RN/YsijhF3NgfKOIU8ZgpD0U8ZigDCqKIU8Qp4nWfLXri9MRBIKWaIh4z5aGIxwwlRdwCJYfTOZxu7BYUcYo4RTzGmmMU8RgXzeJIgARIgAQSSADv95EjRyawBta3piceQ5NQxGMIk0WRAAmQgIsIUMRdZAynqvLtt9/K+++/71TxLJcESIAESCBBBH73u9+5cpkvPfEEdQjelgRIgARIgATqS4AiXl+CvJ4ESIAESIAEEkSAIp4g8LwtCZAACZAACdSXAEW8vgR5PQmQAAmQAAkkiABFPEHgeVsSIAESIAESqC8Binh9CfJ6EiABEiABEkgQAYp4gsDztiRAAiRAAiRQXwIU8foS5PUkQAIkQAIkkCACFPEEgedtSYAESIAESKC+BCji9SXI60mABEiABEggQQQo4gkCz9uSAAmQAAmQQH0JUMTrS9CB68vKymT+/Pkya9YsWbJkiTRo0EAGDRokN998s5xxxhmSmprqwF0TU+TBgwfl3XfflZdfflm1df/+/dK3b1+5/PLL5frrr5cWLVpYVqygoECef/55eeONN+T777+Xbt26yYUXXii33nqrdOrUKTGNieFdKyoqZNq0aXLPPffI4sWLZcCAAb7hsGHDBnnyySflgw8+kNWrV0vHjh3l0ksvlVGjRik7WyXzNXb6UAzNFfOiqqqq5LPPPpMnnnhCFi5cqJ4LvAPwTFx88cWSnZ2dtByKi4vVc4w24rjVYG2N5h2QjO9WinjMH7/6FYhO9tBDD8nEiRMlNzdX+vTpI+Xl5bJ06VL1/1OnTpWbbrpJ0tPT63cjF1y9c+dO9bC++eabtW3NzMyUNWvWyMaNG+WEE06Qp59+Wk477bSA2m7ZskUxwEseL/iuXbvK5s2b1Qs/2DUuaG5EVcCHzdVXX61e3sFEPBk5QLiuueYaZX8INuyLv2sx/8c//qE+ZI1p2bJlqj989dVX6pqjjjqqtg+de+658tRTT6k+4pWED7hHHnlE7rrrLlXl/v37KyFbvny5FBYWqn6B380fuMnAAR/1eMfhDz7agol4NH0/Wd+tFHGXPdmvvfaaOrP217/+tcyYMUO9xKqrq+Wbb75Rgrdjxw6ZO3duUM/MZc0JWh28qCZNmqQe1htvvFEeeOCB2pcSHuSHH35YJk+eLL///e/lueeek5YtW6qySkpKZNy4cerFfO+996q/4wWHB3TOnDkyduxYOfvsswXHBgbz4t3O6Mcff5Q//vGPSpSQrEQ8GTnodqOPw/5DhgxRo05G25566qkye/ZsadOmjWIDbwwv+48//li98PEBgA9cYx+65ZZb1IdxMI/Obf0Bnvcll1wiPXv2VCMSJ598sqSkpKhnH/0do1aPPfaY3Hbbberfk4WDsX1oUzARj7bvJ+u7lSLuoie4qKhIDZfB637nnXfklFNOCajdhx9+KJdddpl6weOlBK/Vqwne1VVXXSWVlZXy6quvSufOnQOaoofU8MJGu8855xz1+3/+8x81nAjvBMPpTZs2rb3O+GHw1ltvqSFYryWID17UONYWbfvXv/5lKeLJxgH9AB9tU6ZMkWeffVZuuOGGWoGCDSHk4IIRCozc9OvXT5l23rx5SuwhaOZnQvehjz76yPJ5cmPfAAdMoTz44INi1YcxdTRs2DA59thjlZjr/u9lDhBltPX+++9XIy74QNu+fXtQEY+m7yfzu5Ui7qInGS9unFkLTxJeOIbPjQleB4Tv0KFD8sorrygv3asJbYXX3KtXL3n00UctvSS80DGtAA97+PDhqqnwtv785z/LM888ox5yc1q0aJEabkXZmFPOyMjwDCKMuCAO4vbbb1dChmkFtN/KE082Dtu2bau1MUaa2rZtG9Zuhw8fVkPOYGH80DNeiI9AeOfoY+gTbk8QNNQTI0lWdscwMt4BaDveAV26dFF/9zIH2HvEiBHSrFkzufvuu+X4449XH+3BPPFo+n4yv1sp4i56ql9//XW54oor1DAz/uihMl3FcA+4i5pS76oYPRIt4qWlpUrgZs6cqYJ+MOVgTviSh6fSunVrNe2Qn59f77rEqwBMmWCkBR8s8ErhjVmJeDJywOgT5q/hgdv9+IJ3hfnhTZs2qQDH7t271zEVgiUHDhwYcn41Xva1cx98yGFqCd44+jnm+o3vAe2FItARH3x5eXnidQ4YdcTHyZVXXqme11A2i7bvJ/O7lSJu58mKUx79hRnKa7DyTuNUvbjeZv369eqhxpAohk/hse/bt0+95PGQv//++3LSSSfVqdOePXuUCGJ+DQ9usGjmuDbGxs0Q5AfPA1MCeDnjI0Tb2uyRJSMH/ZJF3x89erRanYGgRswPw4YYMh8zZkyAh66nZIA32MiUFz/qjLEBiE4///zz1Tz/d999JxMmTJAvv/xSjdTggx8p2TiEEvFo+34yv1sp4jZesPHKYkeg7eSJV32duo+eF4YnMn78eOWV4iVmR6Dt5HGq3tGWq6ORIVrG6OtgIm6njXbyRFtfJ67TQ6oIaFuxYoWa70XcQ05OTtDVCnYE2k4eJ9pT3zKxZA59H/EixoRYAIxUnHXWWbUeup022slT3zrH6vpQIm6nX1vlsfPetJMnVm2MZTkU8VjSrGdZdjqRnTz1rEZCLzcuMRk8eLCaG2zVqpWqU7QPcEIbZOPmOmARXhamC/TyQT+JOKYOIFpIGFbH/2OtN4aS0SfgoUPgEakNgW/Xrp0Kggo3dWInjw0TxTULlhWirYiLwfJSLDNFn9BLzP7whz+oIDAspUOy00Y7eeLayBA3o4hHZgmKeGS8HM1tR6Dt5HG0kg4WjjWwei4QG7foJXb6lsko4ghew7xuhw4d1AeLMdreTyKuhzsRrPm///u/SqyNyRgPoqO27QiTnTwOdumIizYun7JaeonRmvvuu09+85vf1H7g2mmjnTwRV9ahCyjikYGliEfGy9HceIkjkCVY5DVurl/sWPMILyRZEub1EJX79ttvy9ChQ1XEMbwtY4KHgnlRBEHhRd+7d+86zddCv3v3bgGjY445xrWI9LTBJ598oupqbk8wEU82DjCQHk7HB43Vygzk0ZHmEDF87NmJaNfi1b59+4AlWW7tFDpw7cQTT6yNjTDW1biMErETWJKabBxCiXi0fT+Z360UcRc9zckcQRkKMwJ1sKUshguxoQ2ic83L63B9tJGpLjJxQFW0wKDddpIWr2TjgLZjLTeG0UPt0qWXD2oOXo/KtrK5/pgJtkLFyEovo8THYDJF6TM63c7b4Jc8FPHIeDmaO9xaRv3FrdeIenmdOEBiOQ280Ouuu05tJwnxxm51oTaxCbdGVIuBF9aJr1v3/9s779ColiiMHwv4NBrFhuUPOyo2VFCxYRc1dsWOBbFixxLFhtixi2DFXrCgURF7wYqgKNhBEbGgoFGxYnl8A3eZrHf3TnajuXPzDQQfL3P3nvnNZL+ZM2fOPJbk5GRJTU11HVdOulEEeCUmJkpSUpKa5KAEiQPag2A2HK8rV66cWjEXLlz4DybOShxZ/sDN5Hy0swKz5Zx4ekQcUfxIv4qsdl7nxG3i4HUsMJaxH+TvVor4X5Xl9H14kLMKuZFw8mTDRYb0kjgy43W5SyzZmtLXC/6pHcmdDguDxgGrSaRHxX43tkqwKteLk30N2y34wWUgKDZnKnMbaY7Y4My7E8AXyZ2uT0yCxMFLxGMZ+0H+bqWI++c7W1mi5/fFGVFkZNJzpyOxBeq4JTrxWVOimqPnvEYMAM6Ehye3cfsAPfAHLseJEydKQkJCmvzaOH6j51u3iYtuazQRDyIHJ0ofYx6TOiQ0waROz4OOyGz8Dp4JlPDc6cj8BU+O/kw0F73fxkZ4YBuOVzp54vE7JD7CCQbET+C/nRv7gsTBS8RjHftB/W6liPvsr1g/YhXkW8wct6EJfj3tKuojohurNnzpB/UWM7QzmogHkQOCtnBOHlshzpW0yODl3GoHUUe+/PAEPnpMRRBuMfO63Q9tRLKX8Il8UDh4iXisYz+o360UcRMV+cd1gnjnrY5Q38s0QRsu4s4KLMj3iZuIeBA5wOuE/XH0bUpKispGhpvLsMJ20nK6jZmg3ScOwcFlL3CpQ9QwqUFsBHInICI90g19QeBgIuKxjv0gfrdSxE1UhHVIgARIgARIwIcEKOI+7BSaRAIkQAIkQAImBCjiJpRYhwRIgARIgAR8SIAi7sNOoUkkQAIkQAIkYEKAIm5CiXVIgARIgARIwIcEKOI+7BSaRAIkQAIkQAImBCjiJpRYhwRIgARIgAR8SIAi7sNOoUkkQAIkQAIkYEKAIm5CiXVIgARIgARIwIcEKOI+7BSaRAIkQAIkQAImBCjiJpRYhwRIgARIgAR8SIAi7sNOoUkkQAIkQAIkYEKAIm5CiXVIgARIgARIwIcEKOI+7BSaRAIkQAIkQAImBCjiJpRYhwRIgARIgAR8SIAi7sNOoUkkQAIkQAIkYEKAIm5CiXVIgARIgARIwIcEKOI+7BSaRAIkQAIkQAImBCjiJpRYhwRIgARIgAR8SIAi7sNOoUkkQAIkQAIkYEKAIm5CiXVIIAMJXLp0SRo2bKg+cfv27dKnT58M/HR7PmrOnDkyY8YMGTp0qCxbtkxy585tj/G0lAR8QoAi7pOOoBlZh4CJiH/69El27NghzZo1k/Lly1sLJ1o7KOLWdisN9xEBiriPOoOmZA0CXiJ+79496d+/v3z//l327NkjFStWtBKMVzso4lZ2K432GQGKuM86hOYEn4CXiDu/r1GjhtUiHpR2BH9EsoU2E6CI29x7tN1KAhRxK7uNRpOALwlQxH3ZLTQqyAQo4kHuXbaNBP4tAYr4v+XNt5GARBLxBw8eSI8ePeTWrVt/UHKL4P727ZucPXtWNm3aJKdPn5a3b9+q/fNGjRrJoEGDpG7dupI9e/Y0n/XlyxcZN26crF27VkXGN27cWLA3vX//filSpIi0a9dOJkyYICVKlFDP/fr1S+7evavc+qdOnZKrV6+q/1+wYEGpX7++9OrVSzp27CgJCQmh95i2w2tP/Pfv33L//n3ZuXOnHD16VG7evCn58uWTOnXqSKdOndS7CxUq9Acrh2/r1q1VcCDKrl27ZN++fXL+/Hlle/PmzRWjpk2bSq5cuTgqScBaAhRxa7uOhttKICNE/NGjRzJ+/Hg5cuRIRAwjRoxQAg3Rcoou4gsWLJArV67IoUOHQr9v0aKFbN26VYoXLy4fP36UadOmyapVq6KibtCggWzcuDEUgJcRIo6odti3YsUKZYdbwYRl/vz5StCzZcsWqqKL+PTp02Xy5Mlq4uRWxowZI3Pnzk0zCbF1XNHurEmAIp41+52tzkQCkUT8x48fSrCuXbsmbdq0kWrVqilxLFu2rOTMmVMSExOVWD19+lQGDBgg586dU8KJVTqEDL9/+fKlHDhwQIkfVuYQ8kWLFoVEShdxIMDzELm2bduqz33x4oVaWf/8+VOJKEQQq1+8o2fPnlK6dGm1On/48KHs3r1btmzZomxGvZkzZ0qOHDnEtB2RVuLwMGDysGTJEtVLWHEPGTJEqlSpIo73AROL69evqwnK5s2bpX379n+IeKlSpaRAgQLy9evXEKO8efPK7du3Zfny5aEJELwQXbp0ycQRwVeTQOwEKOKxs+OTJBATgXj2xCGQEMt58+Ypd/mGDRukatWqaeyAGxpudriLIeTbtm1TwowSLuKRBOzOnTvSvXt3wTExiDlc7JhI6AVCjwnC1KlTxXFd6+5tr+j0SCKOrYHOnTuryQHaCY9DuMv7+fPnMnLkSOVF0L0HsE/nG4kRnu/Xr5/ihIkOJgz//fdfTP3Jh0ggMwlQxDOTPt+dJQnEI+JPnjxRK1Os1qOtICGwWB3D3YxV++rVq9VqXBfxJk2aqD1jZ/9b74wLFy4ogcbKd926dVKmTBnXvsLzffv2FbfPikXEcTZ+ypQpKoNbhw4dZP369VK0aFHXd8NGeAfgfdBZ6HwhzogB0N3t+DBMdGbPnq1+unXrpiZD+fPnz5LjkY22mwBF3O7+o/UWEohHxE+ePCmtWrWSypUrq4C2ChUqRCQAtzrc0AhAQ3AY3Mu6iA8ePFi53fPkyWNMEc+/evVKBbsdP35cUlJSlBve7Ux7LCL+5s0b6d27twqiwyo8OTk5om3v378XtAEBa6iHlT3c+TrfEydOSMuWLV0/w/EEuHkRjIGwIglkMgGKeCZ3AF+f9QjEI+IIOkM2t/QUXWB1ETfJWf769Ws5ePCgHD58WC5fvqzc824lo0RcD4rzyiuPvW64+desWZMm/7rO9+LFi4LAO7dCEU/PKGJdvxKgiPu1Z2hXYAnEI+KO8KQHDiLNIcK1a9dOsxKPJuIIXkPg2qhRo/4Q7po1a6qVN1awcGVjz/pviPiZM2fUEbBoxW1fnSKentHBurYToIjb3oO03zoC8Yg4zncPGzbMdQ/aBITpSlzfb4ZYI/gLQWIIXNMD3Jw98b8h4l4r8UhtoYibjATWCQoBinhQepLtsIZAPCKOPWhEmuur6/Q03ETE9eAyrIQR3V6yZEnX1yAALSNX4vqeOFbZOGoWHpTmGKLviSNiHz+oSxFPz4hgXdsJUMRt70Habx2BeERcP/qFwK9JkyapYK7wguh0RF4jEK5SpUqycOFCFeVtIuLv3r1Tx6+QJS2ay/3Dhw/K3Y59+oxaiccana6v2ini1v1J0OA4CFDE44DHR0kgFgKmIo4kK4iurlevXug1eiIURJsjQh2r5fDVKtKLIgAOkeN6IhYTEf/8+bMgkxmOXSENK/4tVqxYmqbCjqVLl6oz4ijRRNytHXgmo86JI3ANIo5ENCgU8VhGJZ+xlQBF3Naeo93WEvAScT1CGy7i4cOHq0QkTsY2JGAZOHCgOivuZFODYMPFjmhyJEBZvHixCkjDPjbc4c5RNBMRB1hkisPxLZSkpCQZO3asVK9eXWVrw3sREY4jZk5xE3GvdkTL2DZr1iyVZAYlWsY2tB/n2HFe3CkUcWv/NGh4DAQo4jFA4yMkEA8BLxGHOxtu7L1794ZeE56Q5MaNGzJ69OiIOcHxIFaoK1eulFq1aoU+x1TEU1NTVdIVBNK5FYgngt2QkAX/Pn78WI4dO5bmOJdXO6JdgJIRudNhN4+YxTNS+awNBCjiNvQSbQwUAS8RR2OfPXsWul0MK2o9YYsDA4IM4cQNY1gdw3Ue7XYxPGcq4k5dnBHHnjdsRhpUHC+Dix0JWbDXjn1xJ+GKW3a0aO0wvcUM7nykR3VuMcPkBGlZu3btGvUWM4p4oP5s2JgIBCjiHBokQAIkQAIkYCkBirilHUezSYAESIAESIAizjFAAiRAAiRAApYSoIhb2nE0mwRIgARIgAQo4hwDJEACJEACJGApAYq4pR1Hs0mABEiABEiAIs4xQAIkQAIkQAKWEqCIW9pxNJsESIAESIAEKOIcAyRAAiRAAiRgKQGKuKUdR7NJgARIgARI4H+bdE10BYlJZwAAAABJRU5ErkJggg=="
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![best_objective_plot_2.png](attachment:best_objective_plot_2.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Objective vs. iteration for outer fold 3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The best model test MAE is somewhat better than the default test MAE."
   ]
  },
  {
   "attachments": {
    "best_objective_plot_3.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![best_objective_plot_3.png](attachment:best_objective_plot_3.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Objective vs. iteration for outer fold 4"
   ]
  },
  {
   "attachments": {
    "best_objective_plot_4.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![best_objective_plot_4.png](attachment:best_objective_plot_4.png)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}