ember2018-AHBO-TPE-hyperparameters comparison done.ipynb

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   "source": [
    "import os\n",
    "import ember\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import altair as alt\n",
    "import lightgbm as lgb\n",
    "import matplotlib.pylab as plt\n",
    "from sklearn.metrics import roc_auc_score, roc_curve\n",
    "_ = alt.renderers.enable('notebook')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_dir = r\"C:\\Users\\fahad\\Ember\"  # change this to where you unzipped the download"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Vectorizing training set\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 800000/800000 [17:09<00:00, 777.06it/s] \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Vectorizing test set\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 200000/200000 [04:18<00:00, 774.29it/s] \n"
     ]
    }
   ],
   "source": [
    "ember.create_vectorized_features(data_dir,2)\n",
    "_ = ember.create_metadata(data_dir)"
   ]
  },
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   "execution_count": 5,
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    {
     "name": "stdout",
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     "text": [
      "Wall time: 2.37 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "emberdf = ember.read_metadata(data_dir)\n",
    "X_train, y_train, X_test, y_test = ember.read_vectorized_features(data_dir)\n",
    "lgbm_model = lgb.Booster(model_file=os.path.join(data_dir, \"ember_model_2018.txt\"))"
   ]
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    "plotdf = emberdf.copy()\n",
    "gbdf = plotdf.groupby([\"label\", \"subset\"]).count().reset_index()\n",
    "alt.Chart(gbdf).mark_bar().encode(\n",
    "    alt.X('subset:O', axis=alt.Axis(title='Subset')),\n",
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    "plotdf = emberdf.copy()\n",
    "plotdf.loc[plotdf[\"appeared\"] < \"2018-01\", \"appeared\"] = \" <2018\"\n",
    "gbdf = plotdf.groupby([\"appeared\", \"label\"]).count().reset_index()\n",
    "alt.Chart(gbdf).mark_bar().encode(\n",
    "    alt.X('appeared:O', axis=alt.Axis(title='Month appeared')),\n",
    "    alt.Y('sum(sha256):Q', axis=alt.Axis(title='Number of samples')),\n",
    "    alt.Color('label:N', scale=alt.Scale(range=[\"#00b300\", \"#3333ff\", \"#ff3333\"]), legend=alt.Legend(values=[\"unlabeled\", \"benign\", \"malicious\"]))\n",
    ")"
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  {
   "cell_type": "code",
   "execution_count": 245,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\fahad\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1221: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LightGBM benchmark Classification reports score:                precision    recall  f1-score   support\n",
      "\n",
      "        -1.0       0.00      0.00      0.00      3294\n",
      "         0.0       0.89      0.95      0.92     93903\n",
      "         1.0       0.94      0.92      0.93    102803\n",
      "\n",
      "    accuracy                           0.92    200000\n",
      "   macro avg       0.61      0.62      0.62    200000\n",
      "weighted avg       0.90      0.92      0.91    200000\n",
      "\n",
      "LightGBM Model accuracy score: 0.9183\n",
      "Wall time: 14min 59s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "# build the new benchmark lightgbm model\n",
    "import lightgbm as lgb\n",
    "\n",
    "model = lgb.LGBMClassifier()\n",
    "model.fit(X_train, y_train)\n",
    "\n",
    "# predict the results\n",
    "prediction= model.predict(X_test)\n",
    "\n",
    "#evaluation of Classification report SCORE \n",
    "from sklearn.metrics import classification_report\n",
    "print('LightGBM benchmark Classification reports score: ',classification_report(prediction,y_test))\n",
    "#evaluation of accuracy SCORE \n",
    "from sklearn.metrics import accuracy_score\n",
    "accuracy=accuracy_score(prediction, y_test)\n",
    "print('LightGBM Model accuracy score: {0:0.4f}'.format(accuracy_score(prediction,y_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 246,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
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     "text": [
      "ROC AUC: 0.9222136496\n",
      "\n"
     ]
    }
   ],
   "source": [
    "bench_testpred = model.predict(X_test)\n",
    "bench_trainpred = model.predict(X_train)\n",
    "emberdf[\"bench_pred\"] = np.hstack((bench_trainpred, bench_testpred))\n",
    "benchdf = emberdf[emberdf[\"subset\"] == \"test\"]\n",
    "print(\"ROC AUC:\", roc_auc_score(benchdf.label, benchdf.bench_pred))\n",
    "print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 247,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ember Model Performance at 1% FPR:\n",
      "Threshold: 1.0000\n",
      "False Positive Rate: 0.000%\n",
      "False Negative Rate: 5.805%\n",
      "Detection Rate: 94.195%\n",
      "\n",
      "Ember Model Performance at 0.1% FPR:\n",
      "Threshold: 1.0000\n",
      "False Positive Rate: 0.000%\n",
      "False Negative Rate: 5.805%\n",
      "Detection Rate: 94.195%\n"
     ]
    }
   ],
   "source": [
    "def get_fpr(y_true, y_pred):\n",
    "    nbenign = (y_true == 0).sum()\n",
    "    nfalse = (y_pred[y_true == 0] == 1).sum()\n",
    "    return nfalse / float(nbenign)\n",
    "\n",
    "\n",
    "def find_threshold(y_true, y_pred, fpr_target):\n",
    "    thresh = 0.0\n",
    "    fpr = get_fpr(y_true, y_pred > thresh)\n",
    "    while fpr > fpr_target and thresh < 1.0:\n",
    "        thresh += 0.0001\n",
    "        fpr = get_fpr(y_true, y_pred > thresh)\n",
    "    return thresh, fpr\n",
    "\n",
    "\n",
    "threshold, fpr = find_threshold(benchdf.label, benchdf.bench_pred, 0.01)\n",
    "fnr = (benchdf.bench_pred[benchdf.label == 1] < threshold).sum() / float((benchdf.label == 1).sum())\n",
    "print(\"Ember Model Performance at 1% FPR:\")\n",
    "print(\"Threshold: {:.4f}\".format(threshold))\n",
    "print(\"False Positive Rate: {:.3f}%\".format(fpr * 100))\n",
    "print(\"False Negative Rate: {:.3f}%\".format(fnr * 100))\n",
    "print(\"Detection Rate: {}%\".format(100 - fnr * 100))\n",
    "print()\n",
    "\n",
    "threshold, fpr = find_threshold(benchdf.label, benchdf.bench_pred, 0.001)\n",
    "fnr = (benchdf.bench_pred[benchdf.label == 1] < threshold).sum() / float((benchdf.label == 1).sum())\n",
    "print(\"Ember Model Performance at 0.1% FPR:\")\n",
    "print(\"Threshold: {:.4f}\".format(threshold))\n",
    "print(\"False Positive Rate: {:.3f}%\".format(fpr * 100))\n",
    "print(\"False Negative Rate: {:.3f}%\".format(fnr * 100))\n",
    "print(\"Detection Rate: {}%\".format(100 - fnr * 100))"
   ]
  },
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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8, 8))\n",
    "fpr_plot1, tpr_plot1, _ = roc_curve(benchdf.label, benchdf.bench_pred)\n",
    "plt.plot(fpr_plot1, tpr_plot1, lw=4, color='r')\n",
    "plt.gca().set_xscale(\"log\")\n",
    "plt.yticks(np.arange(22) / 20.0)\n",
    "plt.xlim([6e-5, 1.0])\n",
    "plt.ylim([0.65, 1.01])\n",
    "plt.gca().grid(True)\n",
    "plt.vlines(fpr, 0, 1 - fnr, color=\"r\", lw=2)\n",
    "plt.hlines(1 - fnr, 0, fpr, color=\"r\", lw=2)\n",
    "plt.xlabel(\"False positive rate\")\n",
    "plt.ylabel(\"True positive rate\")\n",
    "_ = plt.title(\"LightGBM BenchMark ROC Curve\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ROC AUC: 0.9964289467999999\n",
      "\n",
      "Ember Model Performance at 1% FPR:\n",
      "Threshold: 0.8336\n",
      "False Positive Rate: 1.000%\n",
      "False Negative Rate: 3.502%\n",
      "Detection Rate: 96.498%\n",
      "\n",
      "Ember Model Performance at 0.1% FPR:\n",
      "Threshold: 0.9996\n",
      "False Positive Rate: 0.098%\n",
      "False Negative Rate: 13.192%\n",
      "Detection Rate: 86.80799999999999%\n",
      "Wall time: 1min 19s\n"
     ]
    }
   ],
   "source": [
    "# Old Model 2007\n",
    "%%time\n",
    "def get_fpr(y_true, y_pred):\n",
    "    nbenign = (y_true == 0).sum()\n",
    "    nfalse = (y_pred[y_true == 0] == 1).sum()\n",
    "    return nfalse / float(nbenign)\n",
    "\n",
    "\n",
    "def find_threshold(y_true, y_pred, fpr_target):\n",
    "    thresh = 0.0\n",
    "    fpr = get_fpr(y_true, y_pred > thresh)\n",
    "    while fpr > fpr_target and thresh < 1.0:\n",
    "        thresh += 0.0001\n",
    "        fpr = get_fpr(y_true, y_pred > thresh)\n",
    "    return thresh, fpr\n",
    "\n",
    "testdf = emberdf[emberdf[\"subset\"] == \"test\"]\n",
    "print(\"ROC AUC:\", roc_auc_score(testdf.label, testdf.y_pred))\n",
    "print()\n",
    "\n",
    "threshold, fpr = find_threshold(testdf.label, testdf.y_pred, 0.01)\n",
    "fnr = (testdf.y_pred[testdf.label == 1] < threshold).sum() / float((testdf.label == 1).sum())\n",
    "print(\"Ember Model Performance at 1% FPR:\")\n",
    "print(\"Threshold: {:.4f}\".format(threshold))\n",
    "print(\"False Positive Rate: {:.3f}%\".format(fpr * 100))\n",
    "print(\"False Negative Rate: {:.3f}%\".format(fnr * 100))\n",
    "print(\"Detection Rate: {}%\".format(100 - fnr * 100))\n",
    "print()\n",
    "\n",
    "threshold, fpr = find_threshold(testdf.label, testdf.y_pred, 0.001)\n",
    "fnr = (testdf.y_pred[testdf.label == 1] < threshold).sum() / float((testdf.label == 1).sum())\n",
    "print(\"Ember Model Performance at 0.1% FPR:\")\n",
    "print(\"Threshold: {:.4f}\".format(threshold))\n",
    "print(\"False Positive Rate: {:.3f}%\".format(fpr * 100))\n",
    "print(\"False Negative Rate: {:.3f}%\".format(fnr * 100))\n",
    "print(\"Detection Rate: {}%\".format(100 - fnr * 100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Old Model 2007\n",
    "plt.figure(figsize=(8, 8))\n",
    "fpr_plot, tpr_plot, _ = roc_curve(testdf.label, testdf.y_pred)\n",
    "plt.plot(fpr_plot, tpr_plot, lw=4, color='k')\n",
    "plt.gca().set_xscale(\"log\")\n",
    "plt.yticks(np.arange(22) / 20.0)\n",
    "plt.xlim([4e-5, 1.0])\n",
    "plt.ylim([0.65, 1.01])\n",
    "plt.gca().grid(True)\n",
    "plt.vlines(fpr, 0, 1 - fnr, color=\"r\", lw=2)\n",
    "plt.hlines(1 - fnr, 0, fpr, color=\"r\", lw=2)\n",
    "plt.xlabel(\"False positive rate\")\n",
    "plt.ylabel(\"True positive rate\")\n",
    "_ = plt.title(\"Ember Model ROC Curve\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Old Model 2007\n",
    "fig = plt.figure(figsize=(8, 8))\n",
    "testdf[testdf[\"label\"] == 0].y_pred.hist(range=[0, 1], bins=10, color=\"#3333ff\", alpha=0.8, label=\"benign\")\n",
    "testdf[testdf[\"label\"] == 1].y_pred.hist(range=[0, 1], bins=10, color=\"#ff3333\", alpha=0.8, label=\"malicious\")\n",
    "plt.gca().set_yscale(\"log\", nonposy=\"clip\")\n",
    "plt.gca().grid(False)\n",
    "plt.xlabel(\"Score\")\n",
    "plt.ylabel(\"Count\")\n",
    "_ = plt.title(\"Ember Test Set Model Score\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### trying to find the best parameters for Ember "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Bayesian Optimization Primer\n",
    "The problem with grid and random search is that these are uninformed methods because they do not use the past results from different values of hyperparameters in the objective function (remember the objective function takes in the hyperparameters and returns the model cross validation score). We record the results of the objective function for each set of hyperparameters, but the algorithms do not select the next hyperparameter values from this information. Intuitively, if we have the past results, we should use them to reason about what hyperparameter values work the best and choose the next values wisely to try and spend more iterations evaluating promising values. Evaluating hyperparameters in the objective function is very time-consuming, and the concept of Bayesian optimization is to limit calls to the evaluation function by choosing the next hyperparameter values based on the previous results. This allows the algorithm to spend more time evaluating promising hyperparameter values and less time in low-scoring regions of the hyperparameter space\n",
    "\n",
    "\n",
    "Bayesian optimization works by building a surrogate function (in the form of a probability model) of the objective function  P(score|hyperparameters . The surrogate function is much cheaper to evaluate than the objective, so the algorithm chooses the next values to try in the objective based on maximizing a criterion on the surrogate (usually expected improvement).\n",
    "\n",
    "The surrogate function is based on past evaluation results - pairs of (score, hyperparameter) records - and is continually updated with each objective function evaluation. Bayesian optimization therefore uses Bayesian reasoning: form an initial model (called a prior) and then update it with more evidence. The idea is that as the data accumulates, the surrogate function gets closer and closer to the objective function, and the hyperparameter values that are the best in the surrogate function will also do the best in the objective function. Bayesian optimization methods differ in the algorithm used to build the surrogate function and choose the next hyperparameter values to try. Some of the common choices are Gaussian Process (implemented in Spearmint), Random Forest Regression (in SMAC), and the Tree Parzen Estimator (TPE) in Hyperopt."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Four Part of Bayesian Optimization\n",
    "Bayesian hyperparameter optimization requires the same four parts as we implemented in grid and random search:\n",
    "\n",
    "Objective Function: takes in an input (hyperparameters) and returns a score to minimize or maximize (the cross validation score)\n",
    "Domain space: the range of input values (hyperparameters) to evaluate\n",
    "Optimization Algorithm: the method used to construct the surrogate function and choose the next values to evaluate\n",
    "Results: score, value pairs that the algorithm uses to build the surrogate function\n",
    "The only differences are that now our objective function will return a score to minimize (this is just convention in the field of optimization), our domain space will be probability distributions rather than a hyperparameter grid, and the optimization algorithm will be an informed method that uses past results to choose the next hyperparameter values to evaluate."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Hyperopt\n",
    "Hyperopt is an open-source Python library the implements Bayesian Optimization using the Tree Parzen Estimator algorithm to construct the surrogate function and select the next hyperparameter values to evaluate in the objective function. There are a number of other libraries such as Spearmint (Guassian process surrogate function) and SMAC (random forest regression surrogate function) sharing the same problem structure. The four parts of an optimization problem that we develop here will apply to all the libraries with only a change in syntax. Morevoer, the optimization methods as applied to the Gradient Boosting Machine will translate to other machine learning models or any problem where we have to minimize a function."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Gradient Boosting Machine\n",
    "We will use the gradient booosting machine (GBM) as our model to tune in the LightGBM library.\n",
    "\n",
    "\n",
    "#### Cross Validation with Early Stopping\n",
    "As with random and grid search, we will evaluate each set of hyperparameters using 3 fold cross validation on the training data. The GBM model will be trained with early stopping, where estimators are added to the ensemble until the validation score has not decrease for 100 iterations (estimators added).\n",
    "\n",
    "Cross validation and early stopping will be implemented using the LightGBM cv function. We will use 3 folds and 100 early stopping rounds."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Data manipulation\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "# Modeling\n",
    "import lightgbm as lgb\n",
    "\n",
    "# Evaluation of the model\n",
    "from sklearn.metrics import roc_auc_score\n",
    "\n",
    "# Visualization\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "plt.rcParams['font.size'] = 18\n",
    "%matplotlib inline\n",
    "\n",
    "# Governing choices for search\n",
    "N_FOLDS = 3\n",
    "MAX_EVALS = 3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### The code below reads in the data and creates a training and a set for testing. We can only use the training data a single time when we evaluate the final model. Hyperparameter tuning must be done on the training data using cross validation!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training data shape:  (800000, 2381)\n",
      "Testing data shape:  (200000, 2381)\n",
      "Wall time: 2.46 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "emberdf = ember.read_metadata(data_dir)\n",
    "X_train, y_train, X_test, y_test = ember.read_vectorized_features(data_dir)\n",
    "lgbm_model = lgb.Booster(model_file=os.path.join(data_dir, \"ember_model_2018.txt\"))\n",
    "\n",
    "print('Training data shape: ', X_train.shape)\n",
    "print('Testing data shape: ', X_test.shape)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Baseline Model\n",
    "First we can create a model with the default value of hyperparameters and score it using cross validation with early stopping. Using the cv LightGBM function requires creating a Dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = lgb.LGBMClassifier()\n",
    "\n",
    "# Training set\n",
    "train_set = lgb.Dataset(X_train, label = y_train)\n",
    "test_set = lgb.Dataset(X_test, label = y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\fahad\\anaconda3\\lib\\site-packages\\lightgbm\\basic.py:842: UserWarning: silent keyword has been found in `params` and will be ignored.\n",
      "Please use silent argument of the Dataset constructor to pass this parameter.\n",
      "  .format(key))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The maximium ROC AUC in cross validation was 0.892 with std of 0.001.\n",
      "The ideal number of iterations was 757.\n"
     ]
    }
   ],
   "source": [
    "# Default hyperparamters\n",
    "hyperparameters = model.get_params()\n",
    "\n",
    "# Using early stopping to determine number of estimators.\n",
    "del hyperparameters['n_estimators']\n",
    "\n",
    "# Perform cross validation with early stopping\n",
    "cv_results = lgb.cv(hyperparameters, train_set, num_boost_round = 10000, nfold = N_FOLDS, metrics = 'auc', \n",
    "           early_stopping_rounds = 100, verbose_eval = False, seed = 42)\n",
    "\n",
    "# Highest score\n",
    "best = cv_results['auc-mean'][-1]\n",
    "\n",
    "# Standard deviation of best score\n",
    "best_std = cv_results['auc-stdv'][-1]\n",
    "\n",
    "print('The maximium ROC AUC in cross validation was {:.3f} with std of {:.3f}.'.format(best, best_std))\n",
    "print('The ideal number of iterations was {}.'.format(len(cv_results['auc-mean'])))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Now we can evaluate the baseline model on the testing data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The baseline model scores 0.010 ROC AUC on the test set.\n",
      "Wall time: 58min 37s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "# Optimal number of esimators found in cv\n",
    "model.n_estimators = len(cv_results['auc-mean'])\n",
    "\n",
    "# Train and make predicions with model\n",
    "model.fit(X_train, y_train)\n",
    "preds = model.predict_proba(X_test)[:, 1]\n",
    "baseline_auc = roc_auc_score(y_test, preds)\n",
    "\n",
    "print('The baseline model scores {:.3f} ROC AUC on the test set.'.format(baseline_auc))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Objective Function\n",
    "The first part to write is the objective function which takes in a set of hyperparameter values and returns the cross validation score on the training data. An objective function in Hyperopt must return either a single real value to minimize, or a dictionary with a key \"loss\" with the score to minimize (and a key \"status\" indicating if the run was successful or not).\n",
    "\n",
    "## Optimization \n",
    "\n",
    "is typically about minimizing a value, and because our metric is Receiver Operating Characteristic Area Under the Curve (ROC AUC) where higher is better, the objective function will return  1−ROC AUC Cross Validation . The algorithm will try to drive this value as low as possible (raising the ROC AUC) by choosing the next hyperparameters based on the past results.\n",
    "\n",
    "The complete objective function is shown below. Will be written to a csv file on each call of the function in order to track results as the search progress and so we have a saved record of the search. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 1.96 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "import csv\n",
    "from hyperopt import STATUS_OK\n",
    "from timeit import default_timer as timer\n",
    "\n",
    "def objective(hyperparameters):\n",
    "    \"\"\"Objective function for Gradient Boosting Machine Hyperparameter Optimization.\n",
    "       Writes a new line to `outfile` on every iteration\"\"\"\n",
    "    \n",
    "    # Keep track of evals\n",
    "    global ITERATION\n",
    "    \n",
    "    ITERATION += 1\n",
    "    \n",
    "    # Using early stopping to find number of trees trained\n",
    "    if 'n_estimators' in hyperparameters:\n",
    "        del hyperparameters['n_estimators']\n",
    "    \n",
    "    # Retrieve the subsample\n",
    "    subsample = hyperparameters['boosting_type'].get('subsample', 1.0)\n",
    "    \n",
    "    # Extract the boosting type and subsample to top level keys\n",
    "    hyperparameters['boosting_type'] = hyperparameters['boosting_type']['boosting_type']\n",
    "    hyperparameters['subsample'] = subsample\n",
    "    \n",
    "    # Make sure parameters that need to be integers are integers\n",
    "    for parameter_name in ['num_leaves', 'subsample_for_bin', 'min_child_samples']:\n",
    "        hyperparameters[parameter_name] = int(hyperparameters[parameter_name])\n",
    "\n",
    "    start = timer()\n",
    "    \n",
    "    # Perform n_folds cross validation\n",
    "    cv_results = lgb.cv(hyperparameters, train_set, num_boost_round = 10000, nfold = N_FOLDS, \n",
    "                        early_stopping_rounds = 100, metrics = 'auc', seed = 50)\n",
    "\n",
    "    run_time = timer() - start\n",
    "    \n",
    "    # Extract the best score\n",
    "    best_score = cv_results['auc-mean'][-1]\n",
    "    \n",
    "    # Loss must be minimized\n",
    "    loss = 1 - best_score\n",
    "    \n",
    "    # Boosting rounds that returned the highest cv score\n",
    "    n_estimators = len(cv_results['auc-mean'])\n",
    "    \n",
    "    # Add the number of estimators to the hyperparameters\n",
    "    hyperparameters['n_estimators'] = n_estimators\n",
    "\n",
    "    # Write to the csv file ('a' means append)\n",
    "    of_connection = open(OUT_FILE, 'a')\n",
    "    writer = csv.writer(of_connection)\n",
    "    writer.writerow([loss, hyperparameters, ITERATION, run_time, best_score])\n",
    "    of_connection.close()\n",
    "\n",
    "    # Dictionary with information for evaluation\n",
    "    return {'loss': loss, 'hyperparameters': hyperparameters, 'iteration': ITERATION,\n",
    "            'train_time': run_time, 'status': STATUS_OK}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Domain\n",
    "Specifying the domain (called the space in Hyperopt) is a little trickier than in grid search. In Hyperopt, and other Bayesian optimization frameworks, the domian is not a discrete grid but instead has probability distributions for each hyperparameter. For each hyperparameter, we will use the same limits as with the grid, but instead of being defined at each point, the domain represents probabilities for each hyperparameter. This will probably become clearer in the code and the images!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "from hyperopt import hp\n",
    "from hyperopt.pyll.stochastic import sample"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### First we will go through an example of the learning rate. We are using a log-uniform space for the learning rate defined from 0.005 to 0.5. The log - uniform distribution has the values evenly placed in logarithmic space rather than linear space. This is useful for variables that differ over several orders of magnitude such as the learning rate. For example, with a log-uniform distribution, there will be an equal chance of drawing a value from 0.005 to 0.05 and from 0.05 to 0.5 (in linear space far more values would be drawn from the later since the linear distance is much larger. The logarithmic space is exactly the same - a factor of 10)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 1 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "# Create the learning rate\n",
    "learning_rate = {'learning_rate': hp.loguniform('learning_rate', np.log(0.005), np.log(0.2))}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### We can visualize the learning rate by drawing 10000 samples from the distribution."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 3.48 s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Density')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%time\n",
    "learning_rate_dist = []\n",
    "\n",
    "# Draw 10000 samples from the learning rate domain\n",
    "for _ in range(10000):\n",
    "    learning_rate_dist.append(sample(learning_rate)['learning_rate'])\n",
    "    \n",
    "plt.figure(figsize = (8, 6))\n",
    "sns.kdeplot(learning_rate_dist, color = 'red', linewidth = 2, shade = True);\n",
    "plt.title('Learning Rate Distribution', size = 18); plt.xlabel('Learning Rate', size = 16); plt.ylabel('Density', size = 16);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### The number of leaves on the other hand is a discrete uniform distribution."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 3.65 s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Density')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%time\n",
    "# Discrete uniform distribution\n",
    "num_leaves = {'num_leaves': hp.quniform('num_leaves', 128, 512, 1)}\n",
    "num_leaves_dist = []\n",
    "\n",
    "# Sample 10000 times from the number of leaves distribution\n",
    "for _ in range(10000):\n",
    "    num_leaves_dist.append(sample(num_leaves)['num_leaves'])\n",
    "    \n",
    "# kdeplot\n",
    "plt.figure(figsize = (8, 6))\n",
    "sns.kdeplot(num_leaves_dist, linewidth = 2, shade = True);\n",
    "plt.title('Number of Leaves Distribution', size = 18); plt.xlabel('Number of Leaves', size = 16); plt.ylabel('Density', size = 16);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Conditional Domain¶\n",
    "In Hyperopt, we can use nested conditional statements to indicate hyperparameters that depend on other hyperparameters. For example, the \"goss\" boosting_type cannot use subsampling, so when we set up the boosting_type categorical variable, we have to set the subsample to 1.0 while for the other boosting types it's a float between 0.5 and 1.0."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 999 µs\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'boosting_type': {'boosting_type': 'goss', 'subsample': 1.0}}"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "# boosting type domain \n",
    "boosting_type = {'boosting_type': hp.choice('boosting_type', \n",
    "                                            [{'boosting_type': 'gbdt', 'subsample': hp.uniform('subsample', 0.5, 1)}, \n",
    "                                             \n",
    "                                             {'boosting_type': 'goss', 'subsample': 1.0}])}\n",
    "\n",
    "# Draw a sample\n",
    "hyperparams = sample(boosting_type)\n",
    "hyperparams"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### We need to set both the boosting_type and subsample as top-level keys in the parameter dictionary. We can use the Python dict.get method with a default value of 1.0. This means that if the key is not present in the dictionary, the value returned will be the default (1.0). One aspect to note is that if boosting_type is goss, then we cannot use subsample (which refers to training on only a fraction of the rows in the training data, a technique known as stochastic gradient boosting). Therefore, we will need a line of logic in our algorithm that sets the subsample to 1.0 (which means use all the rows) if boosting_type=goss. As an example below, if we randomly select a set of hyperparameters, and the boosting type is \"goss\", then we set the subsample to 1.0."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 0 ns\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'boosting_type': 'goss', 'subsample': 1.0}"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "# Retrieve the subsample if present otherwise set to 1.0\n",
    "subsample = hyperparams['boosting_type'].get('subsample', 1.0)\n",
    "\n",
    "# Extract the boosting type\n",
    "hyperparams['boosting_type'] = hyperparams['boosting_type']['boosting_type']\n",
    "hyperparams['subsample'] = subsample\n",
    "\n",
    "hyperparams"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Usage of nested dictionary with GBM\n",
    "The gbm cannot use the nested dictionary so we need to set the boosting_type and subsample as top level keys.\n",
    "Nested conditionals allow us to use a different set of hyperparameters depending on other hyperparameters. For example, we can explore different models with completely different sets of hyperparameters by using nested conditionals. The only requirement is that the first nested statement must be based on a choice hyperparameter (the choice could be the type of model)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Complete Bayesian Domain¶\n",
    "Now we can define the entire domain. Each variable needs to have a label and a few parameters specifying the type and extent of the distribution. For the variables such as boosting type that are categorical, we use the choice variable. Other variables types include quniform, loguniform, and uniform. For the complete list, check out the documentation for Hyperopt. Altogether there are 12 hyperparameters to optimize."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 999 µs\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "# Define the search space\n",
    "space = {'boosting_type': hp.choice('boosting_type', \n",
    "                                            [{'boosting_type': 'gbdt', 'subsample': hp.uniform('gdbt_subsample', 0.5, 1)}, \n",
    "                                             {'boosting_type': 'goss', 'subsample': 1.0}]),\n",
    "    \n",
    "    'num_leaves': hp.quniform('num_leaves', 128, 512, 1),\n",
    "    'learning_rate': hp.loguniform('learning_rate', np.log(0.01), np.log(0.5)),\n",
    "    'subsample_for_bin': hp.quniform('subsample_for_bin', 20000, 200000, 20000),\n",
    "    'min_child_samples': hp.quniform('min_child_samples', 20, 500, 5),\n",
    "    'reg_alpha': hp.uniform('reg_alpha', 0.0, 1.0),\n",
    "    'reg_lambda': hp.uniform('reg_lambda', 0.0, 1.0),\n",
    "    'colsample_bytree': hp.uniform('colsample_by_tree', 0.6, 1.0),\n",
    "    'feature_fraction': hp.uniform('feature_fraction',0.5, 1.0),\n",
    "    'bagging_fraction': hp.uniform('bagging_fraction',0.5, 1.0),\n",
    "    'is_unbalance': hp.choice('is_unbalance', [True, False]),\n",
    "}\n",
    "space['objective'] = 'binary'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Example of Sampling from the Domain\n",
    "Let's sample from the domain (using the conditional logic) to see the result of each draw. Every time we run this code, the results will change. (Again notice that we need to assign the top level keys to the keywords understood by the GBM)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 2 ms\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'bagging_fraction': 0.6911658966360053,\n",
       " 'boosting_type': 'gbdt',\n",
       " 'colsample_bytree': 0.8422785210667557,\n",
       " 'feature_fraction': 0.5455319434563135,\n",
       " 'is_unbalance': True,\n",
       " 'learning_rate': 0.017667812610384397,\n",
       " 'min_child_samples': 85.0,\n",
       " 'num_leaves': 348.0,\n",
       " 'objective': 'binary',\n",
       " 'reg_alpha': 0.30431723931084653,\n",
       " 'reg_lambda': 0.9983264066002596,\n",
       " 'subsample_for_bin': 120000.0,\n",
       " 'subsample': 0.5909881711074163}"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "# Sample from the full space\n",
    "x = sample(space)\n",
    "\n",
    "# Conditional logic to assign top-level keys\n",
    "subsample = x['boosting_type'].get('subsample', 1.0)\n",
    "x['boosting_type'] = x['boosting_type']['boosting_type']\n",
    "x['subsample'] = subsample\n",
    "\n",
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 3 ms\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'bagging_fraction': 0.8455773567952245,\n",
       " 'boosting_type': 'goss',\n",
       " 'colsample_bytree': 0.9502488254365788,\n",
       " 'feature_fraction': 0.5196826972996631,\n",
       " 'is_unbalance': True,\n",
       " 'learning_rate': 0.17967629182674497,\n",
       " 'min_child_samples': 470.0,\n",
       " 'num_leaves': 474.0,\n",
       " 'objective': 'binary',\n",
       " 'reg_alpha': 0.36875270069519406,\n",
       " 'reg_lambda': 0.3381444848009504,\n",
       " 'subsample_for_bin': 200000.0,\n",
       " 'subsample': 1.0}"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "x = sample(space)\n",
    "subsample = x['boosting_type'].get('subsample', 1.0)\n",
    "x['boosting_type'] = x['boosting_type']['boosting_type']\n",
    "x['subsample'] = subsample\n",
    "x"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Let's test the objective function with the domain to make sure it works. (Every time the of_connection line is run, the outfile will be overwritten, so use a different name for each trial to save the results.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The cross validation loss = 0.102.\n",
      "The optimal number of estimators was 827.\n",
      "Wall time: 14h 41min 29s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "# Create a new file and open a connection\n",
    "OUT_FILE = 'bayes_test.csv'\n",
    "of_connection = open(OUT_FILE, 'w')\n",
    "writer = csv.writer(of_connection)\n",
    "\n",
    "ITERATION = 0\n",
    "\n",
    "# Write column names\n",
    "headers = ['loss', 'hyperparameters', 'iteration', 'runtime', 'score']\n",
    "writer.writerow(headers)\n",
    "of_connection.close()\n",
    "\n",
    "# Test the objective function\n",
    "results = objective(sample(space))\n",
    "print('The cross validation loss = {:.3f}.'.format(results['loss']))\n",
    "print('The optimal number of estimators was {}.'.format(results['hyperparameters']['n_estimators']))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  Optimization Algorithm\n",
    "The optimization algorithm is the method for constructing the surrogate function (probability model) and selecting the next set of hyperparameters to evaluate in the objective function. Hyperopt has two choices: random search and Tree Parzen Estimator.\n",
    "\n",
    "The technical details of TPE can be found in this article(https://papers.nips.cc/paper/4443-algorithms-for-hyper-parameter-optimization.pdf) and a conceptual explanation is in this article(https://towardsdatascience.com/a-conceptual-explanation-of-bayesian-model-based-hyperparameter-optimization-for-machine-learning-b8172278050f). Although this is the most technical part of Bayesian hyperparameter optimization, defining the algorithm in Hyperopt is simple."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "from hyperopt import tpe\n",
    "\n",
    "# Create the algorithm\n",
    "tpe_algorithm = tpe.suggest"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Results History\n",
    "The final part is the history of objective function evaluations. Although Hyperopt internally keeps track of the results for the algorithm to use, if we want to monitor the results and have a saved copy of the search, we need to store the results ourselves. Here, we are using two methods to make sure we capture all the results:\n",
    "\n",
    "A Trials object that stores the dictionary returned from the objective function\n",
    "Adding a line to a csv file every iteration.\n",
    "The csv file option also lets us monitor the results of an on-going experiment. Also, do not use Excel to open the file while training is on-going. Instead check the results using tail results/out_file.csv from bash or open the file in Sublime Text or Notepad."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 3.01 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "from hyperopt import Trials\n",
    "\n",
    "# Record results\n",
    "trials = Trials()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### The Trials object will hold everything returned from the objective function in the .results attribute. We can use this after the search is complete to inspect the results, but an easier method is to read in the csv file because that will already be in a dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 2 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "# Create a file and open a connection\n",
    "OUT_FILE = 'bayes_test.csv'\n",
    "of_connection = open(OUT_FILE, 'w')\n",
    "writer = csv.writer(of_connection)\n",
    "\n",
    "ITERATION = 0\n",
    "\n",
    "# Write column names\n",
    "headers = ['loss', 'hyperparameters', 'iteration', 'runtime', 'score']\n",
    "writer.writerow(headers)\n",
    "of_connection.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Automated Hyperparameter Optimization in Practice\n",
    "We have all four parts we need to run the optimization. To run Bayesian optimization we use the fmin function (a good reminder that we need a metric to minimize!)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "from hyperopt import fmin"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### fmin takes the four parts defined above as well as the maximum number of iterations max_evals."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100%|██████████| 3/3 [13:37:33<00:00, 16351.28s/trial, best loss: 0.10217363841527549] \n",
      "Wall time: 13h 37min 33s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'bagging_fraction': 0.8770069068874948,\n",
       " 'boosting_type': 0,\n",
       " 'colsample_by_tree': 0.9124936068013215,\n",
       " 'feature_fraction': 0.8195246637142595,\n",
       " 'gdbt_subsample': 0.826451839298559,\n",
       " 'is_unbalance': 1,\n",
       " 'learning_rate': 0.018037010899059618,\n",
       " 'min_child_samples': 230.0,\n",
       " 'num_leaves': 257.0,\n",
       " 'reg_alpha': 0.0714859828593909,\n",
       " 'reg_lambda': 0.3604398797530104,\n",
       " 'subsample_for_bin': 140000.0}"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "# Global variable\n",
    "global  ITERATION\n",
    "\n",
    "ITERATION = 0\n",
    "\n",
    "# Run optimization\n",
    "best = fmin(fn = objective, space = space, algo = tpe.suggest, trials = trials,\n",
    "            max_evals = MAX_EVALS)\n",
    "\n",
    "best"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### The best object holds only the hyperparameters that returned the lowest loss in the objective function. Although this is ultimately what we are after, if we want to understand how the search progresses, we need to inspect the Trials object or the csv file. For example, we can sort the results returned from the objective function by the lowest loss:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 0 ns\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[{'loss': 0.10217363841527549,\n",
       "  'hyperparameters': {'bagging_fraction': 0.8770069068874948,\n",
       "   'boosting_type': 'gbdt',\n",
       "   'colsample_bytree': 0.9124936068013215,\n",
       "   'feature_fraction': 0.8195246637142595,\n",
       "   'is_unbalance': False,\n",
       "   'learning_rate': 0.018037010899059618,\n",
       "   'min_child_samples': 230,\n",
       "   'num_leaves': 257,\n",
       "   'objective': 'binary',\n",
       "   'reg_alpha': 0.0714859828593909,\n",
       "   'reg_lambda': 0.3604398797530104,\n",
       "   'subsample_for_bin': 140000,\n",
       "   'subsample': 0.826451839298559,\n",
       "   'n_estimators': 1249},\n",
       "  'iteration': 3,\n",
       "  'train_time': 31144.3582952,\n",
       "  'status': 'ok'}]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "# Sort the trials with lowest loss (highest AUC) first\n",
    "trials_dict = sorted(trials.results, key = lambda x: x['loss'])\n",
    "trials_dict[:1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### An easier method is to read in the csv file since this will be a dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 150 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "results = pd.read_csv(OUT_FILE)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### The function below takes in the results, trains a model on the training data, and evalutes on the testing data. It returns a dataframe of hyperparameters from the search.\n",
    "\n",
    "## Saving the results :\n",
    "Saving the results to a csv file converts the dictionary of hyperparameters to a string. We need to map this back to a dictionary using ast.literal_eval."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 0 ns\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "import ast\n",
    "\n",
    "def evaluate(results, name):\n",
    "    \"\"\"Evaluate model on test data using hyperparameters in results\n",
    "       Return dataframe of hyperparameters\"\"\"\n",
    "    \n",
    "    new_results = results.copy()\n",
    "    # String to dictionary\n",
    "    new_results['hyperparameters'] = new_results['hyperparameters'].map(ast.literal_eval)\n",
    "    \n",
    "    # Sort with best values on top\n",
    "    new_results = new_results.sort_values('score', ascending = False).reset_index(drop = True)\n",
    "    \n",
    "    # Print out cross validation high score\n",
    "    print('The highest cross validation score from {} was {:.3f} found on iteration {}.'.format(name, new_results.loc[0, 'score'], new_results.loc[0, 'iteration']))\n",
    "    \n",
    "    # Use best hyperparameters to create a model\n",
    "    hyperparameters = new_results.loc[0, 'hyperparameters']\n",
    "    model = lgb.LGBMClassifier(**hyperparameters)\n",
    "    \n",
    "    # Train and make predictions\n",
    "    model.fit(X_train, y_train)\n",
    "    preds = model.predict_proba(X_test)[:, 1]\n",
    "    \n",
    "    print('ROC AUC from {} on test data = {:.3f}.'.format(name, roc_auc_score(y_test, preds)))\n",
    "    \n",
    "    # Create dataframe of hyperparameters\n",
    "    hyp_df = pd.DataFrame(columns = list(new_results.loc[0, 'hyperparameters'].keys()))\n",
    "\n",
    "    # Iterate through each set of hyperparameters that were evaluated\n",
    "    for i, hyp in enumerate(new_results['hyperparameters']):\n",
    "        hyp_df = hyp_df.append(pd.DataFrame(hyp, index = [0]), \n",
    "                               ignore_index = True)\n",
    "        \n",
    "    # Put the iteration and score in the hyperparameter dataframe\n",
    "    hyp_df['iteration'] = new_results['iteration']\n",
    "    hyp_df['score'] = new_results['score']\n",
    "    \n",
    "    return hyp_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The highest cross validation score from Bayesian was 0.898 found on iteration 3.\n",
      "ROC AUC from Bayesian on test data = 0.009.\n",
      "Wall time: 4h 3min 23s\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>bagging_fraction</th>\n",
       "      <th>boosting_type</th>\n",
       "      <th>colsample_bytree</th>\n",
       "      <th>feature_fraction</th>\n",
       "      <th>is_unbalance</th>\n",
       "      <th>learning_rate</th>\n",
       "      <th>min_child_samples</th>\n",
       "      <th>num_leaves</th>\n",
       "      <th>objective</th>\n",
       "      <th>reg_alpha</th>\n",
       "      <th>reg_lambda</th>\n",
       "      <th>subsample_for_bin</th>\n",
       "      <th>subsample</th>\n",
       "      <th>n_estimators</th>\n",
       "      <th>iteration</th>\n",
       "      <th>score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.877007</td>\n",
       "      <td>gbdt</td>\n",
       "      <td>0.912494</td>\n",
       "      <td>0.819525</td>\n",
       "      <td>False</td>\n",
       "      <td>0.018037</td>\n",
       "      <td>230</td>\n",
       "      <td>257</td>\n",
       "      <td>binary</td>\n",
       "      <td>0.071486</td>\n",
       "      <td>0.360440</td>\n",
       "      <td>140000</td>\n",
       "      <td>0.826452</td>\n",
       "      <td>1249</td>\n",
       "      <td>3</td>\n",
       "      <td>0.897826</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.714817</td>\n",
       "      <td>gbdt</td>\n",
       "      <td>0.791416</td>\n",
       "      <td>0.974536</td>\n",
       "      <td>False</td>\n",
       "      <td>0.049888</td>\n",
       "      <td>150</td>\n",
       "      <td>272</td>\n",
       "      <td>binary</td>\n",
       "      <td>0.436647</td>\n",
       "      <td>0.273901</td>\n",
       "      <td>80000</td>\n",
       "      <td>0.650350</td>\n",
       "      <td>557</td>\n",
       "      <td>1</td>\n",
       "      <td>0.897601</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.842160</td>\n",
       "      <td>gbdt</td>\n",
       "      <td>0.779292</td>\n",
       "      <td>0.841787</td>\n",
       "      <td>True</td>\n",
       "      <td>0.169801</td>\n",
       "      <td>220</td>\n",
       "      <td>194</td>\n",
       "      <td>binary</td>\n",
       "      <td>0.309812</td>\n",
       "      <td>0.630685</td>\n",
       "      <td>160000</td>\n",
       "      <td>0.847914</td>\n",
       "      <td>187</td>\n",
       "      <td>2</td>\n",
       "      <td>0.897225</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   bagging_fraction boosting_type  colsample_bytree  feature_fraction  \\\n",
       "0          0.877007          gbdt          0.912494          0.819525   \n",
       "1          0.714817          gbdt          0.791416          0.974536   \n",
       "2          0.842160          gbdt          0.779292          0.841787   \n",
       "\n",
       "  is_unbalance  learning_rate min_child_samples num_leaves objective  \\\n",
       "0        False       0.018037               230        257    binary   \n",
       "1        False       0.049888               150        272    binary   \n",
       "2         True       0.169801               220        194    binary   \n",
       "\n",
       "   reg_alpha  reg_lambda subsample_for_bin  subsample n_estimators  iteration  \\\n",
       "0   0.071486    0.360440            140000   0.826452         1249          3   \n",
       "1   0.436647    0.273901             80000   0.650350          557          1   \n",
       "2   0.309812    0.630685            160000   0.847914          187          2   \n",
       "\n",
       "      score  \n",
       "0  0.897826  \n",
       "1  0.897601  \n",
       "2  0.897225  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "bayes_results = evaluate(results, name = 'Bayesian')\n",
    "bayes_results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Continue Optimization¶\n",
    "Hyperopt can continue searching where a previous search left off if we pass in a Trials object that already has results. The algorithms used in Bayesian optimization are black-box optimizers because they have no internal state. All they need is the previous results of objective function evaluations (the input values and loss) and they can build up the surrogate function and select the next values to evaluate in the objective function. This means that any search can be continued as long as we have the history in a Trials object."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3trial [00:00, 2996.64trial/s, best loss=?]\n",
      "Wall time: 91.7 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "MAX_EVALS = 3\n",
    "\n",
    "# Continue training\n",
    "best = fmin(fn = objective, space = space, algo = tpe.suggest, trials = trials,\n",
    "            max_evals = MAX_EVALS)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### To save the Trials object so it can be read in later for more training, we can use the json format."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 1 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "import json\n",
    "\n",
    "# Save the trial results\n",
    "with open('trials.json', 'w') as f:\n",
    "    f.write(json.dumps(trials_dict))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### To start the training from where it left off, simply load in the Trials object and pass it to an instance of fmin. (You might even be able to tweak the hyperparameter distribution and continue searching with the Trials object because the algorithm does not maintain an internal state."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Next Steps\n",
    "Now that we have developed all the necessary parts for automated hyperparameter tuning using Bayesian optimization, we can apply these to any dataset or any machine learning method. The functions taken here can be put in a script and run a full dataset. Next, we will go through results from 100 evaluations on  the dataset to see how the search progresses."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### I will continue running the Bayesian Hyperparameter optimization for 100 iterations below"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100%|██████████| 100/100 [671:11:07<00:00, 24162.68s/trial, best loss: 0.10183586177746706]   \n",
      "Finished, best results\n",
      "[{'loss': 0.10183586177746706, 'hyperparameters': {'bagging_fraction': 0.9354093507612627, 'boosting_type': 'gbdt', 'colsample_bytree': 0.8959538632213808, 'feature_fraction': 0.7800657948373264, 'is_unbalance': True, 'learning_rate': 0.02469412244118082, 'min_child_samples': 145, 'num_leaves': 229, 'objective': 'binary', 'reg_alpha': 0.9880250400277432, 'reg_lambda': 0.4516852511612135, 'subsample_for_bin': 160000, 'subsample': 0.6300497061437562, 'n_estimators': 1227}, 'iteration': 26, 'train_time': 20919.2980698999, 'status': 'ok'}]\n",
      "Wall time: 27d 23h 11min 7s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "MAX_EVALS = 100\n",
    "\n",
    "# Create a new file and open a connection\n",
    "OUT_FILE = 'bayesian_trials_100.csv'\n",
    "of_connection = open(OUT_FILE, 'w')\n",
    "writer = csv.writer(of_connection)\n",
    "\n",
    "# Write column names\n",
    "headers = ['loss', 'hyperparameters', 'iteration', 'runtime', 'score']\n",
    "writer.writerow(headers)\n",
    "of_connection.close()\n",
    "\n",
    "# Record results\n",
    "trials = Trials()\n",
    "\n",
    "global ITERATION\n",
    "\n",
    "ITERATION = 0 \n",
    "\n",
    "best = fmin(fn = objective, space = space, algo = tpe.suggest,\n",
    "            trials = trials, max_evals = MAX_EVALS)\n",
    "\n",
    "# Sort the trials with lowest loss (highest AUC) first\n",
    "trials_dict = sorted(trials.results, key = lambda x: x['loss'])\n",
    "\n",
    "print('Finished, best results')\n",
    "print(trials_dict[:1])\n",
    "\n",
    "#Save the trial results\n",
    "with open('trials.json', 'w') as f:\n",
    "    f.write(json.dumps(trials_dict))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Search Results\n",
    "Next we will go through the results from 100 search iterations on the dataset. We will look at the scores, the distribution of hyperparameter values tried, the evolution of values over time.\n",
    "\n",
    "After examining the search results, we will use the optimized hyperparameters to make predictions on a the dataset. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The highest cross validation score from Bayesian was 0.898 found on iteration 26.\n",
      "ROC AUC from Bayesian on test data = 0.008.\n",
      "The highest cross validation score from random was 0.898 found on iteration 60.\n",
      "ROC AUC from random on test data = 0.009.\n",
      "Wall time: 11h 17min 14s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "bayes_results = pd.read_csv(r'C:\\Users\\fahad\\bayesian_trials_100.csv').sort_values('score', ascending = False).reset_index()\n",
    "random_results = pd.read_csv(r'C:\\Users\\fahad\\random_search_trials_100.csv').sort_values('score', ascending = False).reset_index()\n",
    "random_results['loss'] = 1 - random_results['score']\n",
    "bayes_params = evaluate(bayes_results, name = 'Bayesian')\n",
    "random_params = evaluate(random_results, name = 'random')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### We can see that the Bayesian search and Random Search are identical in cross validation, however Bayesian search found better hyperparameteres in the test set. We can get all the scores in a dataframe in order to plot them over the course of training."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 4 ms\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ROC AUC</th>\n",
       "      <th>iteration</th>\n",
       "      <th>search</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.897746</td>\n",
       "      <td>60</td>\n",
       "      <td>Random</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.897669</td>\n",
       "      <td>5</td>\n",
       "      <td>Random</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.897668</td>\n",
       "      <td>54</td>\n",
       "      <td>Random</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.897641</td>\n",
       "      <td>42</td>\n",
       "      <td>Random</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.897570</td>\n",
       "      <td>88</td>\n",
       "      <td>Random</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    ROC AUC  iteration  search\n",
       "0  0.897746         60  Random\n",
       "1  0.897669          5  Random\n",
       "2  0.897668         54  Random\n",
       "3  0.897641         42  Random\n",
       "4  0.897570         88  Random"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "# Dataframe of just scores\n",
    "\n",
    "scores1 = pd.DataFrame({'ROC AUC': bayes_params['score'], 'iteration': bayes_params['iteration'], 'search': 'Bayes'})\n",
    "scores2 = pd.DataFrame({'ROC AUC': random_params['score'], 'iteration': random_params['iteration'], 'search': 'Random'})\n",
    "scores1['ROC AUC'] = scores1['ROC AUC'].astype(np.float32)\n",
    "scores1['iteration'] = scores1['iteration'].astype(np.int32)\n",
    "scores2['ROC AUC'] = scores2['ROC AUC'].astype(np.float32)\n",
    "scores2['iteration'] = scores2['iteration'].astype(np.int32)\n",
    "\n",
    "scores1.head()\n",
    "scores2.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### We can also find the best scores for plotting the best hyperparameter values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 1 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "best_random_params = scores2.iloc[random_params['score'].idxmax(), :].copy()\n",
    "best_bayes_params = scores1.iloc[bayes_params['score'].idxmax(), :].copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Below is the code showing the progress of scores versus the iteration. For Bayesian optimization, we expect to see the scores increasing with the search as more promising hyperparameter values are tried."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 267,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "# Plot of scores over the course of searching\n",
    "sns.lmplot('iteration', 'ROC AUC', hue = 'search', data = scores, height = 7,legend =None,palette=\"deep\");\n",
    "plt.scatter(bayes_params['iteration'], bayes_params['score'], marker = '*', s = 100, c = 'orange',edgecolor = 'k')\n",
    "plt.scatter(random_params['iteration'], random_params['score'], marker = '*', s = 80, c = 'lightblue', edgecolor = 'k')\n",
    "plt.xlabel('Iteration'); plt.ylabel('ROC AUC'); plt.title(\"Validation ROC AUC versus Iteration\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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2npGC2rG9e6uAVxPIzcRVyiW4hQwx/hfX69sbuFPizqX1FUWsTNwVEu51uKGr/sAsETkwJD0dxZ1xOhuXtymhqp/hDPMuPu5dgJnePZ4/4+ZeLxKRqyXkQGwROVREhqcav/dzmogcEddYQEQ64Vb7Qs35rZn+fr6I9AvId8fNr9YaiqtDHHUh9u7u4RtLNfBlcwIuj6cFdQ/o00NExiZoIIXxBW6zeldfJwXDOx+o9R55vsQZ4p4i0iFCphbq9ubFGmMP+tGKWHydgQf9vw9rQ534ku7S1qZ+sWNPX+zaK4HsiIDcO7iVl//2/8f2P6W6bSHy5BZcxbfVP38Ptzk6Fk9sX87HcX5iR0cpbnP933Ev7HZcKzt02wLu9BTFbQp9CnfKwUSgk9ZcElxriwE79tbEDjD+Ozv2za0C+sfJJ1zOTAr7kkL8JDy5xcsc7mW2hqRfcEag0sssw60ifNLnY+z3foC4/Ztx4ZyNmzNUf5/uf7dn2bEXbTvw8xTSFNu7Nz4F2bsI2X6Am7f8yj/7AtfLiJWjWFr/mCDcLrhGSyz9C32+PI4z4BXe/RPi9o6moPN5gXAVODeB7AB2nFyz2ufr47jtNCu9+2NxfkJPUgk8/0OgjD6P623+E1eZK26UolVAXnC9p9h78pwv7+txDZO34uNLN44k+RW6ncE/i+2ZW4gbRp4IXBl43hp3JnGs/L/ly9cLuIZprCwE05vwtCYvE9uuobjGz5SALrcT8Z7j3gfFLVSZ7PW9NfA8ah9fW1wnJbY9Yaovj+u825sE9umlko5Avv6/pL9BOgW8OVy4lUexI5qS7t3DGaXZPsO/xVUkZ5P+fr1kR5bFTqxYj1t88Baut5DoyLKDgZdwFV7sOJ9LcT31KMOXD9yI6zHGNu5Xy6VQeH6Me/m/8S/Wp7jhv54hslkxfF7ueS9XaxO/f74fruX4oc/vzbhK/S/AoBR16Yaba/uX/w22+Xx5G1cZ7JlCGLG9e0rInqUQ+YFedhOBPUz+WXcf73s4Y1yBMxbPAD9MMU0n4CqjT32ebMH1VKbi5jUL6vDOxY4nizUS2iaR7+zL6DzcO7cFd3rNLNwis13j5JMZvoG4hWpv+PzY6u+zfXmqZchx83v34oxwhc+P3+IMS9g+vrTjSJD+RIavH67BupodRiz+yDLBHS34f16uAtf7ehfXoBsa8U4l3E+L6wi87X/DdbjGwQ9IXEd1xb1Tn7OjoZ7qkWVtcFMX831Z3OzTcA1JjixLkq9JDZ94D4ZhGIaRE7SoOT7DMAzDSIYZPsMwDCOnMMNnGIZh5BRm+AzDMIycosWc3NJYdO3aVfv27ZttNQzDMJoV77zzzhpVbYgzY9PGDF+a9O3bl3nzkh2TZxiGYQQRkeXZ1iGGDXUahmEYOYUZPsMwDCOnMMNnGIZh5BRm+AzDMIycwgyfYRiGkVOY4TMMwzByCjN8hmEYRk5h+/gMwwhl69atrF27lg0bNlBZWZltdYwmRn5+Pu3ataNz5860bt062+qkhRk+wzBqsXXrVsrKyujUqRN9+/aloKCAuI+PGzmMqrJt2za+/fZbysrKKCkpaVbGz4Y6DcOoxdq1a+nUqRNdu3alsLDQjJ5RAxGhsLCQrl270qlTJ9auXZttldLCDJ9hGLXYsGED7du3z7YaRjOgffv2bNiwIdtqpIUZPsMwalFZWUlBQUG21TCaAQUFBc1uDtgMn2EYodjwppEKzbGcmOEzDMMwcoqsGj4RyRORsSKyRES2iMhnInKXiLRN0f8uIvIn769CRMpE5F4R6Rgie4iI3Cci/xKRjSKiIjIq44kyDMMwmjTZ7vGNB+4GFgGXAk8BlwHPiUhC3URkZ+At4KfAM97/P4BfADNFpDjOyzDgYqAj8F4G02AYhmE0I7Jm+ESkP85YTVXV01T1IVW9ErgSOAY4K0kQ1wN9gPNV9VJV/bOqXgqcDxzowwnyR6C9qvbHGVzDMAwAZs2ahYjUuNq0acOuu+7KT37yExYvXpxtFY0Mks0N7CMAAe6Jc38IuB04B5iSwP8xQDnweJz7E8BfgJ8At8QcVXV1PfU1DKOFM2LECIYNGwZAeXk577//PhMnTuTpp59mwYIF9OnTJ8saGpkgm4bvYKAKmBt0VNUtIvKuf56I1sAWVdU4/1UiUg7sKiJdVXVNJpU2DKPlMnDgQM4555wabnvssQeXX345U6dOZezYsVnSzMgk2Zzj6wmsUdWtIc9WAF1FpDCB/4VAJxE5MOjo/+/k/y3JhKIiMkZE5onIvK+++ioTQRqG0Uzo2bMnAIWFO6qjBx98kKFDh9KrVy8KCwvp0aMH55xzDsuWLauWqaiooFu3bgwePDg03DvuuAMRYfbs2dVuW7du5dZbb6V///60adOGjh07cuKJJzJ//vwaflWVe+65hwMOOIB27drRvn179tprLy644AK2bduWwdS3TLLZ4ysGwowewJaATEWEzD3AKcCTInIF8AHQ37tvAwq8/3qjqhOACQClpaWaRNwwjGbK5s2bWbPGDRKVl5fzwQcfcMMNN9C1a1eGDx9eLXfnnXdy6KGHctlll9G5c2c++OADJk6cyKuvvsqCBQvo0qULhYWFnH/++dx1110sWbKEvffeu0ZcjzzyCHvuuSdHHnkkANu2beMHP/gBb7zxBueeey6XXHIJ69ev56GHHuKII47g9ddfp7S0FIBbbrmFG2+8kRNPPJELL7yQ/Px8li5dyrPPPsvWrVvt8IFkqGpWLmABsDri2ZOAAoVJwjgd+MLLKrAd+BMw1f9/QIS/H/vno9LV+6CDDlLDaOksWrSo4QL/9DHVaX1UJ4u7f/pYw8WVIjNnztRAPVLj2nfffXXx4sU15Ddu3FgrjFdeeUUBHTduXLXbhx9+qIBeffXVNWTnzJlTS/buu+9WQF988cUasuvXr9fvfOc7OmTIkGq3AQMG6D777FOfJGeUVMoLME+zZG/ir2wOda7EDWeGHendCzcMGtXbA0BVnwJ6AwOAo4Ceqnqhd9sOfJxZlQ3DqBdLJ8PcMbB5OaDuPneMc28CjBkzhunTpzN9+nSee+45xo0bx5o1axg2bBjLly+vlmvb1m01rqqqYv369axZs4bvfve7dOjQgbfeeqtabs8992TIkCFMmjSJ7du3V7s//PDDtGrVivPPP7/a7bHHHmPvvffmoIMOYs2aNdVXRUUF3/ve95gzZw7l5eUAdOjQgRUrVjBnzpyGzpKWSbYsLm7FpQJHxrm3ATYBL9Qx3O64oc5XEshYj88wEtBgPb5pfVQnU/ua1qdh4kuRWI/v97//fa1nb775pgJ65plnVrvNmDFDhwwZom3atKnVQzzmmGNq+J88ebICOm3aNFVV3bBhg7Zt21ZPOeWUGnJFRUWRvc7YVVZWpqqqb7zxhu68884KaM+ePXXkyJE6efJk3bp1a6azJiWaW48vm3N8T+D24l0BzA64j8bNzVU3AUVkN6BAVZckCtBver8PyAd+l2mFDcOoJ5vL0nNvAgwaNIgOHTrw6quvAvD2228zdOhQdt99d26//Xb69etHUVERIsJZZ51FVVVVDf/Dhw/nsssu4+GHH+aUU07h8ccfZ9OmTfzsZz+rIaeq7L///tx9992RunTr1g2Aww47jE8++YSXXnqJmTNnMnPmTKZMmcItt9zCnDlz6Ny5c4ZzoWWRNcOnqgtE5AHgEhGZCjwP7IM7ueU1au7hm4HbrF59GqqI7ITbCjENWAp0wO0NPAi4QVVnBuMTkT7Auf7f/v5+ooj09n8/qqrLMQyj4Sgu8cOcIe5NmO3bt7N1q1uLN2XKFCorK3nhhRfo169ftcymTZtYt25dLb+tW7fmvPPO47777mPlypU8/PDD9OrVix/84Ac15PbYYw+++uorjj32WPLyks9C7bTTTgwfPrx60c2DDz7IxRdfzMMPP8zVV19dn+S2eLJ9ZNkVwC9xhugB3Gkt9wM/UtWqRB5xqz3fB0YCfwBuAL4GfqCqt4bI9wN+66/YqTCnBdz6hfgxDCOTfPd3kB+32Dq/2Lk3UaZPn86mTZs46KCDAMjPzweITZtUc+utt9bq7cUYPXo0lZWVXHvttbz55puMGjWqOpwY5513HqtWrYrs8a1eveMMjtjK0yADBw4EaHYfhc0G2RzqRFUrgbv8lUiub4hbBcmPNQvKzyLQYzQMIwv0O9vd37vBDW8WlzijF3PPMv/5z3947LHHALenbuHChTz00EMUFBRwyy3uIKhTTz2V8ePHM2zYMMaMGUNhYSHTp0/n/fffp2vXrqHh7rPPPgwePJjHHnsMEeGnP/1pLZnLL7+c6dOnc/XVV/Pqq69y7LHH0r59e8rKypgxYwZt2rRh5syZ1eEdeuihDBo0iJ49e/LFF18wYcIECgsLOeuslKvF3CXbk4zN7bLFLUYu0KDbGZogYdsZ8vLytFu3bnrqqafq3Llza8hPmzZNBw4cqMXFxdqlSxc988wzdfny5dqnT58a2w6CTJo0SQE99thjI/XYtm2b3nvvvVpaWqrFxcVaXFysu+++u44cOVJfeumlarnbbrtNjzzySO3WrZsWFhZq79699cc//rG+8847GcmPdGlui1tE47rrRmJKS0t13rx52VbDMBqUxYsXs88++2RbjRbFk08+yZlnnsmUKVMYMWJEttXJKKmUFxF5R1VLG0mlhGR7js8wDCMneOCBB+jatSunnXZatlXJebI6x2cYhtGS+fLLL5kxYwazZ8/m9ddf57bbbqN167AzO4zGxAyfYRhGA7Fo0SJGjhxJx44dufDCC7nqqquyrZKBGT7DMIwG4+ijj6617cHIPjbHZxiGYeQUZvgMwzCMnMIMn2EYhpFTmOEzDMMwcgozfIZhGEZOYYbPMAzDyCnM8BmGYRg5hRk+wzAMI6cww2cYhtEM6Nu3L0cffXS21WgRmOEzDCPnmTVrFiJS49ppp50YOHAg48ePZ/v27dlW0cggdmSZYRiGZ8SIEQwbNgxVZdWqVUyaNIkrr7ySxYsXM2HChGyrZ2QIM3yGYRiegQMHcs4551T/f9FFF7H33nszceJEfve739GtW7csamdkChvqNAzDiKBt27YceuihqCqffPJJtfvLL7/MmWeeya677kpRUREdO3Zk6NChvPbaa7XCOProo+nbty8rV65kxIgRdOrUibZt2/L973+fjz76qJb8Z599xhlnnEGHDh1o3749J554Yo2445k4cSIDBw6kqKiIDh06MHToUObMmVNLTkQYNWoUr776KocddhjFxcX07t2bcePGAbBu3TouuOACdt55Z4qLi/nRj37EypUr65JtTR4zfIZhGAmIGZ3OnTtXu/31r39l7dq1nHfeedx///2MHTuWxYsXc9xxxzF79uxaYWzatImjjjqK/Px8br31Vi6++GJmzZrFySefTGVlZbXcN998w1FHHcXUqVM599xzuf322ykuLuaYY45h06ZNtcK99tprGT16NAUFBdx6661cddVVLFq0iGOOOYbnn3++lvz8+fM5/fTTOfroo7nrrrvYY489+NWvfsW9997Lcccdx7p167jpppu48MILefHFFznvvPMykYVND1W1K43roIMOUsNo6SxatKjBwl7+zSZ9/uNV+vSSlfr8x6t0+TebGiyuVJk5c6YCevPNN+tXX32lX375pb7//vt60UUXKaAHH3xwDfmNGzfWCmPVqlXapUsXPeGEE2q4DxkyRAEdN25cDfc77rhDAX3xxRer3a677joF9C9/+UsN2csvv1wBHTJkSLXbkiVLVET0iCOO0K1bt1a7r1ixQjt06KB9+vTR7du3V7sDKiL65ptvVrtt3bpVu3fvriKil156aY04x44dq4AuWbIkKtuqSaW8APO0CdThqmo9PsMwGo+y9ZuZv3o95durACjfXsX81espW785y5o5fv3rX9OtWzd23nlnDjjgAB588EFOO+00nn322Rpybdu2rf5748aNfP311+Tn5zNo0CDeeuutWuHm5eVx2WWX1XA79thjAfjvf/9b7fbMM8+wyy671OppXXvttbXC/Mc//oGqcs0111BYWFjt3rNnT0aNGsXy5cuZP39+DT+HHXYYgwYNqv6/sLCQQw45BB/MT48AACAASURBVFWtpd+RRx5ZS7+Wghk+wzAajYVrNlAZ913WSnXuTYExY8Ywffp0nn/+ecaNG0fnzp35/PPPadOmTQ25Tz75hLPOOotOnTrRrl07unbtSrdu3Xj++edZt25drXB79uxZK4wuXboA8PXXX1e7ffrpp+yxxx7k5+fXkO3RowcdO3as4bZ06VIA+vfvXyu+/fbbrzq8ILvuumst2U6dOgHQr1+/UPegfi0FW9VpGEajEevppere2Oyxxx4cf/zxAJxwwgkMHjyYwYMHc+GFF/L4448Drod31FFHsWnTJq644gr2339/2rVrR15eHrfddhuvvvpqrXDjDVkQjftCu4ikJBf/fyok0iPqWV3iaepYj88wjEajqFV4lRPlnm0OP/xwzj33XJ544gneeOMNAGbMmMHKlSsZP348N910E8OHD2fo0KEcf/zxoQtQ0mHXXXflo48+qrHgBeCLL75g/fr1Ndx22203ABYuXFgrnEWLFlWHZ9SmaZY2wzBaJP27tiM/rkOTL869qfI///M/5Ofnc+ONNwI7ekbxPaGXX345dH4vHU4++WRWr17NpEmTarjHthwEOemkkxARfv/737Nt27Zq9y+++IJHHnmEPn36MGDAgHrp01KxoU7DMBqNkg7FgJvTK99eRVGrPPp3bVft3hTZfffdOeuss5g8eTKzZ89m8ODBdO/enauuuoply5bRu3dv3n33XR599FH2339/FixYUOe4rrnmGqZMmcLo0aN555136N+/P7NmzeLf//43Xbt2rSG71157cfXVV3PHHXdw1FFHceaZZ7JhwwYmTJjAxo0bmTx5csKhzVzGenyGYTQqJR2KOWG3XThtrx6csNsuTdroxbjhhhvIy8vjxhtvpGPHjrz00ksMGjSI+++/v3rv3PPPP8/AgQPrFU+nTp2YPXs2p5xyCpMmTeKaa65h8+bNzJw5s8ZK0hjjxo1jwoQJbNmyhV/96lf8/ve/Z++99+bVV19l2LBh9dKlJSMtceKyISktLdV58+ZlWw3DaFAWL17MPvvsk201jGZCKuVFRN5R1dJGUikh1uMzDMMwcgozfIZhGEZOYYbPMAzDyCnM8BmGYRg5hRk+wzAMI6cww2cYhmHkFGb4DMMIxbY6GanQHMuJGT7DMGqRn59f4xgsw4hi27Ztze6EGDN8hmHUol27dnz77bfZVsNoBnz77be0a9d0z1oNwwyfYRi16Ny5M+vWrWPNmjVUVFQ0y+Eso+FQVSoqKlizZg3r1q2jc+fO2VYpLZIeUi0ibXFHm21MILMToKpav29yGIbRJGjdujUlJSWsXbuWZcuW1fpMjmHk5+fTrl07SkpKaN26dbbVSYuEhk9E9gIWAHcC1ycQvQ74pYjsq6qfZFA/wzCyROvWrenRowc9evTItiqGkVGSDXVeCHwF3JxE7rde7sJMKGUYhmEYDUUyw3c88L+qujWRkKpuAZ4ChmZKMcMwDMNoCJIZvn5A7e/ah7MY2K1+6hiGYRhGw5LM8OUBqS7nqkohvBqISJ6IjBWRJSKyRUQ+E5G7/IKaVPzvIiJ/8v4qRKRMRO4VkY4R8nuJyDMisk5ENonIbBE5Nh2dDcMwjOZNslWdXwCpfo1yXy+fDuOBy4BpwF0+rsuAASJyvKpWRXkUkZ2Bt4CewJ+BD4D9gF8AR4nIEaq6OSC/G/AGsB24A1gPjAZeEpETVPWVNHU3DMMwmiHJDN9sYKSI3JjCdoaRwAupRiwi/YFLgamqOjzgvhS4DzgLmJIgiOuBPsBIVf17wP8b3t+VwC0B+duAjsBBqvqul52EG8p9QET2VtusZBiG0eJJNjT5B6AbME1EQncoikgnXI+tK3B/GnGPAAS4J879IWAzcE4S/8cA5cDjce5PAFuAnwR0bAucBMyKGT0Ab8wnAnsCB6ehu2EYhtFMSdjjU9V5InIzcBOwVESmAu8B3wLtgAHAKUB74Neq+p804j4YNy84Ny7OLSLyLskNUWtgS3wvTVWrRKQc2FVEuqrqGuAAL//vkHDeDOgzN+S5YRiG0YJIenKLqv5GRD4HfgecH3PG9dYAVgFjVfWRNOPuCayJ2CqxAjhcRApVtSLC/0JgLxE5MNiLE5EDgU7+3xJgjY8rFm5YXAC9ohQVkTHAGICSkpIoMcMwDKMZkNTwAajqX0TkUeAI3AKS9rhe3wfAv1S1Lse4FwNR+wO3BGSiDN89uN7mkyJyhdelv3ffBhR4/wTuYfFtiZOphapOACYAlJaW2jygYRhGMyYlwwfgjdssf2WCzcDOEc/aBGSi9JktImfhFsL80ztX4ubsFgKn4oxzMJywA+WSxmUYhmG0HFI2fA3ASmBfEWkdMtzZCzcMGtXbA0BVn/Lzjvvj5hw/VNUvRWQubtvCx4G4YuHGE3MLGwY1DMMwWhjJDqn+NMFjxa2q/BR4BvibqqZzhPvbuCPODsFtm4jF2QY4EHg9lUB8nME5vu64RTevBfbxLcANcx4WEsSh/j4vDd0NwzCMZkoqJ7dIxJWHG6r8EW4LwssiUpBG3E/gjOcVce6jcfNtk2MOIrKbiOydLEARycMNfebjFuMA1dsWngOOFpHvBuR3An4G/Bdb0WkYhpETJNvO0DdZACLSFbdZ/Fc4I/b7VCJW1QUi8gBwiR+ufJ4dJ7e8Rs3N6zNwm9VjK0ljRmsubg/hUqADbm/gQcANqjozLsrrgONwBno8bv5vNG6o84e2ed0wDCM3qPcX2FV1japeD7yIO20lHa4AfolbjfmA938/8KNEx5V5KoD3cSfG/AG4Afga+IGq3hqi58e4Valv4oz0ncAmL/9SmnobmWbpZHimL0zJc/elk+smYxiGkQTJVEdHRK4CblLVdhkJsIlSWlqq8+bZdGBGWDoZ3rsBNi/HdeaDZdH/X9wHvutHreeOgcrN0TL9zg6EWQaFnV2Q29ZCcUm4TEFnF0xFnEyoninK1zkfUtQ5FT1T0a2h5aP8ppvnUfkQzKtgmMG/Gzo/U0lXKmmpS1lqzN8vA4jIO6pa2mgRJiCThu8i4C5VLcpIgE2UnDF8mark4v0mNHYR5BdDXhFs+zqBUCysBGHmF0O/82Hp3+IMaJzMIRNq6lvL4CaQj/lJtaKD5OGH6ZyKnjEZSC3ehGlJ0Dgp6JLYkKWah9V6Jogr1TITSUQ4qehQl7IR9dsl+k2rdUhi9Ld9CzW2UCf4PSC98tEABrGlGr6HgKNVdY+MBNhEaTDDl0JlWdbtHBZ2v5zygu4UbVtN/6K1lOx+fMPokkqlGKp/ggoDElcUjYHkQyqLjwu7+J5CImPrKe4Dpyxzf0flXb/zKVu3loU9rqS8sBdFFSvov+J2StZNq7vOwXif6evzPo6CLlBVXlufvCLKdjqShb2uC+hzGyVb57uyl0bjpKzTKTXD+eJuSvY80T2sDicJYXpmhSTpTSXPU4om4jeN+r2SNdgSkajhGBVfqo25NGhxhk9Evgf8H/BnVb2s3gE2Yepr+MrWb2bhmg2Ub6+iqFUe/bu2o2TttOhhPH8v63QK8/vcSWX+jgNm8qs2M6BgKXQ7vHaYHSIPognXISgf9ULHhhTT7bXE/ELdK4omQq1KfsVtlKz7B4z0U9IReVfW+TTml9xR8/er3MyA5b8EqA6zYPs611hv1SkQ/jMR2siOeKek8+lMwstTTJ+81nEG2ukQlnYgPJwVN1Oy9umMGLLwPI/Kk0YgnUZRHKmkJVTmm+co63hi4+ZDommGZA3hEJqN4RORvyTxX4xbibkf7szOg1R1VebUa3rUx/CVrd/M/NXrqYzPclUKt69171KrTqGV38Je11He+ju1wizYvo6qgk61wwQKqEC2b6Yiv311D5Fuh9fSIV9gwC4ddhi/RJVofnGEgU5GbEFu5hfPRlUmURVIsKWdXkXU2+kvgTVhWgUIBZXfIK3aUqGt0vv9tn1NVV5RDcNRAx9+UcXnoekqzM9HVdlWVUXh9nXVZSiltIjUii5Mn/zKzZSseZyyrmfVcs+rKmdbQZcQvbWGzlEky/9EBjpeLuUyUE9jUdcwEzY2ILKMJcr/WD4EdQqWv6gykXJ+fXE3Jd++Em7kg73fFGhOhi/ZykqA2B65X6nqZ5lSrKlSH8P3wierKd+eSpbWJL9yM5V5bWpWuDFUQyuwULTKy4bLV/f+Zu5LWesBSY1GWiTp8bkX7nrKC3umVIEn7AlHVBQxI1JYuc4Zi1adCatkklVE6ZKp3y8yXQnk005LlD7pusfpEG6kwiv5oPwL+70V2mAo2voZJ3wwqDq8dMtAKkY5jFQNcRhRaUna+AFEt6NSe/dZ0dbP6L/itlo6ReLTn6pxTZxfgRGHFGhOhq9PEv/luKPF0q/Nmyn1MXxTP0z3A/U7iCr4mSafSkpkJWWVXajMixtWXVbHVnZwzicwZLLD2PUiUYVcq2IJDLm+sOuToZVJffIrlYooXSL1SafhkiicCNJOS5r6pCRfY0SjdmMjnh1GTZg68LOIBkMVp33wXVB4Ya8XKG/du5ZI0rxKyQjWHNFIaIg/HOYXlYQPgU4d+HndGz+RDY8qiipWhOqUNuk2Ylpqj8+oTTZ6fACokl9VntHKOFFcYS9AUcUXnLDgICC9VnY+lQzo3pmSDsWUffwKC8s7U17QnXR6UtWVYdzcQn0aE5GkW/mnGmZIKzu/qpyKsKHC6ICI6rFHxptqj1IgXyuooDC98OvZI64dZhWlK29mYa9fUU74IvGiVnmcsNsuQGbKQM1KPW6bTGCBTiLjVVSQHzpnX9bpFBb2voHygp516jVDgh5fq7y61yl1IOo9TIWmZPgyUlpFZBcRuUZEFmUivJZK/67tyK9jfVpU8TkDVtxMUcVKX9k0IBEvYXlhd1fgcUNn8Ua4Mr+YZTufV9udfBau2eDmOKv6U17Yw8WRRmVZXtjLVURxL1tRq/AwMmy26k8svVoFWkUBFc7ItOrsex4p0hA/vSpFrfIYsEsHDujejXzSGM4O/o4ZKpcF29cxv/fNkUYvX9y7FCMTZaAyv5iFva53ZeywR2Gkut5Mv7Od8fNluqgi4ix7Ecq3VzF/9XrKOp/qymlxH8o6ncr8vne58hvRY0tGfuVm+n45ify4hUKxfIhKf0MQ9R42N+rc4/PnYv4IuAA4AXf82QZV7ZA59ZoeGVvVuW27c0ih8g/2mABeeP/t0KGdtFvfafZsilrlcULeK/DeDUzd+18R8UT3SOrbOg1bgRq5YKjBUZ9/6Vc6BVRQJYXhC5IERISKSm986jm3mKxHGVsZHNwWE1z1mw6FVFBZtb3GEHmddNYKKlp1DH2eahnIFyhpV0TZhvK0ysZpe/WoEW716mfK6b/idti6hvl976QyL/F25ZiekfmY9F11ShdVrKT/57/bMYXQ+wbKC3tS1Cq/Oh8a8x0I9rTTpSn1+NKeBBGRvYCfAufhDqleBzwGPA1Mz6h2LZCSDsXupV06mbKPnqteNl64fR0q4lZ1irjKr0r9C9Shxovef81DzO9+bVpL44HUJrMjqG5ldzgb+p1NUcSwrSChnZJMDMnEWtRAdX7E7gkr6lirOgPDccGKd8ew7S418nrH351DGxbbKAztudWoVJ7pG1hgFL4Cs3ruzJebwrw8t2hHtbrChNrbDWL5EbUXNFZGowxKVAVbQSGlBf/dMZSdzqIrqJ4Pntf37kjRsEo3vgwEf6MuxYUpG/GiVnmRRr+cIub3vpkBu3RgAEnKGzvKakJjFFEeaxj3pTNh63xAKNk6n5KdPoR+NW1HfPqrG09VSmGe+DIRElFsoUvo71S7YZdftZn+uhSom+FrSqTU4xORtsCZOIN3GO5bd/8ChgCnq+rUhlSyKZHRDex13RQaZzSTboYu7kPZMYtqtV5LVj1EWadT3VxKgsq1eu6ijq3s2HaJVCughC8r0a3OqDnUIsrpv+z60EZGrOWcSLekeyMj9u69sP87blg3Dap7HIGN8NHzSlWc9p9Azz/i6Lay7j+rni+LSkvU3s4w96i8Cv4uqfZCdpTFidXvwAtVx0eGH4w/lT2rUWmMJ9UeYjCN9Z2zj2oYBHudmSTstwTCtzd9/mvY+lX4AQdpLGgJ0mx6fCJyOG4o83RgJ2A+MBb3yaBOwEcNrWCLpt/ZdRsn73c2JUDJe2fUNJr/fobQrsTmsh09zRh7TQAmULJ0MiXvnUFZ6wGRG+RLdqt9Okyqrez4yimqMgyrxKIWLURVNpHuFFGy54m18yuQ9/NWrQ/1C+G9jBp893ehG3z7F61lflWPWpVKvm+Nx1Njriam23s3UFSxMnRou9Z80+blTo+Yfx9Gib+iiDdS8T3rMMMSVlkG593iy0ewMVPrt/ZlMUb/iEZV9+LWCfUMpies/AXTEmXQkxnqYBnr37VdnYYY8wXy8/KSl4EME/VbQsh7PG8ioBHbiJo/yYY65wCrcaXyb6q6IPZARMIH4Y3GIcxoRh0PVZyg2vPhlAAEhu5qDINF9EyjXqRE7hBuLMOIGh6NqhwSyidpZKQbVw0CRiqYRyX9joc0WtlBw1Edbr+zww1B5ebqk1NqULnZ6ZFGgyqswq9U516f3zFRRZuIqPBT0TOZEU+kW6LGT4xgeUhpmD3Ef1ploBEI/Z2KS9KvS5oRqczxFeG+dde+gXUx6ktEz6P62KEklOx+fKBn0Mvd4s+ejO9VpEk6lWFYizpR5ZCufKb8ApGGNa1WdoRczQq2ssaCh1A2l6WmsyfdHnRMp7oYtVRJxzAF9UzXiAdJZR66fHsVL3yyulYvMtWh3fjRg7oO2zY49axLmjrJDN++uC+UnwP8VESWAn8DJjW0YoYj6bmaQSJ6HvVadvzeDbXPW0y3V1HHucxEPYtE+VKXyqQ+futCuoajhnz1gocI0myV16u324ikomddjHiMVIcu67rIKj4/G7rxUC8aoi5pQqS6uKUVcBJuvm8obqD3feC7wBmq+nRDKtmUaMzPEkUtIKlxrmZDE3luZ4rHFdXlSw9JaBL50hRIkLdlnU9N2Yg3l/xMRc/IBU4pLsOv62KedPXMRZrN4pYYqrodmApMFZEewE+AUTgD+KiInIPbzvCcqiYfKDdSoj7DNhmjvmP9megxxtEk8qUpENEqL+t8akrzXDEau7dbV6KW7c9btZ6FazbQv2u7eg9Z13WINZGe9cnPtEZ8jJRJex+fqn4B3ArcKiJDcL3A4cDJQAXQJqMa5jD1GbbJGPUd64+ab0pzHipIk8iXpkLI3OLCT1an3TBo0sNuAWrNqfmVkTHjPmCXDjW2zmTCWNRlKDgT+ZnqQh0jfep16rGqvga8JiKXACNx+/yMDNEk5l7iehXVe8IqiigKTPJH0gCrw5pEvjRhcqFhkKjXf8Juu2TUMNR74VMdsZGNhiMjNYWqfquqf1LVQzIRnuEIO9szK0ue+50Npyyj7Icba5yhWH024foEHxsNnHNYTT1XhzWZfGmiJNru0VJoTONe0qGYAbt0qM6/2LmmDW18cqEBky0a/js3Rp1panMvdWqBZnB1WHC+ozBPyCNiQ3SOk60eSmPS2L3+bAwF28hGw2GGr4mTsReursejBahzC7SuJ9QEiJ/vqKhS8gVKu+f2SrkwmlqDqSHIBeOeC2nMFmb4coEMbULPZgvU5jvSo7ksVqkruWDccyGN2cIMXy6QoS0F2WyB2nxHyyfdpfst3bhDbqQxG5jhywUytKUgmy1Qm+9o2djSfaMxSWr4RGRvAFVdUh8ZI4tkcEtBtlqgNt/RsrGhbKMxSdhcFpFSYCHw/SThfB/4QEQOyJRiRgZpgC0FjU22lpQbjYMNZRuNSbIe32hgKXB/Ern7gYuBC4GLMqCXkUlayIGzNt/RcrGhbKMxSWb4jgaeVtWEzS5VrRKRp4FTMqWYkWHqsaXAzgs0Ghobyk4fey/rTjLD9x1S/8r6x0Cf+qljNDVs0YHRGNjS/fSw97J+JDN8VSnIBMOyAfkWhi06MBoLG8pOHXsv60eyAfTPgANTDOtA4PP6qWM0NWzRgWE0Pey9rB/JenMzgZEicpOqro4SEpFdgBHA5EwqZ2QfW3RgtHSa41yZvZf1I1kujQeKgJdEZK8wARHZE3gB9x2+ezKrnpFt7EsIRksmNlcWMyIpfXGkCWDvZf1I2ONT1f+KyEXAn4GFIjIHeBf4FmgHDACOwH2JfbSq/reB9TUaGVt0YLRkmutcmb2X9SPpwhVVfVhElgPjgKP8FWQ+cK2qvtIA+hlNgPouOmiOQ0lGbtCc58psMVDdSWnFpjdqB4lIX2A/oD2u1/eBqi5rKOWM5o8tuzaaMjZXlpukdUi1N3LLGkQTo1kT1atrrkNJRm5gG+dzk5QNn4h0AYYB+wMdgPXAAuAFVV3TMOoZzYFEvbrmPJSUU2TgQ8XNEZsry01S+TqDAL8Gfolb4RlcS6TAFhG5A/iNqmpIEEYLJ1GvzoaSmgEZ+lBxc8XmynKPVGqfR4AbgTXAbcCPge8Bw/3/X/nnjzSQjkYTJ1GvzpZdNwMSfajYMFogCXt8InIycB7wN+BCVd0aJzJNRH4L/BE4X0SmquqzDaOq0VRJ1KuzoaRmQIY+VGwYzYVkQ51jgA+AC6K+0KCqW0XkZ8BBwM8BM3w5RrIFAjaU1MTJ4IeKDaM5kGyosxSYkspniYC/e/mUEZE8ERkrIktEZIuIfCYid4lI2xT97yQi14vIAhHZICJrROQNERnl5yaDsiIiF4rIfBEpF5FvRORFETk0HZ2N2thHYps5LeBDxYaRDsl6fB2BVSmGtQq32jMdxgOXAdOAu4B9/P8DROT4RAZXRPJwR6UdjhuKvR8oxp0Z+ogP69qAlwdxH8qdBVzjZccAr4nI91V1Vpq6GwGsV9eMaSEfKjaMVElm+NYA/VIMqy/wdaoRi0h/4FJgqqoOD7gvBe4DzgKmJAhiEDAYuEdVxwb8PwgswQ27XuvdDsQZvReBYbHVpyLyZy87QUT2TtazNYwWSz0+VGwYzY1kQ51vAOeKSJtEQv75eV4+VUbgtkbEH2z9ELAZOCeJ//b+vjLoqKoVOIO9KeB8jL//LbjlQlW/Af4B7IE7c9QwDMNo4SQzfPfjenLPiEjnMAHvPg339fX704j7YNyHa+cGHVV1C+4g7IOT+J8LfANcIyKni0iJiOwlIrfhFtrcFJBt7e9hR67H3GyuzzAMIwdI9nWG170huQ5YKiLP4A6lXo+bzxsInIz7UsMdqvp6GnH3BNaEbJEAWAEcLiKFvgcXpts6ETkJmAg8GXi0ARiuqs8E3Bb6+7EEVp36BTBD/L/fiVJURMbg5gMpKbGVboZhGM2ZVL7OcIOIfArcApzrL2XHCS6rgV+q6kNpxl0MhBk9gC0BmVDD59mI227xLG6YtTNwMTBFRE5W1ele7gVgEXCRiKwEpvqwr8Qduh2LKxRVnQBMACgtLbXTaQzDMJoxqX6d4WERmYSbB6vxdQbgjaheWRI2AztHPGsTkAlFRPbHGbuxqvqngPvfvV4PichuqlqpqttF5ATc6s9x/gJ4H/gVbkXpt3VIg2EYhtHMSPmQalXdhtsKMCtDca8E9hWR1iHDnb1ww6CJDOpYnIF8Kk7PzSLyT+AS3PzkJ969DDhGREq8+9equtB/aBfc6k7DMAyjhZOxk4JF5AgRmZGGl7d9/IfEhdMGOBCYl8R/L3/PD3nWKu5ejaqWqerrqhqb9xuGW2TzUop6G4ZhGM2YlAyfiHQRkUNEZPeQZ4eKyMvA69T+OnsinsDNFV4R5z4aN982ORDHbiKyd5zcIn8fFadPR9yCm3X43l4UfnHMD4FHVTXkzCbDMAyjpZHskOp84AHgZ/jFLCIyF2dYtgB/As7E9ZimACmfcaSqC0TkAeASEZkKPM+Ok1teo+bm9Rm47RLBY8juwe0dvN3P9/0Lt7hlNNADuFhVtwfS8rD3/y5Qjtv8fjau53l5qnobhmEYzZtkc3yX4pbxfw68CeyOOzHlAaA3bpjyUeC3qpqwdxXBFbgvuo/B9bzW4PYC3pjC+aDLReQQ3CeRjsOd9FKOM2xXqerUOC9zcae5DAcKgY+93/GqWl4H3Q3DMIxmiCT6dqyIvIMzjoep6mbv9gDwC9zxZCep6r8bQ9GmQmlpqc6bl2z60TAMwwgiIu+oalofMmgoks3x7QlMihk9zx/9fVyuGT3DMAyj+ZPM8LWl9tcZYv8vyLw6hmEYhtGwpLKqM34sNPb/tgzrYhiGYRgNTiob2IeJSPfA/8U443e6/9xPEFXV8RnTzjAMwzAyTCqGb6S/4vl5iJviPi5rGIZhGE2SZIbvmCTPDcMwDKNZkeyzRK81liKGYRiG0Rhk7KxOwzAMw2gOmOEzDMMwcgozfIZhGEZOYYbPMAzDyCnM8BmGYRg5hRk+wzAMI6cww2cYhmHkFGb4DMMwjJzCDJ9hGIaRU5jhMwzDMHIKM3yGYRhGTmGGzzAMw8gpzPAZhmEYOYUZPsMwDCOnMMNnGIZh5BRm+AzDMIycwgyfYRiGkVOY4TMMwzByCjN8hmEYRk5hhs8wDMPIKczwGYZhGDmFGT7DMAwjpzDDZxiGYeQUZvgMwzCMnMIMn2EYhpFTmOEzDMMwcgozfIZhGEZOYYbPMAzDyCnM8BmGYRg5hRk+wzAMI6cww2cYhmHkFGb4DMMwjJzCDJ9hGIaRU5jhMwzDMHIKM3yGYRhGTmGGzzAMw8gpsmr4RCRPRMaKyBIR2SIin4nIXSLSNkX/O4nI9SKyQEQ2iMgaEXlDREaJiITInyAiM0RklYhsEpEPReROEdkl86kzDMMwmiLZ7vGNB+4GFgGXAk8BlwHPiUhC3fzzF4DfAm8DVwG3APnAI8DtcfKjgeeBDsA4YCzwOnAF8O9Uja1hGIbRvGmVrYhFpD/O2E1V1eEB96XAfcBZwJQEQQwCBgP3qOrYgP8HgSXAz4FrA/K/BL4AknhDZAAADxJJREFUBqvqFu82QURWAzcA3wOeqW+6DMMwjKZNNnt8IwAB7olzfwjYDJyTxH97f18ZdFTVCmANsClEfl3A6MWI+Y+XNwzDMFogWevxAQcDVcDcoKOqbhGRd/3zRMwFvgGuEZFlwFtAETAKOAi4ME7+JeB8EbkLmAhs9HH8D/Aa8Go90mIYhmE0E7Jp+HoCa1R1a8izFcDhIlLoe3C1UNV1InISzog9GXi0ARiuqvHDlpcDxf5+ZcD9EeDnqloZpaiIjAHGAJSUlCROlWEYhtGkyabhKwbCjB7AloBMqOHzbAQ+AJ4F3gA6AxcDU0TkZFWdHpDdBpQB04DncMOp3wd+ClQCo6MiUdUJwASA0tJSTZgqwzAMo0mTTcO3Gdg54lmbgEwoIrI/ztiNVdU/Bdz/jjOGD4nIbqpa6VeAvohL7xGqGjNe/ysiXwPXisgTqvpK/ZJkGIZhNHWyubhlJdBVRFqHPOuFGwZN1NsbizOQTwUdVXUz8E+gD9DXOw8GjgSeDhi9GDH/Q9LS3jAMw2iWZNPwve3jPyToKCJtgAOBeUn89/L3/JBnreLu6cgahmEYLZhsGr4nAMVtIA8yGje3NznmICK7icjecXKL/H1U0FFEOgInA+uAT+JkzxaRgrhwYv7fTk99wzAMozmStV6Oqi4QkQeAS0RkKu5UlX1wJ7e8Rs3N6zNwQ5fBY8juAc4Dbvfzff/CLW4ZDfQALlbV7T6u90TkaWA4ME9EHmPH4pYTgTeBfzRUWg3DMIymQ7aH964AluG2CvwQt/H8fuBGVa1K5FFVl4vIIcCNwHG4k17KgXeBq1R1apyXkT6+s4Hf4Hq7y4HbgN8l2s5gGIZhtByk9loPIxGlpaU6b16y6UfDMAwjiIi8o6ql2dYDsn9ItWEYhmE0Kmb4DMMwjJzCDJ9hGIaRU5jhMwzDMHIKM3yGYRhGTmGGzzAMw8gpzPAZhmEYOYUZPsMwDCOnMMNnGIZh5BRm+AzDMIycwgyfYRiGkVOY4TMMwzByCjN8hmEYRk5hhs8wDMPIKczwGYZhGDmFGT7DMAwjp7AP0aaJiHyF+3J7femK++J8LpFrabb0tmxyLb1QvzT3UdVumVSmrpjhyxIiMq+pfI24sci1NFt6Wza5ll5oOWm2oU7DMAwjpzDDZxiGYeQUZviyx4RsK5AFci3Nlt6WTa6lF1pImm2OzzAMw8gprMdnGIZh5BRm+AzDMIycwgyfYRiGkVOY4WtERCRPRMaKyBIR2SIin4nIXSLSNtu61QcR2VNEfiMib4rIVyKyQUTeFZEbwtImInuJyDMisk5ENonIbBE5Nhu6ZwIRKRaRpSKiIvKHkOctIr0i0llE7hSRj335/UpEZorIkXFyg0TkFV8OvhWRF0XkwGzpXRdEZCcRuV5EFvh0rBGRN0RklIhInGyzSq+IXCciT4nIp77MLksin3L6RKSniEzyZaNcROaJyOkNkpB6YItbGhERuRe4DJgGvADsA1wKzAaOV9WqLKpXZ0TkduBi4FngTWAbcAxwBvA+cKiqlnvZ3YC5wHbgHmA9MBrYDzhBVV9p9ATUExG5E/g5sBPwgKpeEnjWItIrIn2AWbg0Pgx8BHQADgBeUtXHvdyhXm4FEGsEXALsDByuqgsaVfE6ICJ5wGvA4cDfcGW6GBgBHALcoarXetlml14RUWAt8B/gIOBbVe0bIZty+kSkMzDPP7sb+BwYCQwBfqqqjzRAcuqGqtrVCBfQH6gCno5zvxRQYGS2daxH2kqBDiHut/i0XRJwexKoBA4MuO2EOwbuQ3xjrLlcwECcUbvSp/UPcc9bRHpxjbPPgB5J5OYC3wK9Am69vNvL2U5Himk9zP+W4+PcC4FPgW+ac3qBXQN/fwAsy8TvCdzh8+3EgFu+D+NrYKdspz122VBn4zECEFyrP8hDwGbgnEbXKEOo6jxVXR/y6Al/3w/AD3ueBMxS1XcD/jcCE4E9gYMbWN2MISL5uN/vRWBqyPMWkV4ROQoYjOvpfCEiBSJSHCK3Oy49T6nqipi7//sp4HgR6d5YeteD9v6+MuioqhW4cyo3QfNNr6p+mopcHdI3EvhEVZ8LyFYC9wOdgWEZUD8jmOFrPA7G9fjmBh1VdQvwLs2gAqwDvf19tb8fALQG/h0i+6a/N6d8GAvsjRv6CaOlpDdWYZWJyHNAObBJRD4SkWCDLZaWqPQKbmitqTMX+Aa4RkROF5ESP097G07/m7xcS0lvFCmnT0R64HqCb0bIBsPLOmb4Go+ewBpV3RrybAXQVUQKG1mnBsP3hm7EDQNO8c49/X1FiJeYW68GVi0jiEg/4GbgN6q6LEKspaR3L39/CNdyPx+4AKgAHhWRn/jnLSK9qroO11NfixuqXg4swc1jD1fVh7xoi0hvAtJJX7PKi1bZViCHKAbCjB7AloBMReOo0+DcAxwKXK+qH3q32PBYWD5siZNp6vwRWIqbxI+ipaS3nb9vAI7xQ36IyDTcnNetIvI3Wk56ATbi5r+eBd7AGfyLgSkicrKqTqdlpTeMdNLXrPLCDF/jsRm32imMNgGZZo+I/BY3/DdBVW8LPIqlr3WIt2aTB354byhwlKpuSyDaItKLG9oE+P/tnWuMXVUVx3//MigGC/ZBGh7GQRDpByL2A6FiTG38BDWxDYrRClVbpFQ+EI0GH3QSI0SNYMTWFqJt2mA/QAKiaKiPNtUaYgWigoaiELXIQ0SxMkApLj+sfdPDyZnpzPT23rn3/n/Jzpm79jrn7nUfs+7aZ6+9trWcHmRkJOku4FIyKuwLeyWdQzq7qyNiQ0W+jXSGt5TVun1h7zhMxr6eei081dk5/k5OZzZ9ME4lp0F7PtqTNAJ8AdgEXFHrbi0WaJryaMmapkqmDeX9uwH4EfCkpDPLIoA3FZUTi+wN9IG9hX3l+GRD3xPlOIv+sfdq8p/1bVVhRIwCd5Pv9TD9Y+9YTMa+nnot7Pg6xx7y9T6vKpR0HHAumf/S00haC6wFtgAro6xnrvB7cipkYcPp55fjdH8dXgecBFwEPFJpO0v/8vJ4Jf1hLxxakHVaQ19L9jT5GYex7Q3gvvYO7ajQ+kd9TEPfUOXYL/aOxYTti4gnSMd2/hi6MJ0+693OpxiUBpzD+Hl8y7s9xiO079pixxZgxjh6t5F5bW+ryFp5bXuZ5nltwLHAxQ1tdbH/x+XxWf1gbxnvLDJvax+VXCzgZPJe2N6KbE/RPaUiO6XIftptWyZo743lvfxMTd6K4p8FhvrBXg6fxzdh+4CvMXYe37+Amd22t9W8c0sHkXQTee/rDnKqbD65k8tuYHH07s4ta8hdHf4KfJF08FWeilwM0MoN+jW5u8uN5BdoFfnD4KKIuKdT424nkobJxS71nVv6wl5JlwMbgYeA75LJ3KtJ57ckIrYXvXcAO0gneVM5/SpgHnBBRPy2w0OfNGWXmvtJh38r+f2cTb5vw8CaiFhfdHvOXkkf4dDU/FXke/n18vgvEbG1ojth+yTNISPAOeTtgMfJ/OVF5AzQd46SSZOn2553kBr56+dT5I4dL5EfjBuYRjsaTNGuzeQvvbHazpr+fOD7ZK7UKPBLcsu2rttyBK/BMA07t/STvcAyMifreXKF53byn19dbyHwMzIa3A/cAyzo9vgnaesZ5HZl+8gfLf8BdgHLet1eclp+Qt/VydpHThNvJRP9XyR/QFzSbZvrzRGfMcaYgcKLW4wxxgwUdnzGGGMGCjs+Y4wxA4UdnzHGmIHCjs8YY8xAYcdnjDFmoLDjM8YYM1DY8RkzRSQtkhSSVnR7LFNB0ooy/kXdHosxncSOz5g2IWlY0oikc7s9lhbFOY+UahHGGPDOLcZMFUkzyH0OX46IV0rktAP4aERs7ubYWpQyUWuB06NWKV7SMeSm2weiR/eJNWYquBCtMVOkOIsXD6vYJiTNjIj97bpeRLxCVo4wZqDwVKcxU6R6j6/c59tRujYVeUjaWdGXpNWS7pM0Kmm/pB2S3l277nA5d0TSJUX/Bcru+JLOlrRe0kPlGqNFZ1XtOpvJaA/gscqYRkp/4z0+SXMlrZP0N0kHynFd2X2/qtc6f7GkT0v6s6SXJO2VdNmRvbrGHD0c8RnTHnYB1wGfA24GflHkT1V0tpJlWm4nK9S/Fvgw8BNJyyLirto130eWrfo2sIGsEABZ5uVdwA/JUkjHA+8HbpY0NyKuL3obgROApWRV8WeK/HdjGSHpROBXwJlk+aH7gbeTJYgWSzqvIeq8jizQu5GsOrIa2CzpTxGxe6znMqZrdLs8hJtbrzbSAQWwoulxTXdp6bu8Jh8iK1M/xqF77sNF92VgfsO1jm+QzSDLzTwHHFuRj5RrDTecs6L0LarIvlxkV9Z01xT5lxrOfwB4TUV+KukAt3X7PXJza2qe6jSmMywna5ndWaYS50qaS1b1/gHp7N5SO+fuiPhj/UIR8Xzrb0nHlSnI2WR9vBOAs49gnEuBf5BRa5WNZMS4tOGc9RFxoDK+x8nq8nV7jJkWeKrTmM4wH5jJq6c+68wjHUaLvU1Kkl5PRnIfAN7YoDJrakME4HTgNxFxsCqMiIOSHgYWNJzzaIPsnxyq8m3MtMKOz5jOIDKS+tA4Og/WHo+Oofc9YAkZle0CngUOAheS9/I6PZMz1spQdXQUxkwQOz5j2sd4SbGPAGcB90bEf6f6BCURfQmwNSKuqPW9Z5JjauJR4K2ShqpRn6QhcvxN0Z0xPYXv8RnTPloObXZD3xby+3Z9Qx+S5k3wOVrR1auiKUknAysnOaYm7gROarjWqiK/Y4LXMWba4ojPmPbxB3IBy5WSRoF/A09HxM8j4nZJm4BPSlpApiI8A5wGLCTTB958uCeIiP2StgPLS27fHvJe2ifIlaFzaqfcW45fkXQrmXD/YETUp1VbfJVMjVhXxvkAmc7wceDh0m9MT+OIz5g2EREvAB8k8+2+AWwDrq30fwy4FPgfcA2ZkH4ZGZVdM4mnWk7m2L0X+BaZ7/d5YF3DmHYDnwXOAG4pY7p4HBueAy4gV3FeCHyzHDcA74w27hxjTLfwXp3GGGMGCkd8xhhjBgo7PmOMMQOFHZ8xxpiBwo7PGGPMQGHHZ4wxZqCw4zPGGDNQ2PEZY4wZKOz4jDHGDBR2fMYYYwaK/wP18lzbUDhIAwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.patches as mpatches\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "x = bayes_params['iteration']\n",
    "y = bayes_params['score'] \n",
    "\n",
    "plt.scatter(x, y, c='orange', label='Bayes')\n",
    "\n",
    "x = random_params['iteration'], \n",
    "y = random_params['score']\n",
    "\n",
    "plt.scatter(x, y, c='lightblue', label='Random')\n",
    "\n",
    "plt.legend()\n",
    "\n",
    "plt.title('Validation ROC AUC versus Iteration')\n",
    "plt.xlabel('iteration')\n",
    "plt.ylabel('ROC AUC')\n",
    "\n",
    "#plt.savefig('ScatterPlot_09.png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Sure enough, we see that the Bayesian hyperparameter optimization scores increase as the search continues. This shows that more promising values were tried as the search progressed. In this case, it looks like if we were to continue searching with Bayesian optimization, we would eventually reach higher scores on the cross vadidation data.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Learning Rate Distribution\n",
    "Next we can start plotting the distributions of hyperparameter values searched. We expect random search to align with the search domain, while the Bayesian hyperparameter optimization should tend to focus on more promising values, wherever those happen to be in the search domain.\n",
    "\n",
    "The dashed vertical lines indicate the \"optimal\" value of the hyperparameter."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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JkgrCGk+SJEmSJEkqiG9Ujae2bdumjh07FjsMSZIkSZKkr43nnntufkqpXVV936jEU8eOHZk5c2axw5AkSZIkSfraiIh3qutzq50kSZIkSZIKwsSTJEmSJEmSCsLEkyRJkiRJkgrCxJMkSZIkSZIKwsSTJEmSJEmSCsLEkyRJkiRJkgrCxJMkSZIkSZIKomGxA5AkSZIkff0tWrSI+fPns3z58mKHImkNGjRowEYbbUTr1q1p0qTJWs1l4kmSJEmSVFBffvklH374IVtssQVNmzYlIoodkqRqpJRYsWIFn332Ge+++y5bbrnlWiWf3GonSZIkSSqojz/+mHbt2tGsWTOTTlI9FxE0btyYtm3b0qpVKxYsWLBW85l4kiRJkiQV1JdffsmGG25Y7DAk1VKLFi34/PPP12oOE0+SJEmSpIJauXIlDRta6UVa3zRq1IhVq1at1RxFTzxFRKrmtbiKsdtFxL0RsTAilkTEjIjoV4y4JUmSJEk15xY7af2Tj9/b+pJyngFcV6FtRfmDiPg28BSwErgcWAQMBh6JiANTSo+ti0AlSZIkSZJUM/Ul8fRWSunWNYy5FGgJdEspvQgQEZOAV4BrI2L7lFIqcJySJEmSJK3XOnbsSMeOHZk+fXqxQ/la69u3L3PmzGHOnDnFDqWoir7VrlRENI6IKqvNRURz4HvA9NKkE0BKaTFwA7AtsNs6CVSSJEmSpCpMnz6diMh5bbjhhnTt2pVx48axcuXKYoe4Xvjzn/9M//792WKLLWjSpAnt27dn9913Z+TIkcyfP7/Y4amW6suKpyOAY4EGEfEx8EfgFymlRdn+LkAT4Okqzn0m+74b8PdCBypJkiRJ0uocffTRHHTQQaSUmDdvHpMmTeJ//ud/mD17NtddV7HKjMo755xzuPzyy+nSpQunnnoqm266KXPnzuWFF15g/Pjx/OhHP6Jt27bFDlO1UB8ST38H/g94A2gBHAScDuwdEbtnVzV1yI59v4rzS9s2L3SgkiRJkiStSdeuXTn22GPLjk899VS23357brjhBv73f/+Xdu3aFTG6+uujjz7iV7/6Fbvttht/+9vfaNSoUU7/okWLaNCgQZGig88//5yNNtqoaJ+/vir6VruUUs+U0q9SSvemlCallI4CzgN2AYZlhzXLvi+rYoovK4zJERFDImJmRMz8+OOP8xq7pDyZHLmvOooLI+clSZIk1QfNmzenV69epJR48803c/oeffRRjjzySDp16kTTpk1p2bIlAwYM4Iknnqg0T9++fenYsSNz587l6KOPplWrVjRv3pz999+f119/vdL4//znP/zoRz9i4403pkWLFhx66KGVPr+8G264ga5du9K0aVM23nhjBgwYwJNPPllpXEQwaNAgpk6dSu/evWnWrBlbbLEFY8eOBWDhwoWcdNJJbLLJJjRr1oxDDjmEuXPnrvE+vfXWW3z11Vf06dOnUtIJYOONN2bDDXMr9CxbtowxY8aw0047UVJSQsuWLTn00EN54YUXcsZ99dVX/O///i99+vRhs802o3Hjxmy55ZaccsopfPLJJzlj58yZQ0RwwQUX8Mc//pFu3brRtGlTfvazn5WNmTdvHmeccQadOnWiSZMmbLLJJvTv358pU6ZUirum39fXVdETT9W4AlgOHJw9/iL73qSKsSUVxuRIKV2XUuqeUupuVlmSJEmSVAylCZ/WrVvntN90000sWLCA448/nquvvpqzzjqL2bNns++++zJjxoxK8yxZsoQ+ffrQoEEDxowZw2mnncb06dM57LDDWLVqVdm4Tz/9lD59+nD33Xdz3HHHcdlll9GsWTP22WcflixZUmnec845h8GDB9OoUSPGjBnD8OHDefXVV9lnn3148MEHK41/4YUX+OEPf0jfvn258sor6dy5M6NGjeI3v/kN++67LwsXLuSCCy5g6NChPPzwwxx//PFrvEedOnUC4IEHHqhRomrFihUccMABXHjhhfTu3Ztx48YxatQoXn31VfbYYw9mzpxZNnb58uVcccUVdO7cmREjRjB+/Hj69+/PxIkT6du3L8uXL680/7333sspp5zCAQccwPjx4znwwAOBTGKqW7duTJgwgb59+zJu3DhGjBhBixYteOyxx3LmqOn39XVWH7baVZJSWhERc4HSjZulP3FVbacrbatqG54kSZIkqR7qOOovxQ5hteZcdvCaB1Xjiy++YP78+WU1nn73u9/xwgsvsNtuu7HtttvmjL3++utp3rx5TtvQoUPZaaeduPTSS9lrr71y+ubPn8+IESMYOXJkWVu7du0YOXIkjz32GPvvvz8Al19+OXPmzOEPf/gDJ554IpDZ8nfmmWfym9/8JmfOf/3rX1xxxRXsscceTJ06lcaNGwNw8skns+OOO3Lqqafy5ptv5mxzmzVrFk8//TQ9e/YE4KSTTmKrrbbirLPO4vTTT2f8+PE5nzFu3Dj+9a9/sd1221V73zbZZBNOP/10rrnmGrbeemt69uxJr1696NGjB/vuuy+tWrXKGX/NNdcwffp0Hn744bLrLr3OnXfembPPPrvsyX1NmjThgw8+oGnTpjn3effdd+fkk0/m3nvv5Uc/+lHO/K+88govvfQSO+ywQ077qaeeyty5cyt9LmRWVpVX0+/r66xerniKiBJgC+DDbNMsMtvselcxvFf2fWYVfZIkSZIkrVOjR4+mXbt2bLLJJnTp0oUJEyZw+OGHc//991caWz7ptHjxYj755BMaNGhAz549efbZZyuN32CDDTjjjDNy2vr16wfAv//977K2e++9l0033bTSSqNzzjmn0pz33XcfKSVGjhxZlnQC6NChA4MGDeKdd96ptHWtd+/eZUkngMaNG9OjRw9SSpXiK02elY+vOuPHj2fSpEnsvvvu/P3vf+eKK67ghz/8Ie3bt+ecc87JWSV06623sv3229OtWzfmz59f9lq+fDn9+/fnySefZOnSpUBme2Bp0mnVqlV8+umnzJ8/v+zeVXWvDz744EpJpwULFvDwww9zwAEHVJk02mCDDSod1+T7+jor6oqniGiTUvqkiq6LycT2Z4CU0uKI+DNweETsmlL6Z/b8DYGTgX/jE+0kSZIkSfXAkCFD+OEPf8iKFSuYNWsWY8eO5b333qOkpKTS2DfffJPzzjuPRx55hE8//TSnL6Jy3dIOHTpUmqdNmzYAObWK3nrrLXbbbbdKxbjbt29Py5Ytc9refvttAHbaaadKn7fzzjuXzde9e/ey9tJtceWVrkjaeuutq2yvWEupKhHBcccdx3HHHcfy5ct56aWXePTRR/n1r3/N5ZdfTsuWLTn33HMBmD17NkuXLl1tsfb58+fzrW99C4A777yTK6+8khdeeIEVK1bkjFu4cGGlcyuuTgN44403SCnx3e9+d43XAjX/vr7Oir3V7hcR0QuYBrwLbEjmqXb7AM8CV5cbey6wL/BoRIwDPgMGk9lqd3BKKa3LwCVJkiRJqkrnzp3Zb7/9ADjwwAPZc8892XPPPRk6dCh33HFH2bjFixfTp08flixZwplnnskuu+zCRhttxAYbbMCll17K1KlTK829uqe6VfyzuKrEVVXj6vLn9OriqK6vtp/TuHFjunfvTvfu3Rk4cCA77LADEydOLEs8pZTYZZdduOqqq6qdozQpdffdd3PkkUfSo0cPfvOb3/Ctb32LkpISVq1axQEHHFBpixxAs2aVn2FWeg3V3duKavN9fV0VO/E0HdgROAFoA6wis3rpPOCqlFLpE+tIKb0REXsAlwGjgMbA88ABKaXHkCRJkiStN9amhtL6Zvfdd+e4445j0qRJnHHGGey+++4APP7448ydOzenDlOpX/ziF2v1mZ06deL1119n1apVOcmPDz74gEWLFuWM/fa3vw1kahqV/rvUq6++WjZfMW233Xa0atWK99//b3nnzp078/HHH9OvX79KW9wquuWWWygpKWHatGk5CaXXXnutVnF07tyZiKi09VDVK2qNp5TSfSml/VNKm6eUSlJKzVNK30kpjSmfdCo3fnZK6bCUUsuUUrOU0p4mnSRJkiRJ9d0vf/lLGjRowPnnn1/WVpoQqrjy5dFHH62y5lBtHHbYYXz44YdMmjQpp33s2LGVxn7ve98jIrjiiitytqB98MEH3HjjjWy11VY13lq2NubNm8eLL75YZd+MGTNYsGABO+64Y1nb8ccfz7x586pd8fThhx+W/btBgwZERM7KppQSl1xySa1ibN26NQceeCAPPfRQpSfYlc6pXMVe8SRJkiRJ0tfeNttsw1FHHcVtt93GjBkz2Guvvdhzzz3ZbLPNGD58OHPmzGGLLbbgxRdf5JZbbmGXXXZh1qxZdf68kSNHMnnyZAYPHsxzzz3HTjvtxPTp03n66adp27ZtztjtttuOESNGcPnll9OnTx+OPPJIPv/8c6677joWL17MbbfdttotY/ny3nvvsdtuu9GzZ0/23XdfOnXqxLJly/jnP//JbbfdRqNGjRgzZkzZ+GHDhjFlyhRGjBjB1KlT6devHy1atODdd9/l8ccfL1vhBHDEEUdw11130a9fP44//nhWrFjBvffeyxdffFHrOK+55hp23313DjzwQE444QS6devG0qVLefbZZ+nYsWOVyb1vMhNPkiRJkiStA+eddx633347559/PtOmTaNly5Y88sgjjBw5kquvvpqVK1fSrVs3HnzwQSZOnLhWiadWrVoxY8YM/ud//odJkyaRUqJv375MmzaNfffdt9L4sWPHss022zBhwgRGjRpF48aN6dmzJ5MnTy57Kl2hbb/99lx77bVMmTKFO+64gw8//JAVK1bQvn17vv/97zN8+PCclVeNGjXiL3/5CxMmTOCWW25h9OjRQKagd48ePTjhhBPKxh511FF8/vnnjBs3jrPPPptWrVpx6KGHctlll5UV+66prbfempkzZ3LxxRfz4IMPMmnSJFq1asWuu+7KkCFD8nMzvkbim7QMrHv37mnmzJnFDkNSRZMrFOY7pm7/XYoLc+dJo785/32TJEmqz2bPnl3psfSS1g81+f2NiOdSSt2r6itqjSdJkiRJkiR9fZl4kiRJkiRJUkFY40lS8fX4fV6m+f0h+ZlHkiRJkpQfJp4kFd82+SnAN6SbhfwkSZIkqT5xq50kSZIkSZIKwsSTJEmSJEmSCsLEkyRJkiRJkgrCxJMkSZIkSZIKwuLikopvwXO5x6271Wma5+bmztOtQ93mkSRJkiTlh4knScX3cPfc42NSnabpfn3uPGl03eaRJEmSJOWHW+0kSZIkSZJUECaeJEmSJEmSVBAmniRJkiRJ0nrppptuIiKYPn163ufu27cvHTt2zPu8NTVnzhwiggsuuKBoMeSDiSdJkiRJkvJg+vTpRETOq6SkhE6dOnHiiScye/bsYodYEF999RU333wz/fr1o02bNjRp0oQtt9yS4447jhdffHGt558+fToXXHABn376aR6irV/mzJnDBRdckJf7VF9ZXFySJEmSpDw6+uijOeiggwBYunQpL730EjfccAN33XUXs2bNYquttipyhPmzZMkSfvCDHzBlyhR69uzJqFGjaN26Na+//jo33ngjt99+O1dffTWnnHJKnT9j+vTpXHjhhQwaNIiWLVvm9B133HEcddRRNG7ceG0vpZJHH32UlAr7wKI5c+Zw4YUX0rFjR77zne/k9G211VYsXbqUhg3X79TN+h29JEmSJEn1TNeuXTn22GNz2jp37sywYcO4++67Oeuss4oUWf4NHTqUKVOmcN5553HJJZfk9I0YMYJ9992X0047jc6dO7Pffvvl/fMbNGhAgwYN8j4vUJBkVm2Urphb37nVTpIkSZKkAuvQoQNQOZkxYcIEBgwYwOabb07jxo1p3749xx57LHPmzCkbs3z5ctq1a8eee+5Z5dyXX345EcGMGTPK2pYtW8aYMWPYaaedKCkpoWXLlhx66KG88MILOeemlPj1r39Nly5d2GijjWjRogXbbbcdJ510EitWrFjtNb300kvceuut9OzZk4svvrhSf9u2bZk8eTIAo0aNyumLCAYNGsRjjz1Gr169aNasGZttthnDhg1jyZIlZeMGDRrEhRdeCMDWW29dtoWxtO5RVRtPUj0AACAASURBVDWeStsef/xxLrroIrbaaiuaNm1Kz549eeaZZwB44okn2HPPPWnevDnt27evMv6KNZ5K563uVRrD559/zi9+8Qt69uxJ27ZtadKkCdtssw2jRo3iiy++yJlvn332AeDEE08sm6dv375A9TWeVq5cydixY9lxxx0pKSmhTZs2/OAHP2DWrFk548qf/8ADD7DbbrtRUlJC+/btGTFiBCtXrqx0zYXgiidJkiRJkvLoiy++YP78+UBmq93LL7/MeeedR9u2bRk4cGDO2F/96lf06tWLM844g9atW/Pyyy9zww03MHXqVGbNmkWbNm1o3LgxJ5xwAldeeSWvvfYa22+/fc4cN954I9tuuy177bUXACtWrOCAAw7gqaee4rjjjuP0009n0aJFXH/99eyxxx789a9/pXv37gBccsklnH/++Rx66KEMHTqUBg0a8Pbbb3P//fezbNkyGjVqVO113nXXXQCcfPLJRESVY3baaSd69+7NU089xTvvvJOzzfD555/nT3/6E4MHD+b4449n2rRpjB8/npdffpkpU6awwQYb8NOf/pTPPvuMe+65h3HjxtG2bVsAunTpssbvYdSoUaxatYphw4axfPlyrrzySvbff39uvvlmTjrpJIYMGcKPf/xj7rzzTs4//3y23nrrSivVyuvTpw+33HJLTltKiZ///OfMnTu3LLb333+fG264gYEDB3LMMcfQsGFDnnjiCS6//HJeeOEFHnnkkbL5fv7znzNmzBiGDBlS9v1tuummq72u0pj79+/PKaecwrx587j22mvp3bs3M2bM4Lvf/W7O+AcffJAJEyYwdOhQfvKTn3Dffffxq1/9ilatWvHzn/98jfdxbZl4kiRJkiStexdsXOwIVu+CRXU+dfTo0YwePTqnbccdd2TGjBlsttlmOe2zZs2iefPmOW3f+9732G+//Zg4cSIjR44EYMiQIVx55ZX84Q9/4PLLLy8b+7e//Y3XXnuNsWPHlrVdc801TJ8+nYcffpj999+/rP3UU09l55135uyzzy5bnXPPPfewww47cP/99+fEcNlll63xOl9++WUgs7Vwdbp27cpTTz1Vqb7VrFmzuOeee/j+979fFt+wYcMYP348d955J0cddRS9e/emS5cuZeNq85S5VatW8cwzz5StMttxxx057LDDOOKII3j66afZbbfdADjppJPYaqutuPbaa1ebeOrUqROdOnXKaRs5ciTvvfce48aNY+eddy4b95///CcnaXfaaafxy1/+kksuuYS///3v9OjRg06dOtG/f3/GjBlD7969V/vZpaZMmcKdd97Jj370I+64446yhN+RRx5J165dOeOMM3JWvgG88sorvPLKK2X3bujQoeyyyy5cffXV6yTx5FY7SZIkSZLyaMiQIUyZMoUpU6bw5z//mbFjxzJ//nwOOugg3nnnnZyxpUmnr776ikWLFjF//nx23XVXNt54Y5599tmycdtuuy177703kyZNytkiNXHiRBo2bMgJJ5xQ1nbrrbey/fbb061bN+bPn1/2Wr58Of379+fJJ59k6dKlAGy88ca8//77PPnkk7W+zs8++6xsjtUp7V+0KDeZt91225UlnUqVbsm75557ah1PRaecckrO1sbSFUW9evUqSzpBZvtjjx49+Pe//12r+W+44QauuOIKhg4dyplnnpkzX2nSaeXKlSxcuJD58+eX1bgq/73WVul9Oe+883JWmXXp0oVDDjmEJ598ko8//jjnnIoJu4hgn332Yd68eSxevLjOsdSUiSdJkiRJkvKotJD2fvvtxyGHHMLIkSO5//77efvttznnnHNyxk6dOpW+ffvSvHlzWrZsSbt27WjXrh2LFi1i4cKFOWOHDBnChx9+yAMPPADA4sWLufPOOznkkENytmfNnj2b1157rWyu8q8//OEPrFq1qmwr4JgxYygpKWGvvfZi880358c//jGTJ09m+fLla7zOFi1aAJUTShVVl6DaYYcdKo1t3749LVu25K233lrj569JxdVJrVq1AjK1oipq1aoVn3zySY3nfvzxxznllFMYMGAAV199daX+CRMm0KVLF5o0aULr1q1p165dWe2mit9rbbz99ttssMEGVd670hVXb7/9dk57xfsA0KZNG4BaXXNdudVOkiRJkqQC69mzJxtvvDFTp04ta/vHP/7BgAED2GabbbjsssvYeuutadq0KRHBUUcdxVdffZUzx8CBAznjjDOYOHEi3//+97njjjtYsmQJJ598cs64lBK77LILV111VbXxtGvXDoDevXvz5ptv8sgjjzBt2jSmTZvG5MmTueSSS3jyySdp3bp1tXPsvPPO3H333Tz//POr3W73/PPPA7DLLrvktFdXFyqlVO1ctVHd0+7W9il4s2fPZuDAgWy33Xb83//9Hw0b5qZWrrrqKoYPH86AAQM444wz6NChA40bN+b9999n0KBBlb7X2qjLvVnd9ebrXq+OiSdJkiRJ0rq3FjWU1lcrV65k2bJlZceTJ09m1apVPPTQQzmrcJYsWVLlqpgmTZpw/PHHM378eObOncvEiRPZfPPNOeCAA3LGde7cmY8//ph+/fqxwQZr3ui04YYbMnDgwLLC5xMmTOC0005j4sSJjBgxotrzDj/8cC666CImTpzISSedVGUi6dVXX+Wpp56ia9euOfWdSvsq+uCDD1i0aFHOKp3qElTF8NFHH3HwwQfTpEkTHnjggbJVX+XdcsstdOzYkYceeijn/j/88MOVxtb22r797W/zyCOPMHv27EoF1kvvZ1UruorJrXaSiu/bg3NfdTS46+CclyRJklRfTJkyhSVLltCtW7eyttKVKBVXnYwZM6baVTGDBw9m1apVnHPOOTzzzDMMGjSo0oqW448/nnnz5lW74unDDz8s+3fplrvySlcvLViwYLXXtOuuu3L00UfzzDPPcMEFF1TqX7BgQVnB7KqKlf/rX//i3nvvzWkrLZJevvbThhtuWKN4Cu3LL7/ksMMO44MPPuC+++6rttB5gwYNiIic73XlypVV3oPaXlvpfbn00ktz5n/55Ze5//772XPPPctWs9UXrniSVHw9r8vLNNcdmp95JEmSpLXx/PPPc+uttwKwbNkyXnnlFa6//noaNWrEJZdcUjbuBz/4AePGjeOggw5iyJAhNG7cmClTpvDSSy/Rtm3bKufeYYcd2HPPPbn11luJCH7yk59UGjNs2DCmTJnCiBEjmDp1Kv369aNFixa8++67PP7445SUlDBt2rSy+Xr16kXPnj3p0KEDH3zwAddddx2NGzfmqKOOWuO1/v73v+fDDz/koosuYsqUKRx++OG0bt2a119/nRtvvJH58+dz7bXX0r9//0rn7rLLLhx77LEMHjyYzp07M23aNP70pz+x9957c+SRR5aN69WrFwDnnHMOP/7xjykpKWHnnXcuq2m0rowePZpnnnmGgQMH8sYbb/DGG2/k9Pfv359NN92UI444gnPPPZcDDzyQww8/nM8++4zJkyfnPOWu1I477shGG23EhAkTaNasGS1btmSTTTahX79+VcbQv3//sifaLVy4kEMOOYR58+Zx7bXXUlJSwvjx4wty7WvDxJMkSZIkSXl0++23c/vttwOwwQYb0KZNG/r378+5556b8zS1PfbYg7vuuouLL76YX/7ylzRt2pT99tuPJ554gj59+lQ7/5AhQ3jyySfZZ599qiwc3ahRI/7yl78wYcIEbrnlFkaPHg1Ahw4d6NGjR84T8IYPH86DDz7I+PHjWbRoEZtssgm9evXi3HPPZdddd13jtW600UY8+uijTJo0iZtvvpkxY8awePFiNt10UwYMGMDw4cP5zne+U+W5Xbt25aqrruK8887jd7/7HS1atOD0009nzJgxOVvU9thjD8aOHcvvfvc7Bg8ezMqVKxk9evQ6TzyVrhS76667uOuuuyr1T5s2jU033ZQRI0aQUmLixIkMGzaMzTbbjCOPPJITTzyRHXfcMeecpk2bcscdd/CLX/yCM888k2XLlrH33ntXm3gCuO222+jatSs33XQTw4cPp3nz5uy9995cfPHFlepo1QexLgpJ1Rfdu3dPM2fOLHYYkiRJkvSNMnv27CqfwqW6ufPOOznyyCOZPHkyRx99dLHDqZOI4IQTTuCmm24qdihag5r8/kbEcyml7lX1WeNJkiRJkqT1yLXXXkvbtm05/PDDix2KtEZutZMkSZIkqZ776KOPePzxx5kxYwZ//etfufTSS2nSpEmxw5LWyMSTJEmSJEn13KuvvsoxxxxDy5YtGTp0KMOHDy92SFKNmHiSVHyTI/f4mLrVnosLc+dJo785NewkSZL09da3b1++TjWav07XotWzxpMkSZIkSZIKwsSTJEmSJEmSCsLEkyRJkiRJkgrCxJMkSZIkqeCs6SOtf/Lxe2viSZIkSZJUUI0aNWLp0qXFDkNSLS1dupQmTZqs1RwmniRJkiRJBbXJJpvw/vvv88UXX7jySarnUkqsWLGCBQsW8N5779GmTZu1mq9hnuKSJEmSJKlKLVq0AGDu3LmsWLGiyNFIWpOGDRtSUlLClltuSUlJydrNlaeYJEmSJEmqVosWLcoSUJK+OdxqJ0mSJEmSpIIw8SRJkiRJkqSCMPEkSZIkSZKkgjDxJEmSJEmSpIIw8SRJkiRJkqSC8Kl2koqvVde8TNO1fX7mkSRJkiTlh4knScV34HN5mea5IfmZR5IkSZKUH261kyRJkiRJUkGYeJIkSZIkSVJBmHiSJEmSJElSQZh4kiRJkiRJUkGYeJIkSZIkSVJB+FQ7ScX3ULfc4zo+5a7bdbnz+JQ7SZIkSSouE0+Sim/h83mZ5vkP8jOPJEmSJCk/3GonSZIkSZKkgjDxJEmSJEmSpIIw8SRJkiRJkqSCMPEkSZIkSZKkgjDxJEmSJEmSpIIw8SRJkiRJkqSCMPEkSZIkSZKkgjDxJEmSJEmSpIIw8SRJkiRJkqSCqFeJp4hoFhFvR0SKiGuq6N8uIu6NiIURsSQiZkREv2LEKkmSJEmSpNWrV4kn4CKgbVUdEfFt4CmgN3A5MALYEHgkIvZbZxFKkiRJkiSpRupN4ikiugJnAqOrGXIp0BLYP6V0aUppArAXMBe4NiJi3UQqSZIkSZKkmqgXiaeIaABcDzwM3F1Ff3Pge8D0lNKLpe0ppcXADcC2wG7rJlpJkiRJkiTVRMNiB5B1FrA9MLCa/i5AE+DpKvqeyb7vBvw9/6FJKrhjUl6mSaPzM48kSZIkKT+KvuIpIrYGLgQuSinNqWZYh+z7+1X0lbZtnufQJEmSJEmStBaKnngCfgu8DVy1mjHNsu/Lquj7ssKYHBExJCJmRsTMjz/+uO5RSpIkSZIkqVaKmniKiGOBAcDQlNKK1Qz9IvvepIq+kgpjcqSUrkspdU8pdW/Xrl3dg5UkSZIkSVKtFK3GU0Q0IbPK6UFgXkRsk+0q3TK3cbZtPpkn15XvK6+0rapteJIkSZIkSSqSYq54agq0Aw4G/l3uNT3bf2z2+GRgFpltdr2rmKdX9n1mAWOVJEmSJElSLRXzqXZLgB9W0d4OmAA8DEwEXkopLY6IPwOHR8SuKaV/AkTEhmQSU//GJ9pJ669nh+Qe97yuTtMM+XPuPNcdWrd5JEmSJEn5UbTEU7am058qtkdEx+w/30wple8/F9gXeDQixgGfAYPJbLU7OKXkc9Sl9dWb1+ce1zHxdP3zufOYeJIkSZKk4irmiqdaSSm9ERF7AJcBo4DGwPPAASmlx4oanCRJkiRJkiqpd4mnlNIcIKrpmw0ctk4DkiRJkiRJUp0Us7i4JEmSJEmSvsZMPEmSJEmSJKkgTDxJkiRJkiSpIEw8SZIkSZIkqSBMPEmSJEmSJKkgTDxJkiRJkiSpIEw8SZIkSZIkqSBMPEmSJEmSJKkgTDxJkiRJkiSpIEw8SZIkSZIkqSBMPEmSJEmSJKkgGhY7AEnigJl5mWbm4PzMI0mSJEnKDxNPkoqvdbe8TNOtQ37mkSRJkiTlh1vtJEmSJEmSVBAmniRJkiRJklQQJp4kSZIkSZJUECaeJEmSJEmSVBAWF5dUfG9cl3u8zZA6TXPdc7nzDOlWt3kkSZIkSflh4klS8f39p7nHdUw8/fSB3HlMPEmSJElScbnVTpIkSZIkSQVh4kmSJEmSJEkFYeJJkiRJkiRJBWHiSZIkSZIkSQVh4kmSJEmSJEkFYeJJkiRJkiRJBWHiSZIkSZIkSQVh4kmSJEmSJEkFYeJJkiRJkiRJBWHiSZIkSZIkSQVh4kmSJEmSJEkFYeJJkiRJkiRJBWHiSZIkSZIkSQXRsNgBSBI9fp+XaX5/SH7mkSRJkiTlh4knScW3zZC8TDOkW37mkSRJkiTlh1vtJEmSJEmSVBAmniRJkiRJklQQJp4kSZIkSZJUECaeJEmSJEmSVBAWF5dUfAueyz1u3a1O0zw3N3eebh3qNo8kSZIkKT9MPEkqvoe75x4fk+o0Tffrc+dJo+s2jyRJkiQpP9xqJ0mSJEmSpIIw8SRJkiRJkqSCMPEkSZIkSZKkgjDxJEmSJEmSpIIw8SRJkiRJkqSCMPEkSZIkSZKkgjDxJEmSJEmSpIIw8SRJkiRJkqSCMPEkSZIkSZKkgjDxJEmSJEmSpIIw8SRJkiRJkqSCMPEkSZIkSZKkgjDxJEmSJEmSpIJoWOwAJIlvD87LNIO75mceSZIkSVJ+REqp2DGsM927d08zZ84sdhiSJEmSJElfGxHxXEqpe1V9brWTJEmSJElSQZh4kiRJkiRJUkGYeJIkSZIkSVJBmHiSJEmSJElSQZh4kiRJkiRJUkE0LHYAksTkyD0+pm5P24wLc+dJo785T+2UJEmSpPrIFU+SJEmSJEkqCBNPkiRJkiRJKggTT5IkSZIkSSoIE0+SJEmSJEkqiKImniJiu4i4LSJmR8SiiPgiIl6LiKsion014++NiIURsSQiZkREv2LELkmSJEmSpNUr9lPttgDaA/cA7wErgV2AIcBREfGdlNJHABHxbeCp7JjLgUXAYOCRiDgwpfRYEeKXJEmSJElSNYqaeEopPQ48XrE9Iv4K3AkMIpNkArgUaAl0Sym9mB03CXgFuDYitk8p+ex0SZIkSZKkeqK+1nh6J/veCiAimgPfA6aXJp0AUkqLgRuAbYHd1nWQkiRJkiRJql69SDxFRElEtI2ILSJiAPD7bNeD2fcuQBPg6SpOfyb7buJJkiRJkiSpHqkXiSfgZOBj4D/AI2S21B2bUpqR7e+QfX+/inNL2zYvaISSJEmSJEmqlWIXFy91L/AasCHwXTLb6tqV62+WfV9WxblfVhiTIyKGkClWzpZbbpmPWCVJkiRJklQD9SLxlFJ6j8xT7QDujYi7gH9ERNOU0qXAF9m+JlWcXpJ9/6KKPlJK1wHXAXTv3t3i45IkSZIkSetIfdlqlyOl9BLwAnBqtmlu9r2q7XSlbVVtw5MkSZIkSVKR1IsVT9VoCrTO/nsWmW12vasY1yv7PnNdBCWpAFp1zcs0XdvnZx5JkiRJUn4UNfEUEZullOZV0b4PsDMwHSCltDgi/gwcHhG7ppT+mR23IZnC5P8G/r7OApeUXwc+l5dpnhuSn3kkSZIkSflR7BVPv42I9sBU4B0y9Zq6AUcBnwPDy409F9gXeDQixgGfAYPJbLU7OKVk/SZJkiRJkqR6pNiJp9uBE4DjyDzFLpFJQP0euCKl9G7pwJTSGxGxB3AZMApoDDwPHJBSemxdBy5JkiRJkqTVK2riKaV0J3BnLcbPBg4rXESSJEmSJEnKl3r5VDtJkiRJkiSt/2qdeIqIKRFxZEQ0LkRAkiRJkiRJ+nqoy1a7bkA/YGFE3ApMTCnNym9Ykr5RHuqWe1zHp9x1uy53Hp9yJ0mSJEnFVZfE02bA4cBJwM+An0XETOAG4PaU0uI8xifpm2Dh83mZ5vkP8jOPJEmSJCk/ar3VLqW0PKV0R0qpP9AJuATYlMyT6D6IiInZp89JkiRJkiTpG2ytiounlN5JKY0GtgYOAKYBg4C/RsSrETEsIpqvfZiSJEmSJEla3+TrqXbfAb4H7AUE8CbwFTAOeCMids/T50iSJEmSJGk9UefEU0S0jIjTIuJ5YCZwMvAIsF9KaduU0s7AfsAXwLV5iVaSJEmSJEnrjVoXF4+IfmQKi/8AKAFeB0YCN6WUPik/NqU0NSIuw8STJEmSJEnSN05dnmr3GLAMuBu4LqX0xBrGvwH8rQ6fI0mSJEmSpPVYXRJPw4GbU0oLajI4pTSNTNFxSZIkSZIkfYPUpcbTRkCH6jojYqeIOL/uIUmSJEmSJOnroC6Jp9FAl9X075wdI0mSJEmSpG+wuiSeYg39JcDKOswrSZIkSZKkr5Ea1XiKiBZAy3JNbSJiyyqGtgZ+DPwnD7FJkiRJkiRpPVbT4uJnAaV1mxLw6+yrKgGMXMu4JEmSJEmStJ6raeJpevY9yCSg7gFeqjAmAYuBZ1JKT+UlOkmSJEmSJK23apR4Sik9ATwBEBFbAb9LKT1byMAkfYMck/IyTRqdn3kkSZIkSflR0xVPZVJKJxYiEEmSJEmSJH29rDHxVFpEPKX0bvnjNSkdL0mSJEmSpG+mmqx4mgN8FRHNUkrLs8c12c/SYC3ikiRJkiRJ0nquJomni8gkmlZWOJYkSZIkSZKqtcbEU0rpgtUdS5IkSZIkSVWpdXFxScq7Z4fkHve8rk7TDPlz7jzXHVq3eSRJkiRJ+VHrxFNEbANsk1J6uFxbT+AXQGvg5pSSf+1Jqrk3r889rmPi6frnc+cx8SRJkiRJxVWXFU9jySSYHgaIiLbAQ8CGwFLgtxHxUUrp3rxFKUmSJEmSpPXOBnU4pzvwWLnjo4EWQFegHfAsMGztQ5MkSZIkSdL6rC6Jp3bA3HLHBwB/Sym9nFJaDtwB7JiP4CRJkiRJkrT+qkviaQnQEiAiGgB7An8t17+UzAooSZIkSZIkfYPVJfH0CnBcRLQBBpOp7TSlXP9WwMd5iE2SJEmSJEnrsboUF78CuA/4KHv8AjCjXP8A4Pm1jEuSJEmSJEnruVonnlJKf4mIfsBhwCLgmpRSAsiugnoPmJTXKCVJkiRJkrTeqcuKJ1JKfyW3rlNp+yfA4WsblCRJkiRJktZ/danxJEmSJEmSJK1RnVY8RURv4HSgM9AGiApDUkrp22sZmyRJkiRJktZjtU48RcTx/D979x0fV3Wg//9zpo/ayLIty5ZsbDDYgE2zMYROgNCTsCRZQnoCJpts2u4vW76bhGWTbHY3S9hNh5BCCgkJbQMEQg0djE2zKcaAq1wkWV2j6ef3x73SzMiSrDKjkeTn/eK+7j3nnnvvMbbH0qNzzoWfA0ngDWBboTslIiIiIiIiIiJT31hGPP0LsBE421q7s8D9ERERERERERGRaWIsazwdBPxIoZOIiIiIiIiIiAxnLCOedgDBQndERA5g560tyG3WXlmY+4iIiIiIiEhhjCV4+jHwIWPMddbadKE7JCIHoJoVBbnNinmFuY+IiIiIiIgUxliCp3XApcAaY8wPgM3APgGUtfaxcfZNRERERERERESmsLEETw/lHN8I2AHnjVvnHWunRERERERERERk6htL8PSJgvdCRERERERERESmnVEHT9bam4rRERERERERERERmV7GMuJJRKSw3rwhv7x49Zhuc8O6/PusXjG2+4iIiIiIiEhhjCl4MsbMB64B3gXUAudZax82xswG/hP4kbX2ucJ1U0SmtTVX5ZfHGDxddXf+fRQ8iYiIiIiIlJZntBcYYxYBa3HebPcKOYuIW2ubgZXAFYXqoIiIiIiIiIiITE1jGfH0TSADLAN6gaYB5/8EXDzOfomIiIiIiIiIyBQ36hFPwNnAD6212wE7yPmtQMO4eiUiIiIiIiIiIlPeWIKnKmDXMOcDaNFyEREREREREZED3liCp+3AkcOcPxF4c2zdERERERERERGR6WIswdPtwCeNMcty6iyAMeZS4P3A7wvQNxERERERERERmcLGEjx9E9gBPAv8Gid0+idjzNM4gdNLwLUF66GIiIiIiIiIiExJow6erLWdwDuAG4GVgAHOAZYAPwTOtNbGCtlJERERERERERGZesa0CLgbPn0B+IIxZjZO+NRsrR3sLXciIiIiIiIiInIAGnXwZIw5CbgQOAznDXedwOvAPcAzBe2diIiIiIiIiIhMWSMOnowxVcBvgfNwRjgN9P+MMfcAH7LWdhWofyIiIiIiIiIiMkWNZsTTrcDZwBPAT4GXcUY7VQFHAVcAFwG3ABcUtpsiIiIiIiIiIjLVjCh4MsacixM6XWut/fIgTV4AbjLG/DfwJWPMOdbaBwrYTxERERERERERmWJGOuLpg8BW4B/20+4fgEuBywEFTyIyMquuL8htrr+oMPcRERERERGRwhhp8LQCuHN/b62z1maMMXfijI4SERmZxasLcpvVKwpzHxERERERESkMzwjb1QMbR9h2I9Awtu6IiIiIiIiIiMh0MdLgqQoY6ZvquoCKsXVHRERERERERESmi5EGTx5g2Gl2Y7yviIiIiIiIiIhMUyNd4wngAmNM3QjarRhrZ0REREREREREZPoYTfB0ubuNxIhGRxljDgM+DLwLOAQIAW8BfwD+x1rbM6D9EuA/gdOBAPA8cLW19uER9ktEJqPWdfnlmrHl1+t25t9nxTzl4CIiIiIiIqU00uDpzCI9/5PAZ4E/Ar8Bku6zvgF8wBhzorW2F8AYcwjwFJAC/gvoAK4E/myMOd9a+2CR+igixXbfyvzy5aOZ2Zu18if597FXj+0+IiIiIiIiUhgjCp6stY8W6fm3At+y1nbk1P3YGLMJ+BfgU8D33fpvAdXACmvtiwDGmF8CrwA/MMYstdbqu0wRERERERERkUmipIuAW2vXDgid+tzi7pcBGGPKgXcDf+kLsDPUjQAAIABJREFUndzru4EbgcOA44vcXRERERERERERGYXJ+va5Bne/x90fBQSBpwdp+4y7V/AkIiIiIiIiIjKJTLrgyRjjBb6Gs5bTzW71PHffOMglfXX1Re6aiIiIiIiIiIiMwqQLnoD/AU4Evmat3ejWlbn7+CDtYwPa5DHGrDbGrDXGrG1ubi5sT0VEREREREREZEiTKngyxnwd+FvgBmvtt3JORd19cJDLQgPa5LHW3mCtXWmtXTl79uzCdVZERERERERERIY1aYInY8y/Al8Bfg58esDpne5+sOl0fXWDTcMTEREREREREZESmRTBkzHmauBq4JfAFdZaO6DJepxpdu8Y5PIT3f3a4vVQRERERERERERGq+TBkzHma8C/Ar8CPmGtzQxsY63tBu4CzjDGHJ1zbQVwBbAJWDMhHRYRERERERERkRHxlfLhxpjPAtcA24AHgcuNMblN9lhrH3CP/xk4C7jfGHMd0AlciTPV7sJBRkmJiIiIiIiIiEgJlTR4Ao539wuAmwY5/yjwAIC19k1jzMnAfwD/BASA54HzrLUPTkBfRURERERERERkFEoaPFlrPw58fBTtXwPeU6z+iIiIiIiIiIhI4ZR8jScREREREREREZmeSj3VTkQEDrmyILe58rjC3EdEREREREQKwxxIa3KvXLnSrl27ttTdEBERERERERGZNowx66y1Kwc7p6l2IiIiIiIiIiJSFAqeRERERERERESkKBQ8iYiIiIiIiIhIUSh4EhERERERERGRolDwJCIiIiIiIiIiReErdQdERLjZ5JcvH9vbNs01+fexVx84b+0UERERERGZjDTiSUREREREREREikLBk4iIiIiIiIiIFIWCJxERERERERERKQoFTyIiIiIiIiIiUhQKnkREREREREREpCgUPImIiIiIiIiISFEoeBIRERERERERkaJQ8CQiIiIiIiIiIkWh4ElERERERERERIpCwZOIiIiIiIiIiBSFgicRERERERERESkKBU8iIiIiIiIiIlIUCp5ERERERERERKQofKXugIgIM44ryG2Om1uY+4iIiIiIiEhhKHgSkdI7f11BbrNudWHuIyIiIiIiIoWhqXYiIiIiIiIiIlIUCp5ERERERERERKQoFDyJiIiIiIiIiEhRKHgSEREREREREZGi0OLiIjL1dDTC7vXQvg3at7p7d0t0w5F/BRd8G0JVpe6piIiIiIjIAU3Bk4iU3r0r8svDveXu2evh3n8E7NBtXv4dm1+9jUVXPQWzDytIF0VERERERGT0FDyJSOm1PT+ydq/cCff+w4iaLkol4SfvhL+6HpZeOI7OiYiIiIiIyFhpjScRmRq2Pwd3XDW6axJd8LvL4ZF/h0ymOP0SERERERGRIWnEk4hMfm1b4LeXQSqWrfP44KjLYMZCqF4A1QtY9POT+VeCfIxA/vWP/ifsegkuuR7C1RPZcxERERERkQOagicRmdx62+A374doS379xf8Lx344r2qLsXzcxlhLmusI4cNkT75xnzP17qP/B9XzJ6DjIiIiIiIioql2IjJ5pRLw+49Cyxv59af+f/uETv0MfN8kOYsolM/OP9f6Ftx2BWTSxemviIiIiIiI5FHwJCKTk7Vw95dg82P59cveB+/8yn4vf8ykYfWjMO+4/BPbn4GnvlfAjoqIiIiIiMhQFDyJyOT0+LXw4q/z6+afCO/5ARgz+DUDRerhE/fCIWfl1z/yTdjzSmH6KSIiIiIiIkNS8CQik0/zRiccyjVjEVx2M/hDo7uXPwTv/SGEZ2Tr0gm4/SpnKp+IiIiIiIgUjYInEZl8nvgfsJlsOVQNH7oVymeO7X6VdXDhd/Lr9qyHR/9j7H0UERERERGR/VLwJCKTS9LA+t/n1130HZi1eHz3XfZXzvpQuZ64DrY/N777ioiIiIiIyJAUPInI5NIWgEwqW565GI54b2HufcG3oXJutmwzcMdVkIgW5v4iIiIiIiKSR8GTiEweKQMdgfy6k78IHm9h7l9WA+/+fn5d61vw4NWFub+IiIiIiIjkUfAkIpNHewBszhvrqurhqL8u7DMOPRtWfjK/bs0N8NYjhX2OiIiIiIiIKHgSkUkijRM85XrH34IvMGjzcTnn685b8nLd9XlIxgr/LBERERERkQOYgicRKb3LLSz6DmRyRjuFa2DFx0Z1G3u1zduGFKyAS34M5DyvfZsz8klEREREREQKRsGTiJReshee/mF+3QmfhkB58Z654ERYtTq/7vH/hmhr8Z4pIiIiIiJygFHwJCKl9+JvoKcpWw5UwKori//c0/8RglXZcqwDHr+2+M8VERERERE5QCh4EpHSSqfgye/m1634uPMGumIrnwmn/l1+3ZoboG1L8Z8tIiIiIiJyAFDwJCKl9crt0L41W/YGnEXFJ8oJn4aqhmw5nYCH/m3ini8iIiIiIjKNKXgSkdLJZOCJ6/Lrjv4gVM2duD74w3DWV/PrNtwGjesmrg8iIiIiIiLTlK/UHRCRA9ibD0DTqzkVBiqax3y71XflLxZ+w8UjfEvd8g/A09+H3euzdfd/FT5+Dxgz9HUiIiIiIiIyLAVPIlI6z/8yv1yZgN2/A347ptv95Pmf5JVHHDx5PHDO1+FX783WbX0S3rgPlpw/pr6IiIiIiIiIptqJSKlEW2HT/fl11YnS9AXgkDNh8dn5dQ98zVn8XERERERERMZEwZOIlMardzoLeffxpyGULl1/AM75NzA5H4stb8ALvxy6vYiIiIiIiAxLwZOIlMbLv88vVyWh1MspzTkSjrk8v+6Rb0G8uzT9ERERERERmeIUPInIxGvbAtuezq+rTJakK/s481/AF86We5rg2R+Vrj8iIiIiIiJTmIInEZl46/+QXw6lIGBL05eBqubBOz6bX/fk95w1qURERERERGRUFDyJyMSyFl66Jb+uapKMdupz8uchVJ0txzvgyf8tXX9ERERERESmKAVPIjKxdr4Aezdlyx4fVE6yN8eFInDKl/Lrnr0eunaXpj8iIiIiIiJTlIInEZlYAxcVP/Rd4J0k0+xyrVoNFXOy5VQvPPbfpeuPiIiIiIjIFKTgSUQmTjoFG27Nrzvqr0vTl/0JlMFpX86vW/cLZ2F0ERERERERGREFTyIycd5+BHqas+VgFRx2Xun6sz/HfQyqF2TLmST85T9L1x8REREREZEpRsGTiEyclwcsKn7Ee8AfKk1fRsIXgDP+X37dy7+DptdL0x8REREREZEpRsGTiEyMeBe8dnd+3WSdZpfrqA/A7KXZss3AI98oXX9ERERERESmEAVPIjIxXr/HWaC7T1UDHHRy6fozUh4vvPMr+XWv3QWN60rTHxERERERkSnEV8qHG2P+GTgOWAEsArZaaxcO0/4E4JvACYAFngL+yVr7YvF7KyLj8tLv8stHvR88bvZ93tqCPGLtlYW5zz6WXgTzjoWdL2TrHv4GfOSO4jxPRERERERkmihp8AT8O9AKPA9UD9fQGHMi8BegEfiaW/23wOPGmJOsteuL2E8RGY/OXbD50fy63Gl2NSsK8pgV8wpzn30YA2d9DX51SbburYdh8+Ow6NTiPFNERERERGQaKPVUu0OstTOttecAO/fT9rtAAjjNWnudtfY64DSckU/XFrmfIjIeG25z1kbqU3cU1B5euv6MxcFnwsIBIdMDX4NMZvD2IiIiIiIiUtrgyVr79kjaGWMWA8cDf7DWNuZc3wj8ATjbGFNXnF6KyLit/31+eSosKj5Q36inXDufh1duL01/REREREREpoBSj3gaqePd/dODnHsGMDjrRInIZNO6GXa9lFNhYNmlJevOuMxfBYdfnF/30DWQipemPyIiIiIiIpPcVAme5rn7xkHO9dXVT1BfRGQ0Xv2//PJBJ0HV3NL0pRDOvgY8OcvjtW+DNT8pXX9EREREREQmsVIvLj5SZe5+sGEFsQFt8hhjVgOrARYsWFD4nonI8F69M798xHv3bfPmDfnlxavH9Kgb1uXfZ/WKsd1nWDMPgZWfhDU5z3rs23DM5VBWU/jniYiIiIiITGFTJXiKuvvgIOdCA9rksdbeANwAsHLlSlv4ronIkNq2wM4XcirMvlPVANZclV8eY/B01d359ylK8ARw+j/CS7+DeKdTjrXD49fCud8szvNERERERESmqKky1a7vjXeDTafrqxtsGp6IlNKrf8wvLzhxak+z61M+C075Yn7dmhucoE1ERERERET6TZXg6Tl3/45Bzp0IWGDdxHVHREZkJNPspqoTPwNVOVl4OgEP/Vvp+iMiIiIiIjIJTYngyVr7JrAWeL8xpm+hcdzj9wMPW2t3l6p/IjKI9m3QOCAPHmya3VTlD8M7v5pft+G2fX/NIiIiIiIiB7CSrvFkjPkIcJBbnA0EjDFfcctbrbW/ymn+BeAR4HFjzPfcus/hhGd/PxH9FZFRGDjNbv4JEJlmL5886q/hmR/A7vXZuvu/Ch+/B4wpXb9EREREREQmiVKPePoU8HV3qwWqc8qfym1orX0KOAPYAnzDbfMmcJq19qUJ67GIjMx0nmbXx+OBc76eX7f1Sdj4p9L0R0REREREZJIp6Ygna+0Zo2z/NHBWcXojIgXTsQN2PJdfd8S7S9OXYjvkTFh8Nrz5YLbu/q/AIWeBPzT0dSIiIiIiIgeAUo94EpHpaOA0u4bjIdJQmr5MhHP+DUzOx2nr2/DEd0rXHxERERERkUlCwZOIFN6BMM0u15wjYcUn8uueuA5aNpWmPyIiIiIiIpOEgicRKazOnbD92fy66TrNLtdZX4Py2mw5nYB7/g6sLV2fRERERERESkzBk4gU1sBpdvUroHpBafoykcLVcN638us2PwYv31Ka/oiIiIiIiEwCCp5EpLD2mWb3ntL0oxSWXQoHn5lf9+d/gWhrafojIiIiIiJSYgqeRKRwOnfBtmfy6w6k4MkYuPBa8AazddEWePBfS9YlERERERGRUlLwJCKF89pdQM6aRvOOhRkLS9Wb0ph5CJz25fy652/aN5ATERERERE5APhK3QERmUbGOs1u1fUFefz1FxXmPuN28uedtZ325rzV7u4vwVWPgddfun6JiIiIiIhMMAVPIlIYXbth61P5dUe8d2TXLl5dkC6sXlGY+4ybLwgXXQc3XZSta3oVnv4BnPLF0vVLRERERERkgmmqnYgUxit3kDfNbu7RULOoZN0puUWnwtGX59f95T+geWNp+iMiIiIiIlICCp5EpDDW/yG/vOzS0vRjMnnXNyA8I1tO9cKtn4JkrHR9EhERERERmUAKnkRk/FrfhsZ1+XUKnqB8Jpz7rfy6PevhwatL0x8REREREZEJpuBJRMZvw2355QUnQaShNH2ZbI6+DJa9L7/u2R/DxntL0x8REREREZEJpMXFRWR8rIX1t+bXLR/laKfWAaOlalaMqSvrdubfZ8W8sd2noIyBi74DO56D9q3Z+js/A3/zJFTNK13fREREREREikzBk4iMz55XoPn1bNnjgyMuGd097luZX77cDt5uP1b+JP8+9uqx3afgQhF438/gZ+dCJuXU9bbC7avho/8HHm9p+yciIiIiIlIkmmonIuOzYcBop4PPdNY2knwNK+HMf8mv2/I4PHFdafojIiIiIiIyARQ8icjYWQvrB6zvtPx9g7cVOPmLsOj0/LpH/h22PVua/oiIiIiIiBSZgicRGbvta6BjW7bsC8HSC0vXn8nO44FLroeynBFhNg23XQG9baXrl4iIiIiISJEoeBKRsRs4ze6w8yBYWZq+TBVVc+G9P8qv69gGt3wEUvHS9ElERERERKRIFDyJyNikU/DKHfl1mmY3MoedCyf8TX7dlsfh/z7rTF8UERERERGZJhQ8icjYbH4Uepqz5WAEFp9Tuv5MNedcA/NPyK9b/wd4+Oul6Y+IiIiIiEgR+ErdARGZojYMWFT88IvBH5rQLiTTGXa1x9jRHmVne4xI8jK8tgZDCGODfPRna4gl0kSTKaKJNLFEmkQ6Q9DnJej3EPZ7Cfm97t5DyO+lLOBlTlWIukiIeZEwdZEQcyMhImE/xpjCdd4XhMt+Cz89B1rfytY/fi1UL4AVHy/cs0REREREREpEwZOIjF4yBq/dlV+3/NKiPa6lO876xg5eaezgjT3dNLb30tjWy56uWN7MtGo+nHfdY280Uyghv4e5kTBzIyEOra1gSV0VS+dWsmROJeXBMX6Uls+ED98KN54D0ZZs/d1/B1X1cKhGkImIiIiIyNSm4ElERm/T/RDvzJbLa2HhaQW5dVNyButf28OGxk7WN3awobGD3Z2xgtx7PGLJDJtbetjc0sNTb+3NOze/JszSuiqW1lWytK6KYxdUM686PLIb1xwMl98Cv7gIUr1OnU3D7z8Gn7wX5h5d4F+JiIiIiIjIxFHwJCKjN/BtdkdeAt6xfZy0RxM81X4yj3cfwxPdx7I9UQevrS1AJyfO9tZetrf28sCre/rr6qvDrDhoBscvnMHKhTUcNqcSr2eIqXoNK+HSG+GWDwPuEK5kD/zmA3DFA87UOxERERERkSnI2APoDUorV660a9dOrW9oRSadWCd8ezGk49m6Tz0A81eN6PJ4Ks26rW08samFJ95sYX1jx7he5Da7Mkh9dZj6GWFufvX7pE0zGaJYE+fuy28nHHDWbQr7vYQDXgJeD/FUhlgyTSyZoTeZJpZM9++7Yil2d8TY1RFjd2cvu9pj7OzoJZbMjL2TQGXI5wZRNZx0yEyOaqjeN4h69nq49x/y6yIL4KN3wsxDxvV8ERERERGRYjHGrLPWrhzsnEY8icjovPbH/NCpegE0HD/sJZ2xJA+9tod7Xt7Nk2+20JtMj+qRAa+HJXWVLKuPcOS8KhbOLKd+hrPeUsjv7W/3w2t+nnfdmUtrR/WcoVhr6exNsauzly0tUTbu7uL13Z1s3N3Flr09ZEYQnHXFUvxlYzN/2eisO1Vd5ueUxbM47bDZnH7YbOZUheCEq6BtKzzzg+yFHdvg5+fDR+6AOUcW5NcjIiIiIiIyURQ8icjoPPfT/PKy98Egb3vr6HXCpj+t38Vjb7SQSI9sxFDAJDm8YTbL66tYNi/CsvoIh82pJODzFKL3Y2KMIVLmJ1LmZ2ldFectq+s/F0um2bSnm9fcIOrlHe28tL1jv7/e9miSu1/exd0v7wJgaV0lpx82m9MO/SIndu7E++od2cbde+DnF8CHb4eGFUX5NYqIiIiIiBSDgicRGbnGdbDz+ZwKA8d9pL/UGUvywCt7uGf9Lh7f1EwyPbI5dEurOji1tplTaptYNbOV8Mk/GlP3rjzuyjFdNx4hv5flDRGWN0T662LJNBsaO1i7tY21W1pZu7WN9mhy2Pu8vruL13d3cf1jb1Md+gDXR3o5oeO+bINYO/zy3fDB38KiwizkLiIiIiIiUmxa40lERu7Oz8CLv8mWF59D5vI/8Mzbe/n92u3cu2E38dT+RzbNqQpyyuLZnHroLE5aPJPaylARO116mYzlreZu1mxp5am39vLEphY6eocPogwZvub7FZ/w/Tn/hDcIH7gJlpxfxB6LiIiIiIiM3HBrPCl4EpGRibbCdw6HVKy/6o9HXMd/bV7Ijrbe/V5+0MwyLlg+lwuXz+XIeVWYQabnHSjSGctLO9p5dGMzj21q5qXt7UOsE2X5ku9WvuC7I682Y3x0nfs/RE78yGAXiYiIiIiITCgFTy4FTyLj8OR34YGv9he329mcHr+ODEOvvbRoVjkXLK/j/GUKm4bTHk3wxJstPLqxmYdfb2JvTyLv/BXee/iK/zf7XHdv2btpPeVqzj1qPrMqghPVXRERERERkTx6q52IjMv2vd1UPPZjZuTU/SZ11qChU8OMMO89pp4Lls/l8LmVCptGoLoswEVHzeOio+aRzlie39bG/a/s5s+v7GFba5Qb0xfSTZh/9/0Uj8n+sOD86B9Zd99rvPuuz3PwIUu4+Oi5nHtkHdVlgRL+akRERERERLI04klEBpXJWB7b1Myvnt5KetP9/ML/X/3n4tbPifHv0UYVAEGfh/OX1fGBlfM58eCZeDwKmwrBWsvGPV3c/8oe7n91Nwt3/Zlr/T8maPLXh2qxVXw++bc8lVmGz2M49dBZXHz0PM45Yg6VIX+Jei8iIiIiIgcKTbVzKXgS2b+OaJI/rNvOr5/Zypa9UQBu9H+bs70v9Le5LX0Kf5/8DEfPr+b9Kxq4+Oh5RMIKOIptR1uUNU8+xMnP/z1zMnvyzqWt4drUB/hR+mKsOxIt4PNwxmGzufjoeZx1eC1lAQ1yFRERERGRwlPw5FLwJDK0N5u6+OkTW7jjhR3Ektk30zWYJh4LfClvitdPl97IKWecx5K6ysI8/OYBI6QuH9vnkrkm/z726mn6+RZtJXrLFZRtfWifUw+lj+Wfk1fQlDcxEsJ+L+88vJaLj5rLGUtqCfm9E9VbERERERGZ5rTGk4gMylrLU2/t5cbH3+aRjc2DtvmQ96G80ClTdzSf+uv3gdZuKp2yGso+dis8/t/wyL8D2d+fs7wv8KDny3wz9SFuSZ8BOL9Pvck097y8i3te3kVF0Mc5R8zh4qPncsri2QR8Qy8QLyIiIiIiMh4KnkQOQIlUhrte2smNT2zmtV2dQ7Yr9yb5SPBRSGfrPKuuVOg0GXg8cPo/QP0KuO0K6G3tP1Vlovyn/ye82/MU/5y6gm12Tt6l3fEUd7zQyB0vNBIJ+znvyDouOnou7zh4Jj6vQigRERERESkcBU8iB5C2ngQ3r9nGTU9toakrPmS7uZEQHzphAR8tf4aKe3OCqVA1LLt0AnoqI7b4LLjqUbj1k7DjubxTJ3tf4SH/P/FjcxnX9Zwz6FsIO3qT3LJ2O7es3c7M8gDnL6/joqPmsWphjRaJFxERERGRcVPwJHIAeLu5m589uZlb1+Wv3zTQ0fOrufLURZx3ZJ0z8uUnn85vcOyHIVBW5N7KqFUvgE/+GZ69Hh7+OiSj/af8mTif4yY+2fACv635LNdvnkXzEKHj3p4Ev35mG79+ZhtzqoJcsHwuFx89j2PnV2M0yk1ERERERMZAi4uLTFPWWp7d3MqNj7/NQ683MdRfdWPg3CPquPK0RRy3YEY2YNj5AtxwRn7jzz0PMw8pfGe1uHjhtG2Bu74Ab/9l0NP28Hfz4mFf5A9bAty7fhdt0eR+b1lfHeaio5wQ6sh5VQqhREREREQkj95q51LwJAeCZDrDPS/v4sYn3mZD49DrN5UFvHxg5Xw+cfJCDppZvm+D21fDy7dky4ecBR+5vQg9RsFToVkLL/4G/vz/INax73mPD1Z+iuSpX+apXXD3Szu575XddMVS+731wpllnL98LucdWcdRDRGFUCIiIiIiouCpj4Inmc46osn+9Zt2d8aGbDc3EuLjJy3kslULiIT9gzfa9RJcfzq5b0vjst/C0gsK2+k+Cp6Ko2sP3PtlePX/Bj8frIJTvgQnfJq4J8jjb7Rw98s7eeDVPfQk0oNfk2NeJMS7jqzj3CPrOH7hDC1MLiIiIiJygFLw5FLwJNPR1r09/PzJLfx+7Xaiw4QFy+sjXHHqIi5YPhf/cAGBtfDL98DmR7N1sw+Hv3kSPN4C9jyHgqfi2vIE3P8VZ/rkYMpmwomfgVVXQihCLJnmkdebuPvlXTz0+p5h1wXrU1Me4JzD53DusjmcdMgsQv4i/VkREREREZFJR8GTS8GTTBfWWtZubePGx9/m/lf3DLt+09mHz+GKUxaxalHNyKZFbXoAfvO+/LoP3QaHnj3+jg9FwVPxZTLwyu3w0DXQvm3wNsGIEz6d+BkonwlATzzFg6/t4a6XdvHYG80k0vsPocJ+LycvnsVZh9dy5pJa6iKhQv5KRERERERkklHw5FLwJFNdKp3hTxt289PH3+alHYOs3eMK+728f2UDnzh5EYtmDbJ+01DSKfjxydD8erbu4DPgI3c6KVaxKHiaOKk4rLkBHvv24Os/AfjLYMXH4R2fhUhDf3VnLMkjrzdx34bd/GVjM73J/U/HAzhyXhVnLa3lzKW1HN1QjcejdaFERERERKYTBU8uBU8yVe3tjvO757bz62e2sqtj6PWb5lQF+dhJC7l81QKqywKjf9C6XzhvROtn4KrHYO5Ro7/XaCh4mnjRVnjqu7DmRkh0Dd7GeOHwi2DVVXDQSXnhY28izeObmrnvld08+OoeOkewMDnArIoAJy+exSmLZ3HKobOYGwkX4lcjIiIiIiIlpODJpeBJppoNjR384qkt/PGlnSRSQ09xOmJuFVeetogLl88j4BvjAs/xbvjecdC9J1t39OVwyY/Gdr/RUPBUOr3tsOYn8MwPobd16HZzlsMJq2H5+8GfHxYl0xmeeXsvf35lNw+91jRsODrQIbPL3RBqNiccXENVaIgF70VEREREZNJS8ORS8CRTQTKd4b4Nu7npqS2s3do2bNuzD6/lU6cczIkHj3D9puE88i149D+yZV8IPvc8ROrHd9+RUPBUevFueP4meOp70LVr6HbhGXDsh+HYj8Lsw/Y5ba3ltV1dPLKxiYde28ML29uHXINsIK/HcHRDhBMOnsnxC2ew4qCaod+8KCIiIiIik4aCJ5eCJ5nMdnX08vvndnDzmq3s6YwP2S7s9/JXx9XzyVMWccjsisI8vHOXM9opGc3Wnfr3cNbXCnP//bl3RX75/HVjus2KG/Lvs2712O5zQEvF4cWb4dkf56/1NZj5Jzgh1JGXQLBy0CZ7u+M8+kYzD73exGMbm+mKj2xKHjgz+5bMqeT4hTWsXDiDVYtqNDVPRERERGQSUvDkUvAkk006Y3n0jSZufnY7D7++h8wwfx3n14T56IkL+cDK+UTKCjwK5I+fg+d/mS2XzYLPvwChqsI+R6YOa2HzY85C5Bv/BHaYt9n5y5zw6ZgPwYJ3gGfw6Z7JdIaXtrfzxJstPLGphRe2t5Me7g/WsaawAAAgAElEQVT9IOqrwxwzv5rlDRGOqo+wrCGi6XkiIiIiIiWm4Mml4Ekmi77RTbc8t42d+1kP55TFs/jYSQt559JavMV4G9ieV+DHp+QHCxf8N6y6svDPkqmpbSus/akTTvYOP/2TqgZYdgksuxTmHjPs2xC7YkmefbvVCaLebOHNpu4xde/gWeUsb4iwvN7ZltZVFT6cFRERERGRISl4cil4klJKpDL8ZWMTv1+7nYdfbxp2dFNZwJlO97F3LOTQOYNPYSqITBp+dQlsfjRbN/NQ+MzT4NU37jJAIgqv/RFe+DVseXz/7WsOdgKoZZdC7eH7bb67I8azm/eydksbz21pZeOerhGvDzVQXVWIJXWVLKmr5LA5lSyZU8ni2grCAe/YbigiIiIiIkNS8ORS8CQTzVrLi9vbueOFRu56aSdt0eSw7ZfWVXL5CQt4zzH1E7Oo8oPXwBPfya+77GZYemHxny1TW+vbzlpQL94MnY37bz9zMRx2Hiy5wFkbyuvb7yUd0STrtrXy3JY2ntvcyss7Okikh5nytx/GwIKaMhbOLGfRrHIOmlnGwlnlLJxZTsOMMH7vGN8IKSIiIiJygFPw5FLwJBNle2uUO19o5I4XGnm7pWfYtmG/l4uPnssHVy3gmPnV43873UitvxVu+1R+3cJT4WN3DTs9SiRPJg1vP+IEUBvvzV+gfiihajj0XbDkfDjknRCuHtGjYsk0r+3qZH1jBy/v6GD9jg42NXUNO3pwpLweQ8OMMAtqyqivDjOvOszcSIh5Occhv0ZLiYiIiIgMRsGTS8GTFFNTV4w/v7KHu17cyZotrfttv7Sukg+dsID3HFs/8Ysj73wBfnY+pHqzdeW1sPovEKmf2L7I9JHogTfugw23w6b7IZ3Y/zXG46wFtehUWHSaszh5oHzEj+yJp3h1Vycv7+jg5R3tvL6ri7eau0kVIo0aoKY8wNxIiFkVQWZXBnP2AWZXBpldEWRmRZBI2F+c9dhERERERCYpBU8uBU9SaI3tvdy3YTf3bdjF2q1t+12PprrMz0VHzeXS4xomdnRTrq498JMz86dHeQPw8Xtg/qqJ7w/AvSvyy+evG9NtVtyQf591q8d2HymAWAe8fg9suA3efhQyw08z7efxQ8NKOOhkZ1+/AipqR/XoRCrDlr09vL67izd2d7FxTxdv7OliW2t0zGtGjVZVyMeM8gDVYT+RsgAzyvzOcdhPedBHedBHhbsvD3r7j/v2ZX4vHoVXIiIiIjJFKHhyKXiSQtjS0sO9btj00o6O/bb3ew3vXFrLXx3XwJlLagn4SriOTCoON10M25/Nr3/PD+DYD5emTwA3D/gG+/KxfS6Za/LvY68+cD7fJrVYJ7z1EGy8Dzb9ef9vxhsoMh/mHeuEUPXHQe0RUD5r1N2IJlJsbulh696ou+9hS0uULXt7aOqKj/p+xWQMlPm9VIRyAqmAE1KF/F7KAl7Cfi8hdx9260J+L+GcunAgvxwKeCnze/FpPSsRERERKaDhgqf9r+4qcoCLJdM8t6WVRzc28+gbzWwa4SvfVxw0g0uOreeio+ZSXRYoci9HwFq45+/2DZ1O/ExpQyeZ/kJVcOQlzpZOwY41sPFPsOlBaH5t/9d3bHe21/6YrSubCbOWwOwlMHspzD7MeYteVf2Qb2QsC/g4cl6EI+dF9jnXE0+xZW8PO9p62dXey66OGI3ufld7L7s7YwVZS2qkrIWeRJqeRBoofCjm9xonpMoNqgYJqPrq+gKt/nBriGArHMgGY1qsXURERERAwZPIoDa39PDoxiYefaOZp9/eSyw5sjdpLa+PcN6yOi5cPpeFs0a+Ts2EePZ6eOHX+XUHnwnnfL00/ZEDk9cHB53kbO/6BnQ3wZbHYfNjztb69sjuE90L255ytlzG44RP1QuyW1U9VMxxpuxV1kH57H3CqfLg0KEUQCqdoakrzp7OGM1dcVq6E7R0x93j7H5vT4KuWGos/2cmVDJtSaZTRe2rz2PyRmXlhVYDw668QMtDWcCXN5orHPAQ9vsGhGMeAl5PaaYsi4iIiMiIKXgSAXa0RVmzuZXntrTy1Ft72bp3BG/mcq04aAbnL6vj3CPrmF9TVsRejlE6CQ9/HZ783/z6GYvgfT8b0WvtRYqmohaWXepsAO3bYcsT0LgWGp+H3etHvj4UgM1kR0htfXLodmUzoaLOeX7FHKic44ZTbkBVXuvswzPAGHxeT/8b7vYnlc7QGUvRFk3QHk3S0ZugrSdJe2+Szt4kPfEUPYkU3fE0PfEU3fHUPvuRht2TWSpj6Yqn6IoXL9zy9oVb/eGUl3DAWSOrLJAdpVUWcEKr8oB7PpCdrth3rr+ur73fq0XiRURERApA33HKAcday1vN3azZ3MaazXt5bksbje29+7/Q5fUYVi2s4fzlTtg0pypUxN6OU8cOuPWT+06vC1TCB38HZTWl6ZfIUKrnwzEfdDZw1iXbvQF2Pg+N62DPBmjZBKnY+J4T3etsTa8M387jc0ZIlc92A6nc41qomO3sy2c76055nPWTasoD1JSPfYptKp1xptrlhVJpehIpYsk0vYk0vUlniyXSRHPLuedz6nsTGWLJNNFEakKnDRZTOmPpdv//FEPQ58kLrspyRm/l1QW8lPl9hAMeQn4vIZ+XoN899nsJ+TwE/V5Cfg8hn1vnng/6NGpLREREpjcFTzKtWWvZ3Rlj/Y4ONjR2sL6xg5d2dNDaM4LXvOeYGwlx+mGzOf2w2Zx86CyqQoOvITOpbLwP7vz0vgs5Gy9ceiPULi1Nv0RGwxeEhhXOxpVOXSYN7dugeSO0bHT3bzijpbp3F/b5mRR07XK2/TLuSKqBAdXAoMrdfMEh7+TzeoiEPUTChf+ssdaSSGeIJTL9oVS0P9DK5ARVKTe4yvQHWtFEqj/A6gu2om74lReEJdOkp0G6FU9liKcytEVHMepuDII+T14Y5YRTHoKDBFh57dzgqv983zU+j7P5hzj2efF7jQIvERERmRAKnmTaSGcsO9qibNzdxYbGDl5udMKmlu7RhUzgLLy7alENpx82mzOW1HJobcXU+QI9nYSH/g2e+u6+5yrnwft+6qyvIzJVebxQs8jZlpyXfy7Z64z0a9/qhFPt26BrN3TvcdaT6trtjHSiGKGIhWiLs42Ev9xZeD1YCcGqAccRZx+sdOvdY38Z+MM5m1v2hZxX4Y2AMcYNJ7xEKF6Inkhl9hmBFU2k9xmxlTs6K5bMtusdEGgNHMUVS6ZJpqd+uAXZgKtj5INvx80Y+kMoJ5gaEFq5odfQbfZ3XsGXiIiIOBQ8yZSTTGfYureHTXu6ebOpm03u9nZzN/HU2NZFCfg8HNNQzapFNRy/qIaVB82gPDjF/nqkU/DGffD4tc60pIEWnw2XXD+m19CLTBn+MMw61NmGkk5CT4sbRuVubjDV3QQ9TdDdDImu4vU12eNsIxpNNQK+8IBQyg2mfKFBAqsxnvOFR7wuXMDnIeDLGbVlrfP/Ph2HVALSaUgl3eN4zj4O6YSzT8WdunTSCRyNN2+fwkPCBImbML3u1k2YqA3SlQnQm7JE+6YiJlI5x86orH3rnFFefXXTmbUQS2ZKtp5YqYKvvlFiPk8Bgq9MGmIdzsji3naItTujJG1m383jc4PlSme6e7ASghVOAO3RGyCttWQspDIZ0hlLKmPJuPu+cjptSVtLOpMhlbGk0paMzWmTV86QSjv1zjUWW+Sc2hjwGIPXY/r3Xo9TN+Z6j8FrDB4P+DwefF6D390X5M+wiMgEmVLfWRtjPMAXgKuAhUAz8Hvga9banhJ2TQrIWsvengTbW6Nsb+tle2uUHW1Rtrf2sr0tSmNbL6lxTuGoCPpYcdAMVi2qYdWiGpbXRwj5vQX6FUyw7iZYdxOs+zl0Nu573njhrK/CSV/QF7ci4LzRrmqus+1Pshd6mp0QqqcpG0r1tLjHzdm6gdNaJ1qq19mKPWrGG8gPubyBQb7RtmDT+SFSOl7wrvjcrQyYMfCk8UC4xpn+WObuwzOc/cy++plumzlOm1B1/+ektZZYMkPUDaf6RmJF425YNUhwFUumiaXSbqDj7OOpdP/xwPPxZIZEeuovJD8WpQ6+PIZhw6sZnihzaaHOtlBrm5iVbqIm1UR1somqZAvhdCeh9Pi/9MwYL4lgDbFQLbHQbOL9Wy2xsjpiZXOJl88j5a8CLOkMZKzNbm7ZWkj310MmM8SxtW55wH2sM3I83R/0ZHKOBwt/sgHPPu0GBkKZDJlMfqg08JrBpuZ6SVNOjDBxyk2MMmIESRIwKXyk8ZNytzReBg+KLYYUXpL4SOIjjp+kdcq9BIkSJGpD9BAijh+YWkGOz+MEVH5vXxjlwe81/QGV1+O8HMPvBlXZY49bdutyznk9uffJPefUBXzOG0X9Xg9+n4eA17jnnC3gMwS8Xvw+pz6Qc63fvTbg9eDRyxtEDihTKngCrgM+D9wBXAsc7paPNcacba09ML96myKstXTGUrT2JGjqjLGnK+7sO2Ps6YzT1BWjqTPO7s5YQX/SHPJ7OGJuFcvrIyyrj7C8IcLi2RX4vFM4hEnGYMcaWPcLePWPQ7/1q3Ke8+a6g94xod0TmTb8Yahe4Gz70zeSqm+0VH9Q1ZwTULn7aIsT0ExF6YSzxTtK3ZPh2czopj6CE1aFqqFsJqashnDZTMJlNcwcGGCVu/tQxAkyvX7w9O1zfohhrdOPTMrd0tm9dY7TqSSJZJJ4IkkyGSeRcMqJRIJkMkkilSSZssTTlnjamcLYd9yb9hLNeOnJ+IimfHRnvPSkfHSnfcTTzvS9uBt+9U3niyed4/H+AGcq85Km1rZTn2pmXnov9WYv9aaZeWavu7VQZSZm3qPHpgnFmgnFmodt12nD7LSz2GlnutssGt39TjuTPcwgNQm/rA+QJEIPEdNNNd1ETA/V9FBtuqkyPVTTTbW3h4jXqYu4bcqJEzTFXVttoLQ19BCihzDttpx2W0k75bTZCjqooM1W0E4FHTZ73HcuUcRpy8NJueHdWEf8l5ITmA0Ip3ym/zjgywms3DbZOifgygu83FDMP0QoFvB5CBhLyJMkQJqgxwktg8YJMAOeDD6vD7/fh9/nw+/14fF6nB/g+kLZH7Z4pugPqkVKbPL9CzUEY8yRwOeA2621l+bUbwa+C1wG3Fyi7h0Qchek7Yon6Yo5bxLqijnHnbEU3bEUnbEk7VHn9eGt0QRtPQnaognaosmiLzY7o8zPobWVHD63kmX1EY5qqOaQ2eVTO2RKp5wFlBvXOa+Xb1wHTa8637wMZ8kF8O7vQ/nMiemnyIFuNCOpMhlIdEO8E+JdEOt0jmMdTjnemVPXma1LxZxRWMmoE0Ano065CKOJDkg2A72tzrZ3rDcxzrQqm3HCpf3wAmF3KxyTv25YVVV2rTC3Lh2oJO2vJOmrJOErJ+6rIO6toNdTTq8po9dTRizjcYOrnPBqiCCrv80I2xcn+LJU0EutaWc2HdSaNufYtDPHtFFvWphn9lJHKz4ztb5RrzK9VJntLGX7oOfT1tBMNS02wl5bRQsRWmwVe20Ve22EbsJ0E+of3RMlRI8NksRHBg8WZ3RQ32awBEk6m0kQJEmIJCHiVJkoEfqCoh4ixtmq+o5z9mEz+nU2S8VrLFX0UkUvc03rqK7tsUHaqKTVVtJmK2mjgjbrlnHqWvv2tpL2EoZVk0XfSLeRjXp0/jxWEaXC9FJJlEoTpYJeqtx9gF6C7nGliVKZs++7psKM8424QAIfcYLETZC4J0Svp5y4p5yYp5y4r5y4t4Kkz9lS/kpS/gr387YCglVkAhXuuo0VBHy+vBFkfcFZX51TNgPKzl5TLWWqmTLBE/BBnPGv/zOg/ifAfwAfZpoHTx29SRrbeklnLEl3uHIynZ3Dnky7de7Q52S6b8hz9jjpznmPu0P9+xZojeccO4u7Ol8cOtMCMs4UglS66PPjR2pOVZBDaytZXFvB4toKDnX3MyuGfkvUpNX0OrRtya4xM3Ddma7dI391vL8Mlr8fjv8UzD26qN0WkXHweJwAIFRVmPtl0jmhVG9OOOVOv0sO3Aae6wuyRnDdeBZm7/vJsS8A3mDOPuhO4Ru4DznH/UFOJme0kDt6KNnrhnjdzr7vODWBK3XnsUOPQp3IPsQ7hh2V5nW3AFA+VCNfOH9x+/4F8CPuYvcBCOX+Pg7xe+sLgrfM+X10pa0lmXa+dkmk0iSTSVLJuLtPkHLLmWQvNtED8Sg2GcUko5hUFF+ii0Cyg0Cyi1Cqg1C6i7J0J347sf/vO2wZHbacDsrpsOUk8JPBYPGQwbjHBj8pKohRYaKUE6PS9FJOjDJTmNDYayx1tFFnSjzl9wBVbuKUE6fBjHyEZRIfUVNG1ISJ4ux7CBPFWauuhzA9hOi2TmjYnQkStz5iGQ+xjJeY9ZGy2WmECXwk8ZK0PrfOSwZ36jCA+2exr2yHKTPIeedumby91/2T7iWD1+Sf95MmSMIJLE2i/zgbZCYIkSBossflJpYTGGVDpAp6CZjJse5egBQBUlTaHkjDELM89ytjDd2E6KKMbhvu37cQpiun3EOYhPv7m7B+9/fZRwI/1uvHegJkPH4y3gDWOHuPx4vHY/Ab8LrTJ/3uWmI+r7temKF/XTGfx5mKGfNXkwxEcqZwGry50zI9Tjk7JdOZhumsQ2bwuOucGffeznplzktN+o49g7TNrmu2b9uR3MuQfb+Kcf/M5mZyZsDBcG36wrzcSG/gvft3OY2s85fImfqMM2DDuvUWi/sfdmAbm72+tio4dZd+GYGpFDwdD2SANbmV1tqYMeZF9/y09peNTXzhdy+WuhsTojzgZX5NGQ0zyphfE2b+jDLm15SxoKaMhhnhqbfw93D+/M/w1sPju8fMxXD8FXD0ByFcXZh+icjU4fFCoNzZislaZ5pdXziV7HWmGBqPu5n844EBxEROUUjFIdrqvMWw191HW4eo2+us0RXvnLj+TQWpXujudX4IUmB9wVeo4HcujLQ3SLxsHtHwXHpCdXQF6+gKzaUrOIdO/xyivgi93nJS1uP+ADBD2mbXQUplctY5css255sNcL+ZT8epTLdSldxLZWov1am9VKX2UpVqYUaqmZrUHmpSTRMeqE0KxuMsvh4oh0CZs/eFnM8Tr88Jp70BZ7Sp8Q7+Zs++qa7pZHbNub6XHCRjkOhxA+ueCR856idFxHYSsaP43DFk//LIlObJGWE37qXFLNA3EWIcHxXfSn6QX6YvHmdnZKxu/8xJHLdgnxUrp42p9N37PKDFWjvYvwqNwEnGmIC1duqM6R0l3zRYGLo84KW6LEBtVZA5lSHmVAWprQpRWxlkTlWIOe5xdZn/wBk+WjFnbNdVHwTzT4BjLoeDzxjxq9RFRMbMmOwIlvAk/+LIFxz51Mc+qYQTQA0VTOWVW51pkJmkMyU6k3S+oR04Isx4nJE+xuvsPe6b+Ty+nPoBdXnn3X/7+xdtzzjPsJnsN9OpuDPiLRV3wqL0tP1SqHDKZkGkAarnQ2S+cxxpcI/n4y2fRZkxlAElfxestc76cR3boWNHzpZT7mkqdS8HZ7zOD8TCM7JbKLdcPUh9tTOizhea2K9t0kkngIp3un/fW903FrZm31w4sK6vPIJptTI5JAiQNP7+kWJ9i8+n8LhDUpwRW8Y6k1A9JkOQJGHihEngMZNk+odMO5NlZlGxTKXgqQwY6kcRsZw2eV9tGWNWA6sBFiwYweKwk5jPW/pgwe81hHxeKkI+KoI+KkM+KkN+KkI+qvqOgz5mlPmZUR6gpizAjPIAM8oCVJf5p/XwwTGrqN1/m/LZUL8C5h0H9cc5++m0dtPlhfmktVdP809sESkuXwAq5zjbWPVNAewLlErxQ4F0coi1wvrWFOsYeh2x3DXHxjOtslS8QecHOpVz3H0dVNQ55UgDRBZApN5ZJHiqMAYqZjtb/XGDt0nGoHs39Ox137zpvtSgp8XZ547sibv7RJc7ddVmp7L2hZoY5/+Rr28KZSg79TUUccKhUPUg+xn5dYGKqfODMa/fDcKqR/ZCiT7WOn9fcoPpfY7dcm6grbBqdDz+AVN/I4NMA97/uYDHS2AEj7PuiEVnOnCGnnSGtmSaZDJOKtZDOh4lFevCxjqxvc76jMb9DPUkOvEmuvAku/EluvCluvEnu/GnuwmkugmmewhmSjUdXCavKfhv7ihMpeApCgz1HXoop00ea+0NwA0AK1eunNK/m5Gwn6V1lf2vRu2bj+v3enLm4eaey87J7WvTNz835PMS8nsJ+T0E/V7C/mw59zjUf+wl5PNM7UW6J6s5y+CQdzpfGFfUOl8oV9S6XyzPcUKnUGTqfOEmInIg6xu9VEpev/sGvpqx3yOTgWRPfhCVG1IlurNTl/pGXaXjQ9QlnH0m9xvtAV+SefzOCC+vL+fYn32TlL8sZ3PXnhps5Iy/7MD899IfghkLnU0mljHO12mhCNQcPLJrrHVGKca7slui2z3udkLBvuN4l1Pum9qcTmbfLtp3nMmtzznft/CMhf6/c/11fX8HbbZuuPMeb06g7nXWKswre90Rnt7sCE9/TmjpD+cch5w15HzB/PpA+SDhkhsa+Sd2Yq4x2bfuleUlVeXAOD5b+2TSA8L+AT8s6DuX6HE/S90poukEpBLYdIJMyvl8tX2ftxlnFKzNGTrjrNVlAZNdT4jcdb/o/yNy9iGHsqBhmbt2sDOFOJWx/397dx4tS1Xdcfz7A2VSAwgaQZbiEIUoRiOIZhkjii7jyHKOwSnOiAqIBicUnBIVHEBBFEUmwaggGOcgSogEcEIN4gAPVGZnJoH3dv44deF66fe4vOp6t2/f72etWn27qs7pXf32qu63+9Qpru8uF17ezRV8/Yo58wsvL4q6Yf6i5Stu/LstrZC3fEX7e0XN2b7ipvuuWElfN7xG125m33YY3WXMsz5ebsji+vM1o/epP3s+e7+bbJvTfmaeqXTzUrXns+efmpnPqs0VNXsfun3WWXu6B2hkdmJOsiRfBnYENph7uV2SU4F7VdUdVtXHtttuW2eeeeaAUUqSJEmSJC0tSb5dVduO2raYhq+cQYv3QbNXJlkPuD9gRUmSJEmSJGmCLKbC07G0kWy7zVn/ItrcTket8YgkSZIkSZK0Uotmjqeq+kGSDwK7Jvks8AVga+CVwDeAoxcyPkmSJEmSJP25RVN46uwGLKPdpe5xwOXAAcDeVbViAeOSJEmSJEnSHIuq8FRVy4H9ukWSJEmSJEkTbDHN8SRJkiRJkqRFxMKTJEmSJEmSBmHhSZIkSZIkSYOw8CRJkiRJkqRBWHiSJEmSJEnSICw8SZIkSZIkaRAWniRJkiRJkjQIC0+SJEmSJEkahIUnSZIkSZIkDcLCkyRJkiRJkgZh4UmSJEmSJEmDsPAkSZIkSZKkQaSqFjqGNSbJZcD5Cx3HBNoUuHyhg9CiZg5pHMwj9WUOqS9zSH2ZQxoH80h9LUQO3bWq7jBqw5IqPGm0JGdW1bYLHYcWL3NI42AeqS9zSH2ZQ+rLHNI4mEfqa9JyyEvtJEmSJEmSNAgLT5IkSZIkSRqEhScBHLLQAWjRM4c0DuaR+jKH1Jc5pL7MIY2DeaS+JiqHnONJkiRJkiRJg3DEkyRJkiRJkgZh4UmSJEmSJEmDsPC0BCV5TpLvJrk6ySVJPprkDreg/UuSHJXkx0mWJ/F6zSmTZK0ku3f/xtck+UWS/ZLcZk2013QYQx69Lsl/JDk3SSVZNnDImjB9cijJvZLsm+S0JJcl+WOS7yV5g+eipaNnDt27+75zdpLfJ7mq62f/JJutifi18Mb5nSbJBknO6z7TDhwiXk2eMXwfqpUsVwwduybHOM5FSW6f5D1Jftb1cVmSryf5+yFjB7jV0C+gyZJkd2B/4BvAq4AtgD2AhyR5UFVdOY9uXgdsAnwXuE3Xh6bLe4FXAscB+wFbd88fkGTHqloxcHtNh7558A7gN8B3gI2GDFQTq08O/QvwcuAE4CjgOmAH4G3A05M8uKquHjJ4TYQ+ObQFsFnX9pfA9cA2wIuBZya5f1VdOmTwmgjj/E6zL7Dp+EPUhBtHDp3CTSeLvm6sUWrS9cqjJHcFTgZuCxwK/ATYELgfcOfhwu5UlcsSWWgfdFcCpwNrz1r/BKCA18+zny2Btbq/P9/SaOGPz2VseXIfYAXwmTnrX9HlybOGbO8yHcs48gC4+6y/fwgsW+jjcllzyxjORdsCG45Y/7au/a4LfYwuwy5DfR4BT+vav3ahj9Fl2GWcOQT8La14uUfX9sCFPj6X4ZcxfR8q4LCFPhaXhVvGlEenAL8ANluIY/BSu6VlJ2AD4ICqWj6zsqpOBM4Fdp5PJ1W1rByxMs3+CQjwvjnrPwJcxc3nSd/2mg6986Cqzh0gLi0evXKoqs6sqt+P2HRs93jf3hFq0g31eXR+97jxarbX4jGWHEqydtfmS8BnxxmgJt7YzkNJ1kly2zHGpsWjVx4leRjwUOBdVXVRklsn2WCQSFfCwtPSsl33+K0R204DtvJkJlqerKCNjLtBVV0DfI8b82io9poO5oH6GiqHZi4Pv2T1Q9MiMZYcSrJekk2TbJHk0cCHu01fGGewmkjjOg/tDmwF7DrW6LQYjCuHnkorMPwxyaVJDkiy4Vgj1STrm0eP7R4vSHIicDVwZZKfJFkjgwIsPC0tm3ePvxqx7Ve0KurmI7ZpadkcuLyq/jRi26+ATZOsM2B7TQfzQH2NPYe6UQd70y53Obp/iJpw48qhFwKX0S5R+DJtzrmdq+qUsUWqSdU7h5LcDdgH2Leqlo0/RE24cZyHTgfeQis+PRc4iVbEPMVBA0tG3zy6d/f4EeD2tDx6AXAtcESS548z2FGcXHwRSrIRsNstaPKBqvoN7TI7gFEJe033uEaH3GkibcDoHIE/z5NrB2qv6WAeqK8hcuh9wOD5/xkAAAtSSURBVINpcxqe0yM2LQ7jyqHjgR/TJmR9APBEYN53A9aiNo4cOgg4j3ZzHy09vXOoqrafs+rwJGcBb6fdLOrtfYPUxOubR7frHv8I7FBV1wIkOY425c47knxiyOl0LDwtThsBb74F+x9JuzPUVd3zdWnD62Zbr3u8Ci11VwF3XMm2+eRJ3/aaDuaB+hprDiV5K+0X4kOq6p09Y9PiMJYcqqpf0u5qB3B8ks8AZyRZ31yaer1yqLuE5dHAw6rKO5AtTUN9H3o37f+Dj8PC01LQN49m/u//yZmiE0BV/TbJCcBzaKOizu4b6Mp4qd0i1E3unVuw/KxremH3OOp2iXemzYh/4YhtWloupA3XXHfEtjvThnmu6pe9vu01HcwD9TW2HEryFuCNwMeBl44tQk26Qc5DVXUW8F1gl57xafKtdg51bfanzQV2cZJ7JrkncNdulw27dRsNEbgmxlDnoetm+u4ZnxaHvnk08+PJxSO2XdQ9DnrDDAtPS8sZ3eNDRmzbHjinqq5Yg/FoMp1BOzc8aPbKJOsB9wfOHLi9poN5oL7GkkNJ3kz7Vfhw4IXV3VNYS8KQ56H1afNkaLr1yaH1aZdkPg746azl5G77zt3zF441Yk2aQc5DXfst8EYZS0XfPJqZlHyLEdtm1l3aJ8CbY+FpafkcbZjdrt0EqwAkeQJwD+Co2TsnuUuSrZLces2GqQV2LG3029x5xF5Eu3b4hjxJco8kW61ue021vnkk9c6hJHvTJmQ9Anj+kHMXaCL1yqEkdxrVaZIdgPvS7gis6dYnh64EnjZimRkp96Xu+QmDRK5J0fc8tMlK+n0rbdqcE8cXqiZY3+9Ex9Pmd9p59oT0STYDdgJ+OusqqUHEH/6WliSvBt5D+7Xlk7Shea+m3allu9kjnpKcDPwDcLfZd+HoClV/0z3dmXY96Ju657+rqgMHPQgNLskBtLlQjqMNEd8aeCVwKvCImf+8JVkG3LWqsjrtNd3GkEfP5sZLEl4BrAPs1z0/v6qOGPoYtLD65FCSlwMHAhfQPqPmnncuqaqvDn0MWlg9c+g4YDPaHaTOp82j8UDgmbS5NB5eVd9bYwejBdH3s2xEf1vSJhv/YFXtOljgmhg9z0Pvpd0U4+u0z7PbAo8FdgD+lzZR9Ny5ezWFxvC9+sXAh4EfAR+jfa9+Ge1z7vFV9ZVB47fwtPQkeR6wO61g9Afg88BeVXXpnP1OZnTh6TDaLRhHOb+qthx3zFqzuhFxuwEvBrYELqdV2veeU5xcxugT27zaa7qNIY9Opp2DRvlGVT187EFrovTJoZv5rAJzaEnomUNPp+XQ/WiXTBWtAPVV4N1VdcGaOQotpL6fZSP62xILT0tKz/PQk2ij5O4LbAIsp12i+Slg/6qauaOZptw4zkVJngy8FtiG9oPct4B9qurUweO38CRJkiRJkqQhOMeTJEmSJEmSBmHhSZIkSZIkSYOw8CRJkiRJkqRBWHiSJEmSJEnSICw8SZIkSZIkaRAWniRJkiRJkjQIC0+SJEmSJEkahIUnSZKkNSTJlkkqyVsWOhZJkqQ1wcKTJEmaaEke3hVr9lzoWKZFkrd07+nMsiLJb5L8V5Injqn/ncYRqyRJWtxutdABSJIkLSHnA+sD1y90IJ29gfNo3wnvAbwE+FySnavqqB79vhn4BHB8/xAlSdJiZuFJkiRpNSS5XVX98Za0qaoCrhkopNXxxao6c+ZJkk8D3wP2AvoUniRJkgAvtZMkSVMkybpJXp/kR0muSfK7JCcmecCc/dZK8oYk30xycZJrk1yQ5KAkm8zZ94Z5mZI8I8m3k1wNHNBtP6zbvmHX/tLutU9Nsv3K+lpJ/49PckbX/qIk705ykx8Kkzwlyfe7/S5I8uYkO3b9PG9137+q+j5wOfBXI15zlyRfSfKr7v26KMmRSbaceyzd0+fOvpxvTl87dn39rjuGs5K8dHXjliRJk8sRT5IkaSokuTXwJeDvgCOAA4ENgRcBpyZ52KzRPesArwE+A3wOuBLYDngB8NAkD6yqa+e8xE7AK4GDgIOBP8zZ/mXgMmBfYBNgD+ALSbac58ioxwK7dH1/DHgSsCfwW+Ads47zGcAngZ8D+9Au23su8IR5vMYqJdkY2Bi4dMTmPYHTgA8AvwHuC7wQeESSbarq17Tjfzbt/T8FOGTEa7y4O8bTgLfT3vtHAQcluUdVvabvcUiSpMlh4UmSJE2LXYGHA4+pqi/PrEzyIeCHwHu67QB/AjarqqtntT84yf8AH6UVmT41p//7APerqrNX8vrfqapdZr3u/3V9PAv48Dzivw9wn6pa1rU/GPgB8Aq6wlM3+ml/WoHnQVX12279QcBZ83iNuTZMsik3zvH0NmBt4MgR+25TVVfOXpHkBOBrtILdu7rtRyY5Aji3qo6cs/9mtMLVMVX1rFmbPpTk/cAeSQ6uqp+vxrFIkqQJ5KV2kiRpWuwM/Bj4dpJNZxba6Kav0kYyrQ9trqWZolOStZNs1O17UtfX9iP6/89VFJ0A3jvn+UxfN7lsbSWOnyk6zcQIfB24U5LbdqsfCGwOHDZTdOr2vYI2iuiW+hqtiHUR8N/AQ4B/B14/d8eZolN3meJMwer7wO8Z/X6N8lRgXeDQ2f9GXV8n0r6bPnI1jkOSJE0oRzxJkqRpsTXtjnGXrWKfTYFfACR5OvBq4AHArefst/GItj+5mdc/d/aTqvp1EmiX3c3HuSPW/bp73AS4Arhb9/ycEfuOWndzXk47rg2AHWiXEm5cVTe5616SR9Dugrc9sN6czaPer1G27h6/top9/nKefUmSpEXAwpMkSZoWoV2atscq9rkMIMmTgWOB04FX0YpR19AuM/sSo0eFX7WqF6+q5auIaz5W1n52H/Pta75OnzXv1QlJLgHemeS7VXXDCKok2wFfAX5Gu+PdecDVQAHHMP9R9DPxP4c2ymqUUQU4SZK0SFl4kiRJ0+KnwB2Ak6pqxc3s+2xaoWmHqrqhoJRkqwHjG4fzusd7j9g2at0ttR9tvqa3JTm6qmYmUH8WrSj3j1U1EwNJbsP8RztB+zcCuLyqVjXqSZIkTQnneJIkSdPicOBOrGTEU5LZl3Atp43WWWvW9gBvHDLAMTiTNlLoed0d6ADo5oB6ad/Oq+o62kTmm9Auu5sxMxpr7oir1zP6++QVwO1HrP8UbWL3fWbm25qtmztq3VsatyRJmlyOeJIkSYvFI5PMnVsI2uiZg4H3A48C3t3NR3QS8AfgLrQJq6+hzWME8GngKcBJSQ6nzfG0E22uo4lVVdcn2RM4Cjg9yaHA9cDzaPNB3Y1WUOvjCNpcTnskOaCqfg8cB+wOfCHJIcC1tPf6fsDlI/o4Ddgxyb8CF7TQ65iq+mWSl9HuHHh2d/e782kj1bah/Rv8NbCs5zFIkqQJYeFJkiQtFo/plrnOAQ6uquuSPA7YhXYp3T7d9gtpczl9YqZBVR2T5Ha0Ysp7gN/S7qq2FzdO6D2RquroJNfTRmftA1wCHAqcBXyWNvdSn/6vT/JvtLvk7QbsU1WnJnkK8Cbgrd1rfA34B+CbI7rZBfgg8Abgdt26Y7r+P57kJ8CewEuAjWjFq3O6/i/uE78kSZosaXfqlSRJ0mKW5NW0ItpDquq0hY5HkiQJLDxJkiQtKknWAZbPvoteN8fTWcBfAJtX1bULFZ8kSdJsXmonSZK0uNwd+GKSY2h3udsMeC5tfqeXWXSSJEmTxMKTJEnS4nIZbfLufwbuSJtc/AfAXlX1qYUMTJIkaS4vtZMkSZIkSdIg1lroACRJkiRJkjSdLDxJkiRJkiRpEBaeJEmSJEmSNAgLT5IkSZIkSRqEhSdJkiRJkiQNwsKTJEmSJEmSBvH/+/ma70MZcwcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1440x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (20, 8))\n",
    "plt.rcParams['font.size'] = 18\n",
    "import seaborn as sns\n",
    "# Density plots of the learning rate distributions \n",
    "#sns.kdeplot(learning_rate_dist, label = 'Sampling Distribution', linewidth = 4)\n",
    "sns.kdeplot(random_params['learning_rate'], label = 'Random Search', linewidth = 4)\n",
    "sns.kdeplot(bayes_params['learning_rate'], label = 'Bayes Optimization', linewidth = 4)\n",
    "plt.vlines([best_random_params['learning_rate'], best_bayes_params['learning_rate']],\n",
    "           ymin = 0.0, ymax = 50.0, linestyles = '--', linewidth = 4, colors = ['orange', 'green'])\n",
    "plt.legend()\n",
    "plt.xlabel('Learning Rate'); plt.ylabel('Density'); plt.title('Learning Rate Distribution');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Evolution of Search\n",
    "An interesting series of plots to make is the evolution of the hyperparameters over the search. This can show us what values the Bayesian optimization tended to focus on. The average cross validation score continued to improve throughout Bayesian optimization, indicating that \"more promising\" values of the hyperparameters were being evaluated and maybe a longer search would prove useful (or there could be a plateau in the validation scores with a longer search)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1728x576 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(1, 5, figsize = (24, 8))\n",
    "i = 0\n",
    "\n",
    "# Plot of five hyperparameters\n",
    "for i, hyper in enumerate(['colsample_bytree', 'learning_rate', 'min_child_samples', 'num_leaves','feature_fraction']):\n",
    "    \n",
    "        # Scatterplot\n",
    "        sns.regplot('iteration', hyper, data = bayes_params, ax = axs[i])\n",
    "        axs[i].scatter(best_bayes_params['iteration'], best_bayes_params[hyper], marker = '*', s = 200, c = 'k')\n",
    "        axs[i].set(xlabel = 'Iteration', ylabel = '{}'.format(hyper), title = '{} over Search'.format(hyper));\n",
    "\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1728x576 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(1, 5, figsize = (24,8))\n",
    "i = 0\n",
    "\n",
    "# Scatterplot of next five hyperparameters\n",
    "for i, hyper in enumerate(['reg_alpha', 'reg_lambda', 'subsample_for_bin', 'subsample','bagging_fraction']):\n",
    "        sns.regplot('iteration', hyper, data = bayes_params, ax = axs[i])\n",
    "        axs[i].scatter(best_bayes_params['iteration'], best_bayes_params[hyper], marker = '*', s = 200, c = 'k')\n",
    "        axs[i].set(xlabel = 'Iteration', ylabel = '{}'.format(hyper), title = '{} over Search'.format(hyper));\n",
    "\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The final plot is just a bar chart of the boosting_type."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(1, 2, sharey = True, sharex = True)\n",
    "\n",
    "# Bar plots of boosting type\n",
    "random_params['boosting_type'].value_counts().plot.bar(ax = axs[0], figsize = (14, 6), color = 'orange', title = 'Random Search Boosting Type')\n",
    "bayes_params['boosting_type'].value_counts().plot.bar(ax = axs[1], figsize = (14, 6), color = 'green', title = 'Bayes Optimization Boosting Type');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Bayesian Optimization on the best hyperparameters"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Conclusions\n",
    "Bayesian optimization is one method for automated hyperparameter tuning. Automated hyperparameter tuning aims to find the best hyperparameter values for a machine learning model on a given dataset with no input from the data scientist beyond initial set-up required. Bayesian optimization uses Bayesian reasoning to build a probability model of the objecitve function  P(score|hyperparameters)  which is then used to select the next hyperparameter values to evaluate. The concept is to use more search iterations evaluating promising hyperparameter values by reasoning from the past results. This is an intuitive method of hyperparamter optimization that works in much the same way a human does to get better at any situation: learn from past experiences! If everything works as expected, Bayesian hyperparameter optimization can result in:\n",
    "\n",
    "1-Better generalization performance on the test set\n",
    "2-Fewer iterations than random or grid search require\n",
    "\n",
    "Even if Bayesian optimization (or other automated hyperparameter tuning methods) does not deliver on the above points in all situations, it is a useful skill to master as a data scientist. In the future, data scientists are not going to spend valuable time tweaking model hyperparameters, and knowing methods for accomplishing this automatically will go a long way in your career or studies!"
   ]
  }
 ],
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   "language": "python",
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    "name": "ipython",
    "version": 3
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   "nbconvert_exporter": "python",
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