Covid_Classification.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "f02342ee",
   "metadata": {},
   "source": [
    "# Exploratory Data Analysis (EDA)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "983e6aa2",
   "metadata": {},
   "source": [
    "All the X-ray images are stored in the 'COVID', 'Lung_Opacity', 'Viral Pneumonia', and 'Normal' directories, correspond to their X-ray images of their class. \n",
    "\n",
    "First we carry out the Exploratory Discovery Analysis (EDA) for our dataset. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "3b834e10",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# Lung types with their corresponding directories\n",
    "lungTypes = [\n",
    "    'COVID', \n",
    "    'Lung_Opacity', \n",
    "    'Viral Pneumonia',\n",
    "    'Normal'\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "8cbbb471",
   "metadata": {},
   "outputs": [],
   "source": [
    "import cv2\n",
    "\n",
    "# example image in each class\n",
    "img1 = 'Images/COVID/COVID-1.png'\n",
    "img2 = 'Images/Lung_Opacity/Lung_Opacity-1.png'\n",
    "img3 = 'Images/Viral Pneumonia/Viral Pneumonia-1.png'\n",
    "img4 = 'Images/Normal/Normal-1.png'\n",
    "sample_images_png = [img1, img2, img3, img4]\n",
    "\n",
    "# convert the images into numpy array, read in grayscale\n",
    "sample_images_read = [cv2.imread(img, 0) for img in sample_images_png]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "eb788798",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1080x720 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Readimg example lung X-ray image from Covid, Lung Opacity, Viral Penumonia, and Normal patient\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "# 2*2 subplots \n",
    "fig, ax = plt.subplots(2, 2, figsize=(15, 10)) \n",
    "\n",
    "n = 0\n",
    "for row in ax:\n",
    "    for img in row:\n",
    "        img.imshow(sample_images_read[n], 'gray') #show individual image\n",
    "        img.set_title(lungTypes[n]) # set title for image\n",
    "        n += 1"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "493abdcf",
   "metadata": {},
   "source": [
    "We can see there are similarities and differences between different lung types"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "6df611e7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(299, 299)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# The original pixel of the image is 299 * 299\n",
    "sample_images_read[0].shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "449e6990",
   "metadata": {},
   "source": [
    "# Machine Learning"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5ef4b5f4",
   "metadata": {},
   "source": [
    "## Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "938e18e7",
   "metadata": {},
   "outputs": [],
   "source": [
    "from pathlib import Path\n",
    "\n",
    "#empty lists\n",
    "labels = [] \n",
    "image_samples = []\n",
    "\n",
    "path = Path()\n",
    "for lungtype in lungTypes: \n",
    "    img_dir = path / 'Images' / lungtype # image directory\n",
    "    for img in img_dir.iterdir(): # read each image in the image directory\n",
    "        data = cv2.imread(str(img), 0).reshape(1, -1) # convert the 2D 299*299 to 1 dimension array of 89401\n",
    "        image_samples.append(data) # append the images to list\n",
    "        labels.append(lungtype) # append the class labels to list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "f31668d7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Final shape of samples: (21165, 89401)\n"
     ]
    }
   ],
   "source": [
    "# covert the list to numpy array\n",
    "image_samples = np.concatenate(image_samples)\n",
    "labels = np.array(labels)\n",
    "\n",
    "print(f\"Final shape of samples: {image_samples.shape}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "329eadf7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No of image samples: 21165\n"
     ]
    }
   ],
   "source": [
    "# there are total of 21165 samples of 299 * 299 pixels image in the samples\n",
    "print(f\"No of image samples: {len(image_samples)}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8248a1d1",
   "metadata": {},
   "source": [
    "## Binary classification"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b45913a7",
   "metadata": {},
   "source": [
    "As our objective is only to perform binary classification between \"COVID\" class and \"Others\" (Non-covid) class, the non-covid labels are preprocessed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "8e8d3431",
   "metadata": {},
   "outputs": [],
   "source": [
    "# use certain samples only\n",
    "np.random.seed(10)\n",
    "choices = np.random.randint(len(labels), size=8000)\n",
    "#choices = np.random.randint(len(labels), size=21165)\n",
    "\n",
    "\n",
    "\n",
    "X = image_samples[choices]\n",
    "y = labels[choices]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "3b291702",
   "metadata": {},
   "outputs": [],
   "source": [
    "def convert_binaryclass(x):\n",
    "    if x == 'COVID':\n",
    "        return 1\n",
    "    else:\n",
    "        return 0\n",
    "y_binary = np.array(list(map(convert_binaryclass, y)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "13b1f7a2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, ..., 0, 0, 0])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# example of y_binary\n",
    "y_binary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "460b1056",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Test traing split\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y_binary,\n",
    "                                                    test_size=0.2,\n",
    "                                                    random_state=10,\n",
    "                                                   stratify=y_binary)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "533b9687",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0.829063\n",
       "1    0.170937\n",
       "dtype: float64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# The y_labels distribution after the preprocessing\n",
    "import pandas as pd\n",
    "\n",
    "distribution = pd.DataFrame(y_train).value_counts(normalize=True)\n",
    "distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "4b736391",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "from sklearn.decomposition import PCA\n",
    "\n",
    "pca_700 = PCA(n_components=700)\n",
    "pca_700.fit(X)\n",
    "\n",
    "plt.grid()\n",
    "plt.plot(np.cumsum(pca_700.explained_variance_ratio_ * 100))\n",
    "plt.xlabel('Number of components')\n",
    "plt.ylabel('Explained variance')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "c39f9d67",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "61"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "explained = np.cumsum(pca_700.explained_variance_ratio_ * 100)\n",
    "np.min(np.where(explained > 90))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "485573ad",
   "metadata": {},
   "source": [
    "## Transformation Pipeline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "50536103",
   "metadata": {},
   "outputs": [],
   "source": [
    "# model pipeline \n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.decomposition import IncrementalPCA, TruncatedSVD\n",
    "from sklearn.pipeline import Pipeline\n",
    "\n",
    "pipeline = Pipeline([\n",
    "    ('scaler', StandardScaler()), #standard scaling\n",
    "    ('pca', IncrementalPCA(n_components=65, batch_size=100, copy=False)), # PCA Dimensionality reduction\n",
    "                    ])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "a987842b",
   "metadata": {},
   "outputs": [],
   "source": [
    "pipeline.fit(X_train)\n",
    "X_train = pipeline.transform(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "e85a7d44",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(6400, 65)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "59cc88e6",
   "metadata": {},
   "source": [
    "## Model Training"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "47939a8e",
   "metadata": {},
   "source": [
    "### Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "cda3bf38",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import accuracy_score, f1_score, precision_score, recall_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "f4abdc0e",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "forest = RandomForestClassifier()\n",
    "forest.fit(X_train, y_train)\n",
    "y_train_pred = forest.predict(X_train) #prediciton based on X_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "f980509a",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "def score_check(ytrue, ypred, name):\n",
    "    print(f\"============= {name} Set Score ==========\")\n",
    "    print(\"Accuracy:\", accuracy_score(ytrue, ypred))\n",
    "    print(\"Precision:\", precision_score(ytrue, ypred, pos_label=1))\n",
    "    print(\"Recall:\", recall_score(ytrue, ypred, pos_label=1))\n",
    "    print(\"F1 Score:\", f1_score(ytrue, ypred, pos_label=1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "1cb0dc8b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "============= Training Set Score ==========\n",
      "Accuracy: 1.0\n",
      "Precision: 1.0\n",
      "Recall: 1.0\n",
      "F1 Score: 1.0\n"
     ]
    }
   ],
   "source": [
    "score_check(y_train, y_train_pred, 'Training')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "6e7ddc71",
   "metadata": {},
   "outputs": [],
   "source": [
    "# for stratified cross validation\n",
    "from sklearn.model_selection import StratifiedKFold\n",
    "\n",
    "kf = StratifiedKFold(n_splits=3, shuffle=True, random_state=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "4ce80e67",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Collections of all the cross validation scores\n",
    "final_scores = {}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "af274c48",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "\n",
    "def cv_score_check(model, X, y, cv, model_name=None, scores_dict=None, use=False):\n",
    "    scores = {}\n",
    "    cross_val_accuracy = cross_val_score(model, X, y, cv=cv, scoring=\"accuracy\")\n",
    "    cross_val_precision = cross_val_score(model, X, y, cv=cv, scoring=\"precision\")\n",
    "    cross_val_recall = cross_val_score(model, X, y, cv=cv, scoring=\"recall\")\n",
    "    cross_val_f1 = cross_val_score(model, X, y, cv=cv, scoring=\"f1\")\n",
    "\n",
    "    print(\"Accuracy:\", cross_val_accuracy.mean())\n",
    "    print(\"Precision:\", cross_val_precision.mean())\n",
    "    print(\"Recall:\", cross_val_recall.mean())\n",
    "    print(\"F1 Score:\", cross_val_f1.mean())\n",
    "    \n",
    "    if use: #for recording the scores\n",
    "        scores['Accuracy'] = cross_val_accuracy.mean()\n",
    "        scores['Precision'] = cross_val_precision.mean()\n",
    "        scores['Recall'] = cross_val_recall.mean()\n",
    "        scores['F1_Score'] = cross_val_f1.mean()\n",
    "        scores_dict[model_name] = scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "46ff83cb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.877500701916727\n",
      "Precision: 0.9681387376487195\n",
      "Recall: 0.29707461488283404\n",
      "F1 Score: 0.4579612779130306\n"
     ]
    }
   ],
   "source": [
    "cv_score_check(forest, X_train, y_train, kf)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9eca379e",
   "metadata": {},
   "source": [
    "The model appears to be overfitting when using training set.\n",
    "When using cross validation set, scoring slightly reduced."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8208fbf9",
   "metadata": {},
   "source": [
    "#### Fine tuning hyperparameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "45c72ecf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'bootstrap': [True, False],\n",
      " 'max_depth': [10, 15, 21, 26, 32, 37, 43, 48, 54, 60, None],\n",
      " 'max_features': ['auto', 'sqrt'],\n",
      " 'min_samples_leaf': [1, 2, 4],\n",
      " 'min_samples_split': [2, 5, 10],\n",
      " 'n_estimators': [10, 20, 30, 40, 50, 60, 70, 80, 90, 100]}\n"
     ]
    }
   ],
   "source": [
    "# Define hyperparameters to be used in the randomized search cross validation \n",
    "\n",
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "from pprint import pprint\n",
    "\n",
    "# Number of trees in random forest\n",
    "n_estimators = [int(x) for x in np.linspace(start = 10, stop = 100, num = 10)]\n",
    "\n",
    "# Number of features to consider at every split\n",
    "max_features = ['auto', 'sqrt']\n",
    "\n",
    "# Maximum number of levels in tree\n",
    "max_depth = [int(x) for x in np.linspace(10, 60, num=10)]\n",
    "max_depth.append(None)\n",
    "\n",
    "# Minimum number of samples required to split a node\n",
    "min_samples_split = [2, 5, 10]\n",
    "\n",
    "# Minimum number of samples required at each leaf node\n",
    "min_samples_leaf = [1, 2, 4]\n",
    "\n",
    "# Method of selecting samples for training each tree\n",
    "bootstrap = [True, False]\n",
    "\n",
    "# Create the random grid\n",
    "random_grid = {'n_estimators': n_estimators,\n",
    "               'max_features': max_features,\n",
    "               'max_depth': max_depth,\n",
    "               'min_samples_split': min_samples_split,\n",
    "               'min_samples_leaf': min_samples_leaf,\n",
    "               'bootstrap': bootstrap}\n",
    "pprint(random_grid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "7f64a98f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 3 folds for each of 100 candidates, totalling 300 fits\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "RandomizedSearchCV(cv=StratifiedKFold(n_splits=3, random_state=10, shuffle=True),\n",
       "                   estimator=RandomForestClassifier(), n_iter=100, n_jobs=-1,\n",
       "                   param_distributions={'bootstrap': [True, False],\n",
       "                                        'max_depth': [10, 15, 21, 26, 32, 37,\n",
       "                                                      43, 48, 54, 60, None],\n",
       "                                        'max_features': ['auto', 'sqrt'],\n",
       "                                        'min_samples_leaf': [1, 2, 4],\n",
       "                                        'min_samples_split': [2, 5, 10],\n",
       "                                        'n_estimators': [10, 20, 30, 40, 50, 60,\n",
       "                                                         70, 80, 90, 100]},\n",
       "                   random_state=10, scoring='f1', verbose=2)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Random search of parameters, using 3 fold cross validation, \n",
    "# search across 100 different combinations, and use all available cores\n",
    "rf_random = RandomizedSearchCV(estimator=forest,\n",
    "                               param_distributions=random_grid,\n",
    "                               n_iter=100,\n",
    "                               cv=kf,\n",
    "                               verbose=2,\n",
    "                               random_state=10,\n",
    "                               n_jobs = -1,\n",
    "                               scoring='f1')\n",
    "# Fit the random search model\n",
    "rf_random.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "dc01034d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RandomForestClassifier(bootstrap=False, max_depth=37, min_samples_split=5,\n",
      "                       n_estimators=40)\n"
     ]
    }
   ],
   "source": [
    "# best model of the random forest model\n",
    "forest_best = rf_random.best_estimator_\n",
    "print(forest_best)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "ee671bfb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.8845319522599961\n",
      "Precision: 0.9300605814465089\n",
      "Recall: 0.3445983240503789\n",
      "F1 Score: 0.503013949013949\n"
     ]
    }
   ],
   "source": [
    "cv_score_check(forest_best, X_train, y_train, kf, model_name='Random Forest', \n",
    "               scores_dict=final_scores, use=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c4850a03",
   "metadata": {},
   "source": [
    "The fine-tuned model perform slightly better than the original model"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fb83c241",
   "metadata": {},
   "source": [
    "### Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "ed6b56a3",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/ckng/miniforge3/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "logistic = LogisticRegression()\n",
    "logistic.fit(X_train, y_train)\n",
    "\n",
    "y_train_pred = logistic.predict(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "5cc75bff",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "============= Training Set Score ==========\n",
      "Accuracy: 0.8521875\n",
      "Precision: 0.6594827586206896\n",
      "Recall: 0.2797074954296161\n",
      "F1 Score: 0.392811296534018\n"
     ]
    }
   ],
   "source": [
    "score_check(y_train, y_train_pred, 'Training')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "4835ce60",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/ckng/miniforge3/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/Users/ckng/miniforge3/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/Users/ckng/miniforge3/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/Users/ckng/miniforge3/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/Users/ckng/miniforge3/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/Users/ckng/miniforge3/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/Users/ckng/miniforge3/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/Users/ckng/miniforge3/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.8435939132359159\n",
      "Precision: 0.5962026345747277\n",
      "Recall: 0.26418284911435597\n",
      "F1 Score: 0.36558967414868127\n"
     ]
    }
   ],
   "source": [
    "# CV score before fine-tuning\n",
    "cv_score_check(logistic, X_train, y_train, kf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "45a25098",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/ckng/miniforge3/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/Users/ckng/miniforge3/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/Users/ckng/miniforge3/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/Users/ckng/miniforge3/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/Users/ckng/miniforge3/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/Users/ckng/miniforge3/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/Users/ckng/miniforge3/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/Users/ckng/miniforge3/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/Users/ckng/miniforge3/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/Users/ckng/miniforge3/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/Users/ckng/miniforge3/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/Users/ckng/miniforge3/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n",
      "/Users/ckng/miniforge3/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best: 0.370775 using {'C': 0.01, 'penalty': 'l2', 'solver': 'liblinear'}\n",
      "0.366596 (0.019842) with: {'C': 100, 'penalty': 'l2', 'solver': 'newton-cg'}\n",
      "0.366596 (0.019842) with: {'C': 100, 'penalty': 'l2', 'solver': 'lbfgs'}\n",
      "0.366596 (0.019842) with: {'C': 100, 'penalty': 'l2', 'solver': 'liblinear'}\n",
      "0.366596 (0.019842) with: {'C': 10, 'penalty': 'l2', 'solver': 'newton-cg'}\n",
      "0.365590 (0.019246) with: {'C': 10, 'penalty': 'l2', 'solver': 'lbfgs'}\n",
      "0.366596 (0.019842) with: {'C': 10, 'penalty': 'l2', 'solver': 'liblinear'}\n",
      "0.366596 (0.019842) with: {'C': 1.0, 'penalty': 'l2', 'solver': 'newton-cg'}\n",
      "0.365590 (0.019246) with: {'C': 1.0, 'penalty': 'l2', 'solver': 'lbfgs'}\n",
      "0.366596 (0.019842) with: {'C': 1.0, 'penalty': 'l2', 'solver': 'liblinear'}\n",
      "0.366596 (0.019842) with: {'C': 0.1, 'penalty': 'l2', 'solver': 'newton-cg'}\n",
      "0.367460 (0.018622) with: {'C': 0.1, 'penalty': 'l2', 'solver': 'lbfgs'}\n",
      "0.365465 (0.020488) with: {'C': 0.1, 'penalty': 'l2', 'solver': 'liblinear'}\n",
      "0.365590 (0.019246) with: {'C': 0.01, 'penalty': 'l2', 'solver': 'newton-cg'}\n",
      "0.365590 (0.019246) with: {'C': 0.01, 'penalty': 'l2', 'solver': 'lbfgs'}\n",
      "0.370775 (0.018611) with: {'C': 0.01, 'penalty': 'l2', 'solver': 'liblinear'}\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import RepeatedStratifiedKFold\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "\n",
    "solvers = ['newton-cg', 'lbfgs', 'liblinear']\n",
    "penalty = ['l2']\n",
    "c_values = [100, 10, 1.0, 0.1, 0.01]\n",
    "\n",
    "# define grid search\n",
    "grid = dict(solver=solvers,penalty=penalty,C=c_values)\n",
    "grid_search = GridSearchCV(estimator=logistic, \n",
    "                           param_grid=grid,\n",
    "                           n_jobs=-1,\n",
    "                           cv=kf,\n",
    "                           scoring='f1',\n",
    "                           error_score=0)\n",
    "\n",
    "logistic_grid = grid_search.fit(X_train, y_train)\n",
    "\n",
    "# summarize results\n",
    "print(\"Best: %f using %s\" % (logistic_grid.best_score_, logistic_grid.best_params_))\n",
    "means = logistic_grid.cv_results_['mean_test_score']\n",
    "stds = logistic_grid.cv_results_['std_test_score']\n",
    "params = logistic_grid.cv_results_['params']\n",
    "for mean, stdev, param in zip(means, stds, params):\n",
    "    print(\"%f (%f) with: %r\" % (mean, stdev, param))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "cdc02ee7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LogisticRegression(C=0.01, solver='liblinear')\n"
     ]
    }
   ],
   "source": [
    "logistic_best = logistic_grid.best_estimator_\n",
    "print(logistic_best)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "d06d47ac",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.8401563154270971\n",
      "Precision: 0.5672353927494563\n",
      "Recall: 0.27606503085955136\n",
      "F1 Score: 0.3707748191259222\n"
     ]
    }
   ],
   "source": [
    "# CV score after fine-tuning\n",
    "cv_score_check(logistic_best, X_train, y_train, kf, model_name='Logistic Regression', \n",
    "               scores_dict=final_scores, use=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0cef7837",
   "metadata": {},
   "source": [
    "### SVM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "a6ce1f4e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "============= Training Set Score ==========\n",
      "Accuracy: 0.9415625\n",
      "Precision: 0.9627249357326478\n",
      "Recall: 0.6846435100548446\n",
      "F1 Score: 0.8002136752136751\n"
     ]
    }
   ],
   "source": [
    "# SVM\n",
    "from sklearn.svm import SVC\n",
    "\n",
    "svm = SVC()\n",
    "svm.fit(X_train, y_train)\n",
    "\n",
    "y_train_pred = svm.predict(X_train)\n",
    "score_check(y_train, y_train_pred, 'Training')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "3c879375",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.9075001175353518\n",
      "Precision: 0.9032134813384812\n",
      "Recall: 0.5146193988659742\n",
      "F1 Score: 0.655117352689488\n"
     ]
    }
   ],
   "source": [
    "# CV score before fine-tuning\n",
    "cv_score_check(svm, X_train, y_train, kf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "f426ea95",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=StratifiedKFold(n_splits=3, random_state=10, shuffle=True),\n",
       "             estimator=SVC(probability=True),\n",
       "             param_grid={'C': [0.1, 1, 10, 100],\n",
       "                         'gamma': [0.0001, 0.001, 'scale'], 'kernel': ['rbf']},\n",
       "             scoring='f1')"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# # SVM\n",
    "from sklearn.svm import SVC\n",
    "\n",
    "param_grid = {'C':[0.1,1, 10, 100],'gamma':[0.0001,0.001,'scale'],'kernel':['rbf']}\n",
    "svc = SVC(probability=True)\n",
    "svm_grid = GridSearchCV(svc, param_grid, scoring='f1', cv=kf)\n",
    "svm_grid.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "1911f2cf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SVC(C=10, probability=True)\n"
     ]
    }
   ],
   "source": [
    "svm_best = svm_grid.best_estimator_\n",
    "print(svm_best)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "a9cf9004",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.9326556852765039\n",
      "Precision: 0.8480628729821061\n",
      "Recall: 0.7403908876511616\n",
      "F1 Score: 0.7899470840459172\n"
     ]
    }
   ],
   "source": [
    "# CV score after fine-tuning\n",
    "cv_score_check(svm_best, X_train, y_train, kf, model_name='SVM', \n",
    "               scores_dict=final_scores, use=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dac70a8a",
   "metadata": {},
   "source": [
    "## Cross Validation Results Compare"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "c1cdac03",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Random Forest</th>\n",
       "      <th>Logistic Regression</th>\n",
       "      <th>SVM</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Accuracy</th>\n",
       "      <td>0.884532</td>\n",
       "      <td>0.840156</td>\n",
       "      <td>0.932656</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Precision</th>\n",
       "      <td>0.930061</td>\n",
       "      <td>0.567235</td>\n",
       "      <td>0.848063</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Recall</th>\n",
       "      <td>0.344598</td>\n",
       "      <td>0.276065</td>\n",
       "      <td>0.740391</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F1_Score</th>\n",
       "      <td>0.503014</td>\n",
       "      <td>0.370775</td>\n",
       "      <td>0.789947</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Random Forest  Logistic Regression       SVM\n",
       "Accuracy        0.884532             0.840156  0.932656\n",
       "Precision       0.930061             0.567235  0.848063\n",
       "Recall          0.344598             0.276065  0.740391\n",
       "F1_Score        0.503014             0.370775  0.789947"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result = pd.DataFrame(final_scores)\n",
    "result"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cdfd160c",
   "metadata": {},
   "source": [
    "We can see that SVM has the best accuracy, precision, recall, and F1 score in the cross validation set"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "28bcc3e5",
   "metadata": {},
   "source": [
    "# ROC Curve"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "66db900c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYIAAAEWCAYAAABrDZDcAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/MnkTPAAAACXBIWXMAAAsTAAALEwEAmpwYAABar0lEQVR4nO2dd3gU1feH35OekBAg9F6kQ2ghSBFBOtJBaUoRe8Mv/lBsFBtYsIAFFAWUpoIIUgUEBKRD6L2EEFpISEjfbPb+/phNAUMIsJtNsvd9nnl2zsyduZ/ZbObM3HKOKKXQaDQajfPi4mgBGo1Go3Es2hFoNBqNk6MdgUaj0Tg52hFoNBqNk6MdgUaj0Tg52hFoNBqNk6MdgUaj0Tg52hFoChQiclZEEkUkTkQuicgsEfG9qUwLEflbRGJFJEZE/hSROjeVKSwiX4jIOeu5Tlnt4reoV0TkZRE5KCLxInJeRH4Tkfr2vF6NxhZoR6ApiHRXSvkCDYFGwBtpO0SkOfAXsAQoC1QB9gFbRKSqtYwHsA6oC3QGCgPNgUgg+BZ1fgmMBF4GigE1gD+Ah+9UvIi43ekxGs29IHpmsaYgISJngSeVUmut9sdAXaXUw1Z7E3BAKfX8TcetBCKUUkNE5EngA6CaUiouB3VWB44CzZVSO25RZgMwRyk1w2oPs+psZbUV8CLwCuAGrALilVL/l+kcS4CNSqnPRKQsMBVoDcQBnyulptz+G9Jo/ot+I9AUWESkPNAFOGm1fYAWwG9ZFP8V6GBdbw+syokTsNIOOH8rJ3AH9AKaAXWA+UB/EREAESkKdAQWiIgL8CfGm0w5a/2viEine6xf46RoR6ApiPwhIrFAGHAFGGfdXgzjN38xi2MuAmnt/wG3KHMr7rT8rZiolIpSSiUCmwAFPGDd1w/YqpS6ADQFSiil3lVKmZRSp4HvgQE20KBxQrQj0BREeiml/IA2QC0ybvDXAAtQJotjygBXreuRtyhzK+60/K0IS1tRRpvtAmCgddMgYK51vRJQVkSi0xbgTaCUDTRonBDtCDQFFqXURmAW8KnVjge2Ao9kUfxRjA5igLVAJxEplMOq1gHlRSQomzLxgE8mu3RWkm+y5wP9RKQSRpPRIuv2MOCMUqpIpsVPKdU1h3o1mhvQjkBT0PkC6CAiDaz2GGCodainn4gUFZH3MUYFTbCW+RnjZrtIRGqJiIuIBIjImyLyn5utUuoE8A0wX0TaiIiHiHiJyAARGWMtFgL0EREfEbkPGHE74UqpvRhvKTOA1UqpaOuuHUCsiLwuIt4i4ioi9USk6R1/OxoN2hFoCjhKqQjgJ2Cs1d4MdAL6YLTrh2IMMW1lvaGjlErG6DA+CqwBrmPcfIsD229R1cvAV8DXQDRwCuiN0akL8DlgAi4Ds8lo5rkd86xa5mW6plSgG8bw2DNkOAv/HJ5To7kBPXxUo9FonBz9RqDRaDROjnYEGo1G4+RoR6DRaDROjnYEGo1G4+Tku+BWxYsXV5UrV3a0DI1Go8lX7N69+6pSqkRW+/KdI6hcuTK7du1ytAyNRqPJV4hI6K326aYhjUajcXK0I9BoNBonRzsCjUajcXK0I9BoNBonRzsCjUajcXLs5ghE5EcRuSIiB2+xX0RkioicFJH9ItLYXlo0Go1Gc2vs+UYwCyPx963oAlS3Lk8D39pRi0aj0Whugd0cgVLqHyAqmyI9gZ+UwTagiIjYIsvTHXP+PISFZdhhYca2zHZ4eIZ97hxcuJBhh4bCxUyJCs+ehUuXMuwzZ+Dy5Qz79Gm4ciXDPnUKIiIy7JMnb7RPnICr1txZShl2ZKRhWyyGHWX9plNTDfvaNcM2mw07OjrDXrUqo/yFC7BoUcb+sDDDvn4949oWLYK4uIxrWbQIEhIytC9aBElJGVoXLQKTybCPHTPs1FTDPnLEsNOC3h46ZNhpHDgAf/yRYe/bB0uXZth798KyZcZ6VBR88AHMmZOx/+uvYf78DHvKFPj11wz7889vrO+TT2DJkgx70qSM84Nx/pUrM+x334W//sqwx42DddZ0NqmpMHYsbNxo2CaTYW/ebNgJCYa9bZthx8Ya9s6dhh0dbdh79hh2ZKRh79tn2FeuGPZB6zv2hQuGffSoYYeFGfaJE4Z99qxhnz5t2CdPGva5c4Z97Jhhp/22Dx827LTf7oEDhp32WwwJMey0386uXYad9lvZvt2w4+MN+99/DTvtt7Fpk2GnpBj2hg2GbbEY9tq1MH58xne7ejW8916GvXw5TJyYYS9dCh9/nGEvXgyTJ2fYCxfCF19k2L/8AlOnglKKlFQLM2aamfyFmWvxJq7Fm/j2ezOfTzXsqHgTU79N4YuvU4iITeZKbBKTp6Qw5VsTF2MSCY9OZOJnJr6clkxYVAKhkfG8+1EyX36XxOmIOE5eieOdD5L48rtEjl+O5eil64yZkMQX3ydw6EIMB8NjGPV2Il/+EMvBE2fYf/w0r7xygemf7uD41mUc3bqMkc8cZfqn2zm0+U+2LPuJV5/9nYS4GOyCUspuC1AZOHiLfcswYsCn2euAoFuUfRrYBeyqWLGiuld+/VUpUCo52bDbt1eqRYuM/Q88oFTbthl2s2ZKdeqUYTdsqFSPHhl2nTpK9euXYd93n1KDBmXYFSooNXx4hl2qlFLPPJNhFymi1MsvZ9g+Pkr93/9l2G5uSr35prGemmponzDBsBMTDXviRMOOjjbszz4z7CtXDPurrwz7/HnDnj/fsBcvNuy9ew17wQLDPnzYsH/6ybBPnjTsGTMM+9w5w/76a8O+fNmwP//csK9dM+xJkww7IcGw333XsM1mw377baVcXDKudfRopby9M+yRI5Xy98+wn31WqZIljfXjx5V68kmlOnfO2B8YqFSfPhl2rVpKDRiQYVetqtSQIRl2uXJKPfVUhl2ihFIvvJBhFy6s1KhRGbaXl1JjxmTYLi5KjR1rrJtMSoko9f77hh0XZ9gff2zYkZGG/eWXhn3xomF/+61hh4Ya9g8/ZFyfiFJz5hj2wYOG/euvhr17t2EvWWLYW7ca9sqVhr1xo2GvW2fYf/1l2Js3G/ayZYa9Y4dhL1pk2Pv2Gfb8+YZ95Ihhz55t2KdOGfZ33xn2+fOG/eWUVCViUdsOxaqdZyLVM6OvKxGL+n7dOfXDptOq51NXlYhFTfjjiBq35KBq1f+iQizqmdm71PCZO1TdrmFKXFNV1y//UQ99ul6VbX1WuXikqOAP1qjgD9aoUs1Dlat3crpdMjhMufsmpdvFm5xXHkUS0u2ABheUZ0CcCv5gjWr6/hrlX/ei8ihxXVUZs0xVe2O58q1xSXmXiVENJqxWDSasVn73RSif8tfSbd8qV5VvpSjV+N2/VJP31ijfitdU4WpXVbMP1qr7P1yrCpWPVsVqXlGPfThTPT7xR1W0dKSqWPeMGjZplho+aZYqWyJcdaq/Rm1/t43a8V4bVbFoqBrcYIGKGF9ZRYyvrMr4XVBPNZ6p1LjCKmZ8OVXC54oaETxb7X+/ldr//gPK1zNWDWm5QH07uKoq6+ei/DxnqnOnDqu7BdilbnGvtms+AhGpDCxTStXLYt8yYJIyEoUgIuuA15VS2U4bDgoKUvc6s3jJEhgyBGbPhl69jCc6sxk6dTL2r1kDItC+vWGvWgUeHvDQQ4a9YgUUKgQPPmjYy5aBvz88YE0zvnQpBARAy5aGvXgxlC4NzZsb9qJFUL48NGtm2L/9BlWqQJA10eGCBVCjBjS29prMmwd16kDDhsaT9Lx5EBgI9esbT6ELFhj76tY1nkJ/+804tnZt42ls0SLj3DVrGk+lCxca2itVgpgY4wnxvvvA29t4Kg0Lg+rVwcvLeLM4f97Q4+lpPA2Ghxvn8vAwnlovXDDqcnMz3lwuXjT0uroaT5OXLhnaXFyMp9rLl6FePeM7vnTJKFO/vnGtFy8a56xn/cVcuGBoqFvXsMPDjSfQ2rXv6SegAVJSLVyLN3E1zkR0golks4Xw6EQSTcbrW2yymdMRcZyLSsBktmBKtZCcYiHZnEqS9dNsybh/CODv7U5RHw+K+LhTxMeDwl5ueHu44unmiqe7C16ZPr3cXfF0c8HL3RUvdxc83YzPzNvdXAVBbHK9nm4ueLq74OHqgpurC8RHgkqFQ39AavJ/D7CYIWwneBXO2HbpIJjiwM0LIo5kbC+RxQ8yKRqCn4LSgRnbfEtCoZuiPPiWAhfX/xweGhpKp06dOHbsGO7u7nzyySeMHDnyjq45MyKyWymVZTpVRzqC6cAGpdR8q30MaKOUunhz2czciyNQCt56C/r1M26kbvkuwIZGkz1R8SYOhMdwNTYZBSSYzFyNMxEVn0xknInIOBNXrevxyWaK+HhQ3Ne4cXu4uVLW3wtfT+Mfw8fDlaolfKkU4IOHm4txI027kVtv1u4uGa3LIiBim5u2TVEK4q/Crh8hJgyuHDGWlHjwKQ4JV6H5i1kfl5oM5ZpkbLOYIeA+8PA1HIR/xbQLt6lkk8mEn58fJpOJ4OBgVq5cSbFixe7pnNk5AkfeCpcCL4rIAozE3DG3cwL3ypUrxtN8RAR8/709a9Jo/ktUvIlkcyoJplTik83EJ6eSYDITn26bjX0m63qysZ5gSiUu2UyiKZXsnttik1KITTJTt1xhyvh7I4CPpyvFCnlSo5QfAVU9CfA1bvzFCnlSxNsdF5c8eOPOKUpB3BXj5nz5EIjVKSVfh9Mb4OxmSIoxbvRpNBgEdXtD2zegZF0o7JBuyVty4sQJqlWrhoeHB6+88gp169ZlyJAhdq/Xbm8EIjIfaIOR5/UyMA5wB1BKTRPj0eErjJFFCcDw2zULwb03Da1eDQ0aGE01Go09iYo3seNMJJtPXmXLyUiuxiXj4+FKIQ83fDxd8fFww9fT7YZthu1GofT9rum2l7srrtncuL3dXalQ1Cd/39xvhSUVos4YzTLKAuG7IWQeXNgDnv5gSYGK1rbXVJPR1FK2EdTuDn5l89wN/2YsFgsvvPAC06dP57HHHuOnn36yeR0OeSNQSg28zX4FvGCv+m9FWj+ARmMrUi2Ky9eTOBeVwLmoBPaei2b76UgiYpNpVKkore4LYNCgStQq7Vcwb9L2IO4KHFwEZ/4xHEBae3yp+sZN35xk3Og7vAtVHnCs1ntk+/btdO/enYiICHx9fenXr1+ua3CqVvJGjYzO0mPHHK1Ekx9JSbWw+cRV/tx3gVNX44mzNsVEJ6ZQ1MedisV8qFDMh9qlCzOkeSVqlPLL9gneabGkgikeDi8xnugPLjKacLz8jf0picZTPUDlB6D1/0Hx6lCmgeM024mXXnqJr776CoDevXuzYMECPDw8cl2HUzmCN980RtRoNNkRn2wmNNIYG37W+hkVb2JX6DWqFC9Ez4Zleax5JQp7ueHr6U4RH3e83P876kODcdNf+RpEh8H1cIiPgDjrpBrvolC9IwQ/DfX6gV+pjOPcvMHdyzGac5FSpUpRrFgxFi9eTOvWrR2mw66jhuzBvY4aApt38GvyKfHJZtYeuczKA5e4GpeM2aIIj07kemIKlQJ8qBRQiMrWz6I+HgSW96dCMR9Hy857HPkTNn1mPNVHnQIPv4x9pljjs8VLULYx+JU2lqJVnPIfMSkpib59+2IymVizZg1g9A+4uNg/7FteHTWUq1gsxvj5tm2hRJbJ2jQFlVSLIjIumYsxSRy7HMv6o1e4fD2JE5fjaFK5KN0Cy1IpwAcXgbJFvCnl56Xb8m8mIcoYF7/xY0i8ZozI8S4GKGNIZv1HIGgE+AQYN/rMuPuAq9Pcam7Jr7/+yvDhw0lISKBChQrpDiA3nMDtcJq/zoUL0L8/vPYafPSRo9Vo7InFovhlVxgzt5zhYnQSSeZU/L3dKe3vRbki3nSqW5ryRX2oWcoPfx93R8vNG5iTjQ7Y438ZN3yA8zvhWiiEWWNiFCoBCZHQ8X1oOAhKWacHeRYGX/10dSuuX7/Oww8/zObNmxERRo0axSeffJInHEAaTuMIAgKMGcOlSt2+rCZ/cjA8hm2nI5m3/Rw+nq5M7FOf+0r44e3hiodb3vmncxgpSXBxHxz+Ay4dMJ7Uz/wDPsWM9vs07msPRSoZban1+0Hr0VC5lVO02duDdevWsXnzZqpWrcqqVauoXr26oyX9B6fqI9Dkf5RSnI1M4MTlWC7GJHEhJpGL0UnsOx/N9cQUOtQpRb8mFWhauWjenOWaGyTHGpOsIk/D5s+MyVYeheCyNVqdfwWo1xfKNzXs0vWMzlk//ZRkKy5dusS7777LN998A8CGDRto06aNQzXpPgKMGDkbNhjxf/RbQf7AYlEcvBDD0Yux7Dl3jT3nrhEVb8LVRahb1p+yRbwo4+9NrVp+DG5WkaaVizl32/6JtbDwCUi2DsVMioFi1aDRY1C1rRESIeA+p+ykzU0mTZrEO++8g9lspmvXrnTr1s3hTuB2OI0jOHYM+vY1Qgp3zi5LgsahKKU4eSWO7WeiWLrvAhGxyQSW96dxxaI8dn8lihXyoFRhL+ccn29OhuQ4ow0/JgzSgrGFzIX9vxjrVR6Ebp9DQDVHqXRazpw5Q6dOnThx4gTu7u5MnTqVbt26OVpWjnAaR9CggRHXvXJlRyvR3IxSih1novhpWyj/nryKn5c7QZWL0j+oAr0alXPOm35KEhz6HVzc4Ohyo3kn8kTGfr+yGTf7pBjo8B7c/xy46s5vR2AymahVqxYmk4nmzZuzYsUKihQp4mhZOcZpHIGPjxFxVJM3sFgUx6/EsubQZZYfuIgp1cLwFpV55+E6lPZ38k7JLVNgzTvGev1HICUBmgyFSi2Msfi6aSfPcOzYsfQgcaNGjaJevXoMHjzY0bLuGKfpLD592hg11K+fMYJIk7sopdh2Oopfdp7jVEQ8pyLiCPD1oF2tUnSqW5pmVZy4fT/puvH0f2gxXD4M8VegRmfo8/2NsfA1eQaLxcKzzz7LjBkzGDRoEHMyp8nLo+jOYowUh88+Cy1aaEeQm0TFm1i+/wI/bwvFouCxZhUZ2qIy95X0xc/LCZsxLBa4dgYiT0LMeSNc8hFrLs6aD0PbN6FqGyhWxZEqNdmwZcsWevbsSWRkJH5+fgwaNMjRku4Zp3EEDz9sZLfSs4pzh2RzKhuPRfDm4gM0qxLA+B51aV41wDmHdCbHwb75sH2a4QAACpcz2vgLlTCe/Ov1zTJLlSZv8dxzzzFt2jQA+vXrx/z583ErABmu8v8V5BAvLyhb1tEqCjbXk1LYcCyC1Ycu8c/xCKqX9GXyow15sIYTet/rF4zRPKc3wtlNxrZq7eDhyUbqQp97yzalcQzly5cnICCAJUuW0DItF20BwGn6CPbsMbKTjRwJfn63L6/JGUcvXWfLyUj+OR7B7tBrNK1clE51S9OudilK+Hk6Wl7uoJRxs796As5sNMIrA7h6GqEYKjaHBv0dq1FzVyQkJNCnTx9SUlJYt24dkHtB4myN7iMAdu6Ed96BESO0I7AFpyLimLbhFGuPXKZL/TI8ElSerwc3Ts9361T8OwXWjDVy25aoBW3eNIZy6o7efM38+fMZMWIEiYmJVKxYMU8FibM1TvNGYLEYi6urHn13L4RHJ7Lh2BU+++s4g++vxLAWlSlWKPcTaTiE6HNw6m84uwWuHAY3T3BxN4Ky1X8E+s5wtEKNDYiOjqZr165s3boVFxcXXn31VT7++GNHy7pn9BsB4OJiLJo7x5xqYc+5aNYcvsSiPeE0rxbAd0OCaFKpqKOl2Z+k67BzBmyYBKnJ4OFrBGBrMNBIlZiWML1sI8fq1NiM9evXs3XrVqpXr87q1aupUqXgj+ByGkewcyf8+SeMHq2bhm5FXLKZwxeuE59s5p8TEewLi8ai4FxUAqUKe9Ghdkl+f64FlYsXcrRU+7L6LTi5zgjUFm59+6zXz8iP61/Osdo0duHSpUuMHz+eadOm0bt3bzZu3OjQjGG5jdM4gj174P334bnntCO4mYjYZFYdusTkv45Rys+LAF8PmlcN4PXOtXB3c6F0YS/KFvF2tEz7E3EMdv4AO6ZDs2eNm7+7txGdU1Ng+eCDDxg3bhypqal069aNbt26OZUTACfqI9BkYLEo9oZd48ctZzl84TqRccm0rlGCQcEVaXFfcUfLcwwX9sJ3baBEbWg4EFq8rDuTCjgnTpygc+fOnD59Gg8PD7744guee+45R8uyG7qPQAMYOXon/3WcFQcu4uflRr8m5RnZrjpVihfC3dVJO1C2fWu0/ydFQ4VmMOIvRyvS5AImk4l69ephMplo2bIly5Yty1dB4myN0ziCbdtg8WJ46y0o7ISj+vaFRTNywV6aVCrGzyOCqV7KidvHrp2Fk2th3btG5M46PaHdOB262Qk4cuQI1atXx8PDg1dffZUGDRrQv7+e4+E0juDgQZgyBV55xbkcQapFMW3jKWZuOcOEHvV4OLCMoyU5nimNjPDOVdtCu7G6D8AJsFgsPPnkk8yaNYuBAwcyd+5cPvzwQ0fLyjM4jSN48kljcSYi45J5c/EBrsaZWPpiK+fo8M2OzZ/D2vHG+v8OgW9Jh8rR5A6bN2+mZ8+eREVFUbhwYYYMGeJoSXkOp3EEzkRIWDQbj0Xw87az9Glcni8HNMLL3ckDms0fBMeWQ/Az0OUj3RHsJDz77LNMnz4dgAEDBvDzzz8XiCBxtsZpvpF//4XffoMJEwpu09DVuGQ+WnmUlQcv0aNhWeY9dT81nLUvICURjq2AkHmgLMaM4J7fQKP8lzREc/dUqlSJEiVKsGTJEpo3b+5oOXkWpxk+OnOm0T9w7BiULm17XXmBF+ftwc/LjdGdajlP2IebObEGDi4ywj4DlG8KTZ8E31JQra1jtWnsTkJCAj179iQ1NZW///7b0XLyFHr4KDB8uLEUVDafuMrfR6/w75iHKOLjpE5gwyTYMBHKNITuX0LjoboJyImYO3cuTz75JElJSVSqVCnfRgl1BE7jCAoqSin+3H+R95YdZurARs7jBFKSjKGf274xhoJeD4fEa9DwMcMJuOqftrMQFRVF165d2b59Oy4uLowZM4aJEyc6Wla+wq7/LSLSGfgScAVmKKUm3bS/IjAbKGItM0YptcIeWjZtggULYOLE/N9HcDUumUW7z3PyShy7Qq8REZvMzyOCaVSxgAeBS0mCv96Gnd9n2igQ+KjRAVysKhTW2YecjU2bNrF9+3Zq1KjBqlWrnCJInK2xmyMQEVfga6ADcB7YKSJLlVKHMxV7G/hVKfWtiNQBVgCV7aHn1Cn49VcYOzb/OoIlIeEs2hPO3nPXaF2jBC2qBTAguAINyhfBrSDPDE5JhP2/wJ8jDbv1a9DiRfDyd6wujcM4f/4848ePZ8aMGfTs2ZPNmzcXqIxhuY093wiCgZNKqdMAIrIA6AlkdgQKSLst+wMX7CVm2DBjyY+YUy088/Nujl+J5fXOtfjGGRLAKAXhu2H3LNj7s7EtaISR6lG3+zs1EyZM4L333iM1NZVevXrRrVs37QTuEXveTcoBYZns80Czm8qMB/4SkZeAQkD7rE4kIk8DTwNUrFjR5kLzMkkpqXy57gSXriexdtSDeLo5wXyAYyth/gBjvUQt4w3ggVFGJFCN03Ls2DG6dOnCmTNn8PT0ZNq0aXTr1s3RsgoEjn6sHAjMUkpNFpHmwM8iUk8pZclcSCn1HfAdGMNH76aiDRtgzhyYPBn880GLgslsYeSCvfx7KpL7qxZj+uNNCrYTsFjg8kGY0xfir0DDwfDwZ+Du5WhlmjyAyWSifv36pKSk0Lp1a/78808K59c23jyIPR1BOFAhk13eui0zI4DOAEqprSLiBRQHrthaTFgYrFoF+SW8yPvLD2MyW/jrf60pVbiA3wwTr8EXgZB8HfwrwLOboXR9R6vS5AEOHjxIrVq18PDw4PXXX6dBgwb069fP0bIKHPbsYdwJVBeRKiLiAQwAlt5U5hzQDkBEagNeQIQ9xDz+OJw/DyXzQXiZo5eus3hvOJ8+0qBgO4H4SJjWCj6qbDiBZzfD/w5qJ6DBYrEwbNgw6tevnx4b6L333tNOwE7Y7Y1AKWUWkReB1RhDQ39USh0SkXeBXUqppcCrwPci8j+MjuNhKr9NdbYxm09cZdSvIXzYuz5FC+rs4KQY2LcAVr5m2IMXwn3tdSewBoANGzbQp08frl27hr+/PyNGjHC0pAKPXfsIrHMCVty0bWym9cNArnT3//03zJplhKLOq/knft56lg9WHGFin/p0b1BAx8PP6w/HVxnrQSOgy8d68pcmnaeeeooZM2YAMGjQIGbPnq2DxOUCTvMNX7wImzdDSoqjlWTN1HUnWLjnPGv+9yAVivk4Wo7tWfUmHF0G0aEweBFUz3KAmMbJqVatGiVLlmTZsmU0bdrU0XKcBqcJOpdXsVgUX68/ydzt51j6UktK+hXAPoGtX8PqN6Hrp1ClNZSo6WhFmjxCXFwcPXr0wGKxsGHDBkfLKdDooHN5FKUUI38J4UJ0It8NaVLwnIBSsHeO4QRqPgzBTzlakSYPMXv2bJ555hmSk5OpWrWqDhLnQJzmW//rL+jfH6KjHa0kg09WH+PklTjmPdWMwPJFHC3Htlw9AT92hqUvQmB/6D/H0Yo0eYSrV68SFBTEsGHDSElJ4e233+bUqVPaCTgQp3kjiIyE/fvBbHa0koyIoQt2hhXM2cLXL8JXQeDpD4/+ZCSH12isbNmyhd27d1O7dm1WrVrldNEC8iK6jyCXUUrx7JzdnIqI54v+DalXLh9Mc75Txluvaew10E95GuDcuXOMHz+eH3/8EYDt27fTrNnNEWc09iS7PgL9X5qLhEUl0OvrLVyMSWLlyAcKnhOwWGCFdW7AqCPaCWgAGD9+PFWqVGHmzJmsWGGMJtdOIG/hNP+pq1ZB796O7SOYtvEUzaoG8PtzLXAvSGGjLamQHAuLn4Yd043UkDovgNNz5MgRKleuzIQJE3B3d+fHH3+ka9eujpalyQKn6SO4fh1On4bUVMfUf/JKLP+eimRCj7oFJ3fA1ZNweDFs/BhSTca2wQuhegfH6tI4HJPJRIMGDUhJSaFt27YsXboUX19fR8vS3AKncQSPPmosjmDe9nNMWnmEVtWL06hiEceIsBXmZDi/Cywp8FNP8CsDjYdAh/fAowBOhNPcEfv376dOnTp4eHgwZswYGjVqRO/evR0tS3MbcuwIRMRHKZVgTzEFEZPZwvR/TjFzeDBNKuXjVJKmeNg9G1a/YdgVW0C9vtDvR8fq0uQJzGYzQ4cOZd68eQwYMID58+fz7rvvOlqWJofc1hGISAtgBuALVBSRBsAzSqnn7S3OlixfDt98Y+QkKJqL9+PXFu6jVmk/GufXNwGlYMMk2GhNN91gEDz8KXgUcqwuTZ5h3bp19OvXj+joaIoWLcozzzzjaEmaOyQnbwSfA52whpBWSu0TkdZ2VWUHEhPhyhVjYEtuceB8DFtPR7JxdFskP0bWXP0WbP3KWG/4GHT9WDsAzQ088cQTzJw5E4AhQ4Ywc+ZMPTEsH5KjpiGlVNhNNzIHdbnePf36GUtusfrQJd5bdpiXHqqOl3s+mzCmFPzymBEkrtUoePB1nSlMkyW1a9emdOnS/PnnnwQFZTlEXZMPyIkjCLM2DykRcQdGAkfsKyt/8/fRy7y1+ACfPdqQ1jVKOFrOnZEUA5NrQ0o8dPoQmr/gaEWaPMT169fTg8T9888/jB49mtGjRztaluYeyck73LPACxjJ6MOBhkC+6h8AWLoU2reHa9fsV4dSipUHLjL6t/1Mf7xJ/nMCqSkwqaLhBJ7fpp2A5gZ++OEHSpYsycaNG7lw4QKW3Gxn1diVnDiCmkqpwUqpUkqpkkqpx4Da9hZma8xmSEqybx3LD1xkwp+H+ax/Q5pUKmbfymxFchxcCIEjfxp5gwFG7oOS+e5PrLETV65coUmTJjz55JOYzWbGjRvHyZMndV9AASInTUNTgcY52Jan6dPHWOzJD5vPMKFnXR7ML28CZhNMLGesF6sKRSvBIzOhaGWHytLkLbZt28aePXuoW7cuq1atonz58o6WpLExt3QEItIcaAGUEJFRmXYVxshBrMnEvyevcuV6Mu1rl3K0lJwzx+oZ37yoJ4NpbiA0NJSxY8cye/ZsevTowY4dO3TGsAJMdu92HhhzB9wAv0zLdSAXx9/YhsWLoXVr2/cRRMYl88bvB3hx/l7e71UPV5d8MEz03Db4qRec3WSEidZOQJOJN998k6pVq/LTTz+lB4nTTqBgc8s3AqXURmCjiMxSSoXmoia74OIC9siBvXhvOKci4lj+civK+HvbvgJ7sGCwERtowHyo2cXRajR5hEOHDtGlSxfCwsLw8vJi+vTpOkick5CTW2OCiHwC1AXSB5MrpR6ymyo70LOnsdiSUxFxfLn2BDOGBuUPJ3B2CywYaAwRfX6b7hDWpGMymWjUqBEpKSm0b9+eJUuW4OOj3xSdhZx0+88FjgJVgAnAWWCnHTXlG/7cd4E+jcvRrGqAo6VkjykB9s6FWV0hoDq8tEc7AQ0Ae/bswWw24+HhwVtvvcWSJUtYs2aNdgJORk4cQYBS6gcgRSm1USn1BJCv3gYAFi6EZs0gKsp251y8N5zejfPwCIrLh2FSJfiwDCx53ggT8dQ6CKjmaGUaB2M2mxk4cCBNmjThscceA2DcuHH06NHDwco0jiAnTUMp1s+LIvIwcAHIJ4PkM/DygmLFbJc0KyQsmvhkMw3K59EsY/9Ohb/eNtZf3A3F73OsHk2eYfXq1fTv35+YmBiKFSvG88/nu/mhGhuTE0fwvoj4A69izB8oDLxiT1H2oFs3Y7EF8clmen29hcmPNMh7weSuhcI3zY3Zwa1GQatXwCuPOitNrjNs2DBmz56NiDB8+HBmzJihJ4Zpbu8IlFLLrKsxQFsAEWlpT1F5nY9XHaV/UAX6NsljzUJKwYJBhhN4ea8xSUyjyUTdunUpU6YMK1asoGHDho6Wo8kj3PJRQERcRWSgiPyfiNSzbusmIv8CX+WaQhvxyy/QqNG9zyOYsy2UJfsu8PSDeewmey0UPq8Hlw/CiLXaCWgAI0hcq1ataNWqFQCjR4/mwoUL2globiC7d8IfgCeBAGCKiMwBPgU+Vko1yg1xtsTPDypWvLc+gh1novhi7XF+eiKYaiXyUP7V0xvhy0CIuwxP/Q0V9OQfDUyfPp0SJUqwZcsWrly5ooPEaW5Jdk1DQUCgUsoiIl7AJaCaUioyd6TZlq5djeVusVgUL8zbw6sdaxJYvojNdN0zW78x0keWDjQSx/vloxAXGrtw6dIlOnfuzL59+3B1deX999/nrbfecrQsTR4mO0dgUkpZAJRSSSJy+k6dgIh0Br7EiE00Qyk1KYsyjwLjAQXsU0oNupM6cos520MpVdiTgcEVHS3FICURfhsGx1dB0yfh4cmOVqTJI+zYsYN9+/ZRv359Vq1aRdmyZR0tSZPHya6hpJaI7LcuBzLZB0Rk/+1OLCKuwNdAF6AOMFBE6txUpjrwBtBSKVUXO45GmjcP6ta9u3kEEbHJTPjzMG92zSOTsJSCD0obTqDdWO0ENJw5cyZ9PkCPHj3YvXs3+/fv105AkyOyeyO417teMHBSKXUaQEQWAD2Bw5nKPAV8rZS6BqCUunKPdd6SYsWgTh1wvYu4qasOXqRHg7K0qFbc9sLulBNrYW5fY33sNdtNjNDkW15//XU+/fRTLBYLgwcPpkuXLjRunK+ixGscTHZB5+410Fw5ICyTfR5odlOZGgAisgWj+Wi8UmrVzScSkaeBpwEqVry7ppnOnY3lTtlxJoopf5/ko77176pem3JxH6x/H3yKw7ObtRNwcvbv30/Xrl0JDw/H29ub77//ni5ddBBBzZ3j6DuJG1AdaAMMBL4XkSI3F1JKfaeUClJKBZUokbtJX77dcJJXO9TgoVoO7oRNNcP01sbksCfXQuEyjtWjcSgmk4mgoCDCw8Pp2LEjV69eZfDgwY6Wpcmn2NMRhAMVMtnlrdsycx5YqpRKUUqdAY5jOAabM2cO3HcfRN5Bd3dsUgo7z17j4cA8cNNd/Izx+chsKFbFsVo0DmPnzp3pQeLefvttli9fzurVq3WQOM09kSNHICLeIlLzDs+9E6guIlVExAMYACy9qcwfGG8DiEhxjKai03dYT44oVQruvx/c3XN+zPpjETSsUAQ/rzs4yB6sew8OLoRuX4B3Ecdq0TgEs9lMv379CA4OZtAgY2Dd2LFjdb4AjU24rSMQke5ACLDKajcUkZtv6P9BKWUGXgRWA0eAX5VSh0TkXRFJC3G4GogUkcPAemC0veYpdOhgvBUULpyz8kopvv/nNI83r2QPOTnn+kXY9Ck0fxGChjtWi8YhrFixgmLFirFo0SICAgIYOXKkoyVpChg5CTo3HmME0AYApVSIiOSobUIptQJYcdO2sZnWFTDKuuQpTlyJIyreRAdH5iA2m+CzWsZ6h/ccp0PjMIYOHcpPP/2EiPDUU08xbdo0HSROY3Ny8otKUUrF3LRN2UOMPZk1ywgxkdN5BKsOXqLVfcVxcVQOYqVgyQvG+phzeoSQk9KgQQPKlSvH/v37+e6777QT0NiFnPyqDonIIMBVRKqLyFTgXzvrsjkVKkD79jnrI0hKSeWzNcd5qrUDA7dt/AgO/Gr0C+gw0k5DdHQ0LVq0oEWLFgCMGjWK8+fPU69ePQcr0xRkcuIIXsLIV5wMzMMIR/2KHTXZhXbt4McfjeBzt+N0RDzFCnlwX0kHBZaLOA4bJkLQE7pfwIn49ttvKVWqFFu3biUqKkoHidPkGjlxBLWUUm8ppZpal7eVUkl2V+ZAZmw+zSBHxhRa9Tp4+EGXTxynQZNrXLhwgcDAQJ5//nksFgsffvghR48e1c1AmlwjJ7+0ySJyRETeS8tLkB/54QdjCOnt5hHEJZtZGuLAfAMHF8Gpv+GRmeCak758TX5nz549HDhwgAYNGhAWFsYbb7zhaEkaJ+O2jkAp1RYjM1kEMN0adO5tuyuzMdWqQZ8+4OmZfbltpyK5v2oAhR0xd+DEGlj4BNR/BKp3yP36NbnGiRMnGDhwIADdunVj7969hISEULp0aQcr0zgjOXr3VEpdUkpNAZ7FmFMwNvsj8h5t2sC334LvbZr9o+JNlCx8G29hDyypMLcfVGoFfWfkfv2aXMFisfDqq69Ss2ZNFixYwMqVKwF0xjCNQ7lt24OI1Ab6A32BSOAXjET2BZJtZyJp4IjEM7MeNj61EyiwhISE0LVrVy5evIiPjw8zZ87UQeI0eYKcvBH8CEQDnZRSbZRS39ozXLS9+P57KFr09n0E209H0bpG7ga2Y9dMOLcVhq/SweQKKCaTiaZNm3Lx4kW6du1KZGQkjz76qKNlaTRADt4IlFLNc0OIvalZE4YMyb6PICYhhegEE5WK5VIAL6VgTh+jc7hGZ6hUIL5qTSa2b99OkyZN8PDwYPz48QQFBdGpUydHy9JobuCWjkBEflVKPWrNTpZ5JrFgRIcItLs6G9K6tbFkx9FL16lR2i/3ZhMfWmw4ge5ToPGQ3KlTkyuYTCYGDhzI77//ziOPPMKvv/6q8wZr8izZvRGkRbbqlhtC8gKHLlyndpkcRqW7VywWWDgc6vWFJkNzp05NrrBs2TIGDhxIXFwcJUqU4H//+5+jJWk02XLLPgKl1EXr6vNKqdDMC/B87sizHd9+C4UK3bqPwJxqYc62ULrUy6Xhe/98bHx2/TR36tPkCo899hjdu3cnPj6eZ599lkuXLtG8uW7y0+RtctJZnNWA9nw31KF+fXj+efDyynr/HyEXKFnYk1b35UJe4ugwI4REu3HgU8z+9WlyjcaNG1OhQgUOHDjAt99+q2cHa/IFYkSCzmKHyHMYT/5VgVOZdvkBW5RSj9lf3n8JCgpSu3btsvl5n/15N13ql6Znw3I2P/d/WDEa9v9iRBXV5GuioqLo0qULIsK2bdscLUejuSUislspFZTVvuweV+YB3TGyinXPtDRxlBOwJ2euxudOkLmURNjxHbQebf+6NHZl6tSplC5dmh07dhAbG6uDxGnyLdk5AqWUOgu8AMRmWhCRfNee8dVX4OaWdR9BbFIKpyLiqBxQyP5CVr8J4gr3v2D/ujR2IS0s9Msvv4xSik8//ZRDhw7pZiBNviW7UUPzMEYM7cYYPpp5TKXCaDLKNzRpAmPGgLf3f/ctCblAu9olKeRp5yBvoVth14/Q4yudaCYfs3//fg4dOkSTJk1YsWIFJUuWdLQkjeaeuOWdTynVzfqZo7SUeZ3mzY0lK1YdvMTgZrkQdnrF/4FfWWj8uP3r0tiUY8eOMW7cOBYsWEDXrl05cOCAThajKTDkJHl9SxEpZF1/TEQ+ExEHBuu/O5Qyhu7f3Dd+KiKOwxev08Leo4WiTsPlg9Dra/vWo7EpFouFkSNHUrt2bX755RdWr14NoJ2ApkCRk/aJb4EEEWmAEWzuFPCzXVXZga++AlfX/+YsDo2MJ7C8P/7edg47/ccL4FMcqj1k33o0NmPPnj2UK1eOKVOm4O3tzcKFC3V4CE2BJCeN4mallBKRnsBXSqkfRGSEvYXZmmbNYMKE//YRHLkYa//cA2E74Ny/8Pgf9q1HYzNMJhPNmjXDbDbTrVs3fvvtN7xuNQlFo8nn5MQRxIrIG8DjwAMi4gI4IGvLvREcbCw3s2DnOd5+uI59K1/1BpRvCtXa2rcezT2zZcsWmjZtioeHB++++y7BwcG0a9fO0bI0GruSk6ah/hiJ659QSl0CygP5Lpmu2QyJiTf2EZhTLVy+nsyD9gw7fWAhhO/SoSTyOCaTiZ49e9KqVSsGDx4MwBtvvKGdgMYpyEmqykvAXMBfRLoBSUqpn+yuzMZ88w34+MC1axnbzl9LpISvJ17urvap1JIKi0ZAzYehbEP71KG5ZxYvXkyxYsVYunQpJUuW5LXXXnO0JI0mV8nJqKFHgR3AI8CjwHYR6WdvYbamRQuYNOnGPoK/j16hblk7Rhs9udb4fHS2/erQ3BODBg2iT58+JCQk8OKLL3Lx4kWaNm3qaFkaTa6Skz6Ct4CmaVnJRKQEsBZYaE9htiYoyFgyczA8hsDy/vap0GyCeY9CxRbgmu+6VAo8FosFFxcXmjVrxr///svKlSupXbu2o2VpNA4hJ30ELjelpozM4XF5iuRkiIkx5hKkEXYtgaDKdoqWEXfJ+Bzyh33Or7krrl69SlBQUHpo6JEjR3L27FntBDROTU5u6KtEZLWIDBORYcByYIV9Zdme6dOhSBGIjjZspRQ7z16jgr3SUq7/ELz8wS2b3JiaXOWLL76gTJky7N69m4SEBB0kTqOxkpOcxaNFpA/QyrrpO6XUYvvKsj0PPACffWZ0GAMcCI8BoExhO4wNP7kO9s2HHlNtf27NHXPu3Dk6derE0aNHcXNzY/LkyYwaNcrRsjSaPEN2OYurA58C1YADwP8ppcJzS5itadTIWNLYE3qNgcEVbJ+f+OoJIyF9idrQSMcUygscPHiQo0ePEhQUxMqVKylePBeSD2k0+YjsmoZ+BJYBfTEikN7x462IdBaRYyJyUkTGZFOur4goEckyaYItSEiAiIiMPoL94TFUL+ln20pOb4DpD4JnYXh+K4iNnYwmxxw5coR+/YzBbV27duXw4cPs3LlTOwGNJguycwR+SqnvlVLHlFKfApXv5MQi4gp8jZHWsg4wUET+M4VXRPyAkcD2Ozn/nTJjBpQsmdFH8M/xq9QrZ8MRQ3FX4KeeUKSidgIOxGKx8MILL1C3bl0WLVqUHiROdwZrNLcmuz4CLxFpREYeAu/MtlJqz23OHQycVEqdBhCRBUBP4PBN5d4DPgLsmrKrTRsj8JxPet+wonJxG3UUm5Ph83rgXRRe0OkKHcX27dvp0aMHV65coVChQsydO1cHidNockB2juAi8Fkm+1ImWwG3C6NZDgjLZJ8HmmUuICKNgQpKqeUicktHICJPA08DVKx4dxGwAwONBYwRQ9EJKRTx9rirc/2H35+G1GR4xva5lDU5IykpiVatWmE2m+nVqxe//PILHh42+vtqNAWc7BLT2DVCmjV43WfAsNuVVUp9B3wHRvL6u6kvNtaYR1C2LMSZzHi6ueDhZoPpEInX4PAf0Hu60SykyVU2b95McHAwXl5efPDBBwQHB9OmTRtHy9Jo8hX2nBgWDlTIZJe3bkvDD6gHbBCRs8D9wFJ7dRj/+CNUqGA4g0Ph1ylpq2Gje+eAqyc0GGCb82lyRFJSEt26deOBBx5g0KBBALz22mvaCWg0d4E9HcFOoLqIVBERD2AAsDRtp1IqRilVXClVWSlVGdgG9FBK2aV9pV07+P57o49gb9g1Hqhuo9Ej26ZBzc62OZcmRyxcuJCAgACWL19O6dKlefPNNx0tSaPJ19jNESilzMCLwGrgCPCrUuqQiLwrIj3sVe+tqFcPnnwSPDwUIeeiqVDUBh3Fn1SH6+fhwVuOjNXYmAEDBvDII4+QmJjIK6+8Qnh4OI0bN3a0LI0mX3PbmcUiIsBgoKpS6l1rvuLSSqkdtztWKbWCm8JRKKXG3qJsmxwpvktiYiAyElShBP46fPnek9HsWwDxV+Cp9VDKzoltNOlB4lq2bMmOHTtYuXIlNWvWdLQsjaZAkJM3gm+A5sBAqx2LMT8gXzFrFlSrBrtOxNKkUlEqBtzDG4HFAhsmQd3eUE4/jdqTK1eu0LhxY5o1MwacvfTSS5w+fVo7AY3GhuTEETRTSr0AJAEopa4B+W5cXseOMHs2mCWFygGF7u1k276Ba2fggf+zjThNlnzyySeUK1eOvXv3YjKZdJA4jcZO5MQRpFhnCStIz0eQ7/4ja9eGIUMgIjGRMv73OGLor7egXj8oXc824jQ3EBoaSs2aNXnttdcQEaZOncq+fftwccl30c81mnxBThLTTAEWAyVF5AOgH/C2XVXZgagouHwZwiITCapS9O5PdCHE+Oya79I25xsOHz7M8ePHadasGStWrKBYMTvljNBoNEDOchbPBV4DJmLMNu6llPrN3sJszc8/Q506EHIqniLe95AxbOvXULYR+Oibky05ePAgffr0AaBLly4cPXqUbdu2aSeg0eQCOclZXBFIAP7EmAcQb92Wr+jSBRYsgJPXoqleyvfuT3TiL2gw8PblNDnCYrHw3HPPERgYyOLFi1mzZg2A7gzWaHKRnDQNLcfoHxDAC6gCHAPq2lGXzalRAypVTeWd/XL3ncXJsZAUDZVb3bao5vZs3bqVnj17EhERga+vL/Pnz6dDhw6OlqXROB05yVBWP7NtDRT3vN0U2YmICDhyyoK3uyturnfZ6TjHiG9PsWq2E+akJCUl0bp1a8xmM3379mXevHk6SJxG4yDu+I5oDT/d7LYF8xhz58KDzd2RlHu42VwMgd7fgbsd0ls6CRs2bMBkMuHl5cWHH37Ipk2bWLhwoXYCGo0DycnM4szJXV2AxsAFuymyE926gXexJGadu6vgpXBwEZiToIaOb383JCUl0bt3b1atWkWfPn1YtGgRo0fbNQWFRqPJITnpI8icz9GM0WewyD5y7Md998GR+CjqcZfpKU/9DTU6g3cRm+pyBn755ReGDx9OYmIiZcqU4Z133nG0JI1Gk4lsHYF1IpmfUirfT6G9dAn27hGK+Xre3Qn2/QLBT9lWlBPwyCOPsHDhQlxcXPi///s/PvlEz7/QaPIat+wjEBE3pVQq0DIX9diNBQtg3BNl8Lyb6BgX9oIlBYKftr2wAkpaOIjWrVtTrVo1jh8/rp2ARpNHya6zOC26aIiILBWRx0WkT9qSG+JsSc+eUG/4fppVv4uE9ZcPQYlaUKyK7YUVMC5dukTDhg0JDg4GjCBxJ0+epFo1PdJKo8mr5GTUkBcQiZGjuBvQ3fqZr6hSBXzuu0LF4t53fvDhpeBf4fblnJyJEydSvnx59u3bh9ls1kHiNJp8QnZ9BCWtI4YOkjGhLI27HHrjOMLDIfJkYbzcctI/fhPxEdDqFZtrKiicOXOGjh07cvLkSTw8PJgyZQrPP5/vpppoNE5LdndFV8CXGx1AGvnOEfz2G5yaGYxMSL6zAxOi4MIe/UaQDUePHuXkyZM0b96cFStWUKRIEUdL0mg0d0B2juCiUurdXFNiZx7ukcqnO3ZTrkTQnR24/FVwL6QT0NzE/v37GTt2LH/88QddunTh+PHjVK9e3dGyNBrNXZBdH0FWbwL5ltJlLRSpfg1vrzuYTL1gMBz6Hdq8bj9h+QyLxcJTTz1Fw4YNWbJkSXqQOO0ENJr8S3ZvBO1yTUUucDZUkXg2ALMZctRNkJoCR5fBiDVQIdju+vIDmzdvplevXkRGRuLn58eCBQt0kDiNpgBwy8djpVRUbgqxN3/87sLpn4KIj8/hAVePG5/aCQBGiIi2bdsSGRlJ//79iYqKomvXro6WpdFobIDT5P5r97CJwGf2UCinEagPL4XiNeyqKT+wbt269CBxH330EZs3b2bBggW43c3oK41GkydxGkdQqoyFgPuu56xZaM9PsHGSUweYS0hIoGPHjrRv356BA41EPKNGjaJlywIx0Vyj0WTCaRxBWKgQebQYKSm3KRhxHJa+BJUfgIfG5oq2vMbcuXMpXrw4a9asoXz58kyYMMHRkjQajR1xGkewZoUb+38IJCHhNgWntQSvIjBsGbg5X4z8vn378thjj5GcnMxrr71GWFgY9erVc7QsjUZjR5ymoffh3mZWXT1IoUJNbl3owl5INcH/Hc89YXkEi8WCi4sLbdu25cCBA6xevZoqVXRsJY3GGXCaN4KSpRT+lWKz7yOYPxD8yoB30VzT5WguXLhAYGBgepC4F198kePHj2snoNE4EU7jCELPChEHA27dR5AcB7EXYcDcXNXlSN5//30qVqzIgQMHUErpIHEajZPiNI7g71WuHJxV/9Z9BGvGgrhCuWyajgoIJ06coFq1arzzzju4uroyffp0du/ejYuL0/wcNBpNJpymj6BHXzNronfh63uLWEO7foCHnCOF4unTpzl9+jQPPPAAy5Yto3Dhwo6WpNFoHIhdHYGIdAa+xIhkOkMpNemm/aOAJzFyIUcATyilQu2hJaCEwq9cHK6uWezc+rXx2XiIParOE4SEhPD222+zdOlSOnXqxOnTp3U/QAEiJSWF8+fPk5SU5GgpGgfj5eVF+fLlcXd3z/ExdnME1nzHXwMdgPPAThFZqpQ6nKnYXiBIKZUgIs8BHwP97aHn9Enhyr4SpKTADd9PfCSsfhPajQPfkvao2qFYLBZGjBjBrFmzAGOmcIcOHbQTKGCcP38ePz8/KleujEiBihepuQOUUkRGRnL+/Pk7+h+3Z6NwMHBSKXVaKWUCFgA9MxdQSq1XSqW12m8DyttLzPo1bhz8uS6JiTft+KmH8dnyFXtV7TD++ecfSpQowaxZs/D392fVqlU6SFwBJSkpiYCAAO0EnBwRISAg4I7fDO3ZNFQOCMtknweaZVN+BLAyqx0i8jTwNEDFihXvSkyvR8ysj99JoUJNb9wRfxWGrYAC1lGalJREu3btMJvNDBw4kJ9++knHByrgaCeggbv7HeSJu5+IPAYEAZ9ktV8p9Z1SKkgpFVSiRIm7qqNYgMK3dMKNfQSmeIi7VKCCy61ZsyY9SNwnn3zCtm3bmDdvnnYCGo3mltjTEYQDmfM7lrduuwERaQ+8BfRQSt1hHsmcc/K4Cxd3l8RkyrTx/E5w9wHfu3MueYmEhATat29Px44dGTBgAACvvPIKzZpl9xKm0dgOV1dXGjZsSL169ejevTvR0dE2Oe+sWbN48cUXbXKuzLRp04aaNWvSsGFDGjZsyMKFC21eB8DZs2eZN2+eXc5tK+zpCHYC1UWkioh4AAOApZkLiEgjYDqGE7hiRy3887crh+fV5oams5jzUP4OU1fmQX766ScCAgJYt24dFStW5L333nO0JI0T4u3tTUhICAcPHqRYsWJ8/fXXjpZ0W+bOnUtISAghISH069cvR8eYzeY7qsOpHYFSygy8CKwGjgC/KqUOici7ImLtoeUTwBf4TURCRGTpLU53z/QZkEKLN7bj65tpY+RJ8AmwV5W5Qp8+fRg6dCgmk4k333yT0NBQ6tat62hZGienefPmhIcbDQA7duygefPmNGrUiBYtWnDs2DHAeNLv06cPnTt3pnr16rz22mvpx8+cOZMaNWoQHBzMli1b0refPXuWhx56iMDAQNq1a8e5c+cAGDZsGM899xz3338/VatWZcOGDTzxxBPUrl2bYcOG5Vh3VFQUvXr1IjAwkPvvv5/9+/cDMH78eB5//HFatmzJ448/TkREBH379qVp06Y0bdo0XePGjRvT3zAaNWpEbGwsY8aMYdOmTTRs2JDPP//8nr5Xe2HXhmOl1ApgxU3bxmZab2/P+jNT2B98iifd2Cd8dHm+zTmQFiSuQ4cOHDlyhFWrVlGpUiVHy9LkESqPWW7zc56d9HCOyqWmprJu3TpGjBgBQK1atdi0aRNubm6sXbuWN998k0WLFgHG/Ja9e/fi6elJzZo1eemll3Bzc2PcuHHs3r0bf39/2rZtS6NGjQB46aWXGDp0KEOHDuXHH3/k5Zdf5o8//gDg2rVrbN26laVLl9KjRw+2bNnCjBkzaNq0KSEhITRs2PA/WgcPHoy3tzdgDK0eP348jRo14o8//uDvv/9myJAhhISEAHD48GE2b96Mt7c3gwYN4n//+x+tWrXi3LlzdOrUiSNHjvDpp5/y9ddf07JlS+Li4vDy8mLSpEl8+umnLFu27B6+ffviND2Ix4+6cGFHaUwm8EiLLn31OPSd4VBdd8r58+fp1KkTnp6e7Nmzh+eee47nnnvO0bI0eYyc3rRtSWJiIg0bNiQ8PJzatWunD1WOiYlh6NChnDhxAhEhJVPAr3bt2uHv7w9AnTp1CA0N5erVq7Rp04a0gSH9+/fn+HEjIvDWrVv5/fffAXj88cdveIvo3r07IkL9+vUpVaoU9evXB6Bu3bqcPXs2S0cwd+5cgoIymoc3b96c7qQeeughIiMjuX79OgA9evRIdxpr167l8OGMKVHXr18nLi6Oli1bMmrUKAYPHkyfPn0oX95uI+JtSp4YNZQbbNngyuFfamb0EcRFGJ8l808zyoQJE6hcuTKHDx/G1dVVB4nT5CnS+ghCQ0NRSqX3Ebzzzju0bduWgwcP8ueff94wxt3T0zN93dXV9Y7b3zOTdi4XF5cbzuvi4nJP502jUKY8txaLhW3btqX3L4SHh+Pr68uYMWOYMWMGiYmJtGzZkqNHj95zvbmB0ziCRwan0OqdbRl9BPvmgac/uOb9l6Jjx45RpUoVxo8fj5ubGzNmzGDnzp06SJwmT+Lj48OUKVOYPHkyZrOZmJgYypUrB5A+wz07mjVrxsaNG4mMjCQlJYXffvstfV+LFi1YsGABYDzNP/DAAzbV/sADDzB3rhGBeMOGDRQvXjzLWFwdO3Zk6tSp6XZa89GpU6eoX78+r7/+Ok2bNuXo0aP4+fkRGxtrU522xmnuJIX8FN5FkzP6CK4eh/p9Haopp5w7d46zZ8/y4IMPcvXq1fS2V40mr9KoUSMCAwOZP38+r732Gm+88QaNGjXK0ZN5mTJlGD9+PM2bN6dly5bUrl07fd/UqVOZOXMmgYGB/Pzzz3z55Zc21T1+/Hh2795NYGAgY8aMYfbs2VmWmzJlCrt27SIwMJA6deowbdo0AL744gvq1atHYGAg7u7udOnShcDAQFxdXWnQoEGe7SwWpZSjNdwRQUFBateuXXd83OK1cbw17SIh86obfQTj/aHLx9DsGduLtAG7du1i7NixLFu2DBcXF0JDQ3VnsOaWHDly5IYbpsa5yer3ICK7lVJZjpd3mjeCbZvdOLKoOsnJQGK0sbH+I46UlCUWi4UhQ4bQtGlTVq5cyfr16wG0E9BoNHYj7zeQ24hHHzex3WUvhQo1h8vnwNUTfIo5WtYN/P333/Tt25fo6GiKFCnCwoULadeunaNlaTSaAo7TOAIfH/AqnGL0EVwPh4BqjpZ0A0lJSXTs2JHU1FQGDx7MrFmzdHwgjUaTKzhN09Dh/S6EbixrxBqKOgMueeMmu3LlSpKSkvDy8mLy5Mns2LGDOXPmaCeg0WhyDadxBNv/dePoH/cZfQQublCusUP1xMXF0bZtW7p27cqgQYMAGDlyJE2bNr3NkRqNRmNbnMYRDBxq4qEP/jXmEVwMATdvh2mZOXMmxYsXZ8OGDVSqVImJEyc6TItGo9E4jSPw9AJ3HzMiwP5foahjRuH07t2bJ554gpSUFN555x3Onj1LzZo1HaJFo7ElvjdEdLw7du3axcsvv3zL/TdH8rxd+ZtJCz3doEGD9BhEeYWlS5cyadKk2xe0A07TEH0gxIUzf5fH9PglPCwp0HBQrtafFiSuc+fOHDt2jFWrVt11tjWNpqASFBR0Q+yfm0lzBGnNqbcrnxVp8YVmzpzJ6NGjWbNmzT1pBiPQnusNWa/unB49etCjR4/bF7QDTvNGsGubK8f/rEryqZ3gWRi8/HOl3nPnzlG7dm2aNGkCwDPPPMPhw4e1E9A4BSEhIdx///0EBgbSu3dvrl27BsDOnTsJDAykYcOGjB49mnr16gFGWIdu3boBOQvpnLl8XFwcw4cPp379+gQGBqYHj7sVmUNlx8fH88QTTxAcHEyjRo1YsmQJYCR8evTRR6lTpw69e/emWbNmpE1o9fX15dVXX6VBgwZs3bqVOXPmEBwcTMOGDXnmmWdITU0lNTWVYcOGUa9ePerXr58+s3jKlCnUqVOHwMDA9ERSmRPwZBdu++WXX6ZFixZUrVrVZsl0nOaNYPAIEyG+e/BNOQuVWuRKne+88w4ffvghFouF4ODg9LcCjcbujLfDg874mDs+ZMiQIUydOpUHH3yQsWPHMmHCBL744guGDx/O999/T/PmzRkzZkyWx+YkpPOGDRvSy7/33nv4+/tz4MABgHSncytWrVpFr169APjggw946KGH+PHHH4mOjiY4OJj27dvz7bffUrRoUQ4fPszBgwdviGAaHx9Ps2bNmDx5MkeOHOGjjz5iy5YtuLu78/zzzzN37lzq1q1LeHg4Bw8eBEjP2jZp0iTOnDmDp6dnlpncsgu3ffHiRTZv3szRo0fp0aNHjhPqZIfTOAJ3d3DztCBRJ6F4dbvWdeTIEbp06UJoaCheXl5MmzaNoUOH2rVOjeYG7uKmbWtiYmKIjo7mwQcfBGDo0KE88sgjREdHExsbS/PmzQEYNGhQlrH67zSk89q1a9MD0gEULVo0y3KDBw/GZDIRFxeX3kfw119/sXTpUj799FPAmNdz7tw5Nm/ezMiRIwHSYwil4erqSt++RryydevWsXv37vRRf4mJiZQsWZLu3btz+vRpXnrpJR5++GE6duwIQGBgIIMHD6ZXr17pzigz2YXb7tWrFy4uLtSpU4fLly9n+53kFKd5PA3Z5cqpvypgiroCPsXtWteFCxcIDQ2lbdu2REREaCeg0dwF9grpPHfuXE6fPs3QoUN56aWXAFBKsWjRovSw0mlNutnh5eWV3i+glGLo0KHpxx87dozx48dTtGhR9u3bR5s2bZg2bRpPPvkkAMuXL+eFF15gz549NG3a9I7CZGcOsW2rWHFO4wj27nTlxIrKmCIvQfEaNj//9u3b6dSpExaLhXbt2hEaGsrff/9tk5EUGk1+xN/fn6JFi7Jp0yYAfv75Zx588EGKFCmCn58f27dvB7jhKT4zdxrSuUOHDjfkSc6uaUhEeO+999i2bRtHjx6lU6dOTJ06Nf3GunfvXsB4K/n1118BI0NZWrPTzbRr146FCxdy5YqRej0qKio9yY7FYqFv3768//777NmzB4vFQlhYGG3btuWjjz4iJiaGuLi4G85n73DbN+M0TUNDnjZxsOguCiWegCK266g1m80MGTKE+fPnA7B+/XratWunO4M1TkdCQsINzTejRo1i9uzZPPvssyQkJFC1alVmzpwJwA8//MBTTz2Fi4sLDz74YHqWssx88cUXrF+/HhcXF+rWrUuXLl1wcXFJD+k8bNiw9BSWAG+//TYvvPAC9erVw9XVlXHjxtGnT59b6vX29ubVV1/lk08+4auvvuKVV14hMDAQi8VClSpVWLZsGc8//zxDhw6lTp061KpVi7p162aptU6dOrz//vt07NgRi8WCu7s7X3/9Nd7e3gwfPjw9idTEiRNJTU3lscceIyYmBqUUL7/8MkWKFLnhfFOnTmX48OF88sknlChRIv17sxtKqXy1NGnSRN0NB85Hqy6fb1RqXGGl4iPv6hw389dffyl/f38FqKJFi6r169fb5LwazZ1y+PBhR0u4I2JjY9PXJ06cqF5++WUHqrk1ZrNZJSYmKqWUOnnypKpcubJKTk52sKrbk9XvAdilbnFfdZo3gr07XTm/ogTJwR54emfdiXQnJCUl0aVLl/ThYT/88IMeEaTR5JDly5czceJEzGYzlSpVylHmMkeQkJBA27ZtSUlJQSnFN998g0d60vOCg9M4gn27XTmwthYpzYviKXLX51m2bBnt27fHy8uLL774ghYtWtC4sWPjFmk0+Y3+/fvTv39/R8u4LX5+ftxNIqz8htM4gmHPmnAv8iO+7oVuXzgLrl+/Tvfu3fnnn3/o1asXixcvTp/8odFoNPkZp2rLqJx6BgqVvOPjZsyYQcmSJfnnn3+oWrUqH3/8sR3UaTQajWNwGkew819X/lr+IMmF7yzAW/fu3Xnqqacwm81MmDCBU6dOUb26fSekaTQaTW7iNE1DRw65suzfTqSMOYXn7YtjNptxc3OjW7dunDlzhlWrVt12ZqNGo9HkR5zmjeDxJ00sefcxfEsUzrZcaGgoNWrUSO8AfuaZZzh48KB2AhpNDvjggw+oW7duekC5CRMm8MYbb9xQJiQkJH3WbuXKlf8zWaphw4bpQeg0uYPTOAKFwgVLtikq33jjDapWrcqJEyfw9fVNnwSi0Whuz9atW1m2bBl79uxh//79rF27lrZt2/LLL7/cUG7BggUMHDgw3Y6NjSUsLAww4nRpch+ncQTbt7gxe1l/klPc/7Pv0KFDVKhQgUmTJuHp6cmcOXP4999/9bwATb6lTRtIG5qfkmLYc+YYdkKCYafdn2NiDNsa44yrVw37zz8N+9KlnNV58eJFihcvnh4Lp3jx4rRu3ZqiRYumh5MA+PXXX29wBI8++mi6s5g/f/4N+zS5g9Pc6U4edWHZ7k6Y3Yr8Z9+lS5cIDw+nQ4cOXL16lcGDB+e+QI0mn9OxY0fCwsKoUaMGzz//PBs3bgRg4MCB6XFztm3bRrFixW4YcNG3b9/0SJt//vkn3bt3z33xTo7TdBYPesLER6oS1DVeQbdu3crYsWNZvXo17dq14/z585QtW9bBKjUa25ApTD/u7jfaPj432v7+N9rFi99oly6dszp9fX3ZvXs3mzZtYv369fTv359JkybRv39/WrRoweTJk//TLAQQEBBA0aJFWbBgAbVr18bHxydnFWpshl3fCESks4gcE5GTIvKf7BMi4ikiv1j3bxeRyvbS4mIyIhaaXTwYMGAALVq0YO3ataxfvx5AOwGNxga4urrSpk0bJkyYwFdffcWiRYuoUKECVapUYePGjSxatCjLGcX9+/fnhRde0M1CDsJubwQi4gp8DXQAzgM7RWSpUupwpmIjgGtKqftEZADwEWCXeec71sbz7qLPWf1ZBWJjr1KsWDGWLFlCq1at7FGdRuN0HDt2DBcXl/Rmn5CQECpVqgQYzUP/+9//qFq1apYj8Hr37s3Fixfp1KkTFy5cyFXdGvu+EQQDJ5VSp5VSJmAB0POmMj2B2db1hUA7kXsIBJQNJ48Lvx/qTmxsIiNGjCAiIkI7AY3GhsTFxaWHbA4MDOTw4cOMHz8egEceeYRDhw7d8onfz8+P119/vUAGdMsP2LOPoBwQlsk+DzS7VRmllFlEYoAA4GrmQiLyNPA0cNdx/v/vNR8Kq1F0GbL5hryjGo3GNjRp0oR///03y33FixcnJSXlP9vPnj37n22VK1dOz/GryR3yRWexUuo74DuAoKCgu8rNFlCqAm989rNNdWk0Gk1BwJ5NQ+FAhUx2eeu2LMuIiBvgD0TaUZNGo9FobsKejmAnUF1EqoiIBzAAWHpTmaVAWmb3fsDf1kw6Go3mDtH/Ohq4u9+B3RyBUsoMvAisBo4AvyqlDonIuyLSw1rsByBARE4Co4D/DDHVaDS3x8vLi8jISO0MnBylFJGRkXh5ed3RcZLffjhBQUHKGTIGaTR3QkpKCufPnycpKcnRUjQOxsvLi/Lly+PufmM4HRHZrZQKyuqYfNFZrNFossfd3Z0qVao4WoYmn+I0sYY0Go1GkzXaEWg0Go2Tox2BRqPRODn5rrNYRCKA0Ls8vDg3zVp2AvQ1Owf6mp2De7nmSkqpElntyHeO4F4QkV236jUvqOhrdg70NTsH9rpm3TSk0Wg0To52BBqNRuPkOJsj+M7RAhyAvmbnQF+zc2CXa3aqPgKNRqPR/BdneyPQaDQazU1oR6DRaDROToF0BCLSWUSOichJEflPRFMR8RSRX6z7t4tIZQfItCk5uOZRInJYRPaLyDoRqeQInbbkdtecqVxfEVEiku+HGubkmkXkUevf+pCIzMttjbYmB7/tiiKyXkT2Wn/fXR2h01aIyI8ickVEskzTJgZTrN/HfhFpfM+VKqUK1AK4AqeAqoAHsA+oc1OZ54Fp1vUBwC+O1p0L19wW8LGuP+cM12wt5wf8A2wDghytOxf+ztWBvUBRq13S0bpz4Zq/A56zrtcBzjpa9z1ec2ugMXDwFvu7AisBAe4Htt9rnQXxjSAYOKmUOq2UMgELgJ43lekJzLauLwTaiYjkokZbc9trVkqtV0olWM1tGBnj8jM5+TsDvAd8BBSE+Mw5ueangK+VUtcAlFJXclmjrcnJNSugsHXdH7iQi/psjlLqHyAqmyI9gZ+UwTagiIiUuZc6C6IjKAeEZbLPW7dlWUYZCXRigIBcUWcfcnLNmRmB8USRn7ntNVtfmSsopZbnpjA7kpO/cw2ghohsEZFtItI519TZh5xc83jgMRE5D6wAXsodaQ7jTv/fb4vOR+BkiMhjQBDwoKO12BMRcQE+A4Y5WEpu44bRPNQG463vHxGpr5SKdqQoOzMQmKWUmiwizYGfRaSeUsriaGH5hYL4RhAOVMhkl7duy7KMiLhhvE5G5oo6+5CTa0ZE2gNvAT2UUsm5pM1e3O6a/YB6wAYROYvRlro0n3cY5+TvfB5YqpRKUUqdAY5jOIb8Sk6ueQTwK4BSaivghRGcraCSo//3O6EgOoKdQHURqSIiHhidwUtvKrMUGGpd7wf8ray9MPmU216ziDQCpmM4gfzebgy3uWalVIxSqrhSqrJSqjJGv0gPpVR+znOak9/2HxhvA4hIcYymotO5qNHW5OSazwHtAESkNoYjiMhVlbnLUmCIdfTQ/UCMUurivZywwDUNKaXMIvIisBpjxMGPSqlDIvIusEsptRT4AeP18SRGp8wAxym+d3J4zZ8AvsBv1n7xc0qpHg4TfY/k8JoLFDm85tVARxE5DKQCo5VS+fZtN4fX/CrwvYj8D6PjeFh+frATkfkYzry4td9jHOAOoJSahtEP0hU4CSQAw++5znz8fWk0Go3GBhTEpiGNRqPR3AHaEWg0Go2Tox2BRqPRODnaEWg0Go2Tox2BRqPRODnaEWjyJCKSKiIhmZbK2ZSNs0F9s0TkjLWuPdYZqnd6jhkiUse6/uZN+/69V43W86R9LwdF5E8RKXKb8g3zezROjf3Rw0c1eRIRiVNK+dq6bDbnmAUsU0otFJGOwKdKqcB7ON89a7rdeUVkNnBcKfVBNuWHYURdfdHWWjQFB/1GoMkXiIivNY/CHhE5ICL/iTQqImVE5J9MT8wPWLd3FJGt1mN/E5Hb3aD/Ae6zHjvKeq6DIvKKdVshEVkuIvus2/tbt28QkSARmQR4W3XMte6Ls34uEJGHM2meJSL9RMRVRD4RkZ3WGPPP5OBr2Yo12JiIBFuvca+I/CsiNa0zcd8F+lu19Ldq/1FEdljLZhWxVeNsODr2tl70ktWCMSs2xLosxpgFX9i6rzjGrMq0N9o46+erwFvWdVeMeEPFMW7shazbXwfGZlHfLKCfdf0RYDvQBDgAFMKYlX0IaAT0Bb7PdKy/9XMD1pwHaZoylUnT2BuYbV33wIgi6Q08Dbxt3e4J7AKqZKEzLtP1/QZ0ttqFATfrentgkXV9GPBVpuM/BB6zrhfBiEVUyNF/b704dilwISY0BYZEpVTDNENE3IEPRaQ1YMF4Ei4FXMp0zE7gR2vZP5RSISLyIEayki3W0BoeGE/SWfGJiLyNEadmBEb8msVKqXirht+BB4BVwGQR+QijOWnTHVzXSuBLEfEEOgP/KKUSrc1RgSLSz1rOHyNY3JmbjvcWkRDr9R8B1mQqP1tEqmOEWXC/Rf0dgR4i8n9W2wuoaD2XxknRjkCTXxgMlACaKKVSxIgo6pW5gFLqH6ujeBiYJSKfAdeANUqpgTmoY7RSamGaISLtsiqklDouRq6DrsD7IrJOKfVuTi5CKZUkIhuATkB/jEQrYGSbekkptfo2p0hUSjUUER+M+DsvAFMwEvCsV0r1tnasb7jF8QL0VUody4lejXOg+wg0+QV/4IrVCbQF/pNzWYw8zJeVUt8DMzDS/W0DWopIWpt/IRGpkcM6NwG9RMRHRAphNOtsEpGyQIJSag5GML+scsamWN9MsuIXjEBhaW8XYNzUn0s7RkRqWOvMEmVkm3sZeFUyQqmnhSIelqloLEYTWRqrgZfE+nokRlRajZOjHYEmvzAXCBKRA8AQ4GgWZdoA+0RkL8bT9pdKqQiMG+N8EdmP0SxUKycVKqX2YPQd7MDoM5ihlNoL1Ad2WJtoxgHvZ3H4d8D+tM7im/gLIzHQWmWkXwTDcR0G9oiRtHw6t3ljt2rZj5GY5WNgovXaMx+3HqiT1lmM8ebgbtV2yGprnBw9fFSj0WicHP1GoNFoNE6OdgQajUbj5GhHoNFoNE6OdgQajUbj5GhHoNFoNE6OdgQajUbj5GhHoNFoNE7O/wOqHwd6HiZ0gQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import roc_curve, roc_auc_score\n",
    "from sklearn.model_selection import cross_val_predict\n",
    "\n",
    "# Random Forest\n",
    "y_probas_forest = cross_val_predict(forest_best, X_train, y_train, cv=kf, method=\"predict_proba\")\n",
    "y_scores_forest = y_probas_forest[:, 1] # score = proba of positive class\n",
    "fpr_forest, tpr_forest, thresholds_forest = roc_curve(y_train, y_scores_forest)\n",
    "\n",
    "# Logistic Regression\n",
    "y_probas_logistic = cross_val_predict(logistic_best, X_train, y_train, cv=kf, method=\"predict_proba\")\n",
    "y_scores_logistic = y_probas_logistic[:, 1] # score = proba of positive class\n",
    "fpr_logistic, tpr_logistic, thresholds_logistic = roc_curve(y_train, y_scores_logistic)\n",
    "\n",
    "# SVM\n",
    "y_scores_svm = svm_best.decision_function(X_train)\n",
    "fpr_svm, tpr_svm, thresholds_svm = roc_curve(y_train, y_scores_svm)\n",
    "\n",
    "\n",
    "def plot_roc_curve(fpr, tpr, label=None): \n",
    "    plt.plot(fpr, tpr, linewidth=1, label=label)\n",
    "    plt.plot([0, 1], [0, 1], 'k--') # dashed diagonal\n",
    "    plt.xlabel('False Positive Rate')\n",
    "    plt.ylabel('True Positive Rate')\n",
    "\n",
    "    \n",
    "plot_roc_curve(fpr_forest, tpr_forest, \"Random Forest\") # Random Forest Plot\n",
    "plot_roc_curve(fpr_logistic, tpr_logistic, \"Logistic Regression\") # Random Forest Plot\n",
    "plt.plot(fpr_svm, tpr_svm, \"b:\", label=\"SVM\") #SVM Plot\n",
    "plt.title(\"ROC Curve\")\n",
    "plt.legend(loc=\"lower right\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b5539d5b",
   "metadata": {},
   "source": [
    "## ROC Scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "bf77c6eb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Random Forest ROC AUC: 0.907343606044966\n",
      "Logistic Regression ROC AUC: 0.8003613928145916\n",
      "SVM ROC AUC: 0.9992092701787705\n"
     ]
    }
   ],
   "source": [
    "# Random Forest\n",
    "roc_auc_forest = roc_auc_score(y_train, y_scores_forest)\n",
    "\n",
    "# Logistic Regression\n",
    "roc_auc_logistic = roc_auc_score(y_train, y_scores_logistic)\n",
    "\n",
    "# SVM\n",
    "roc_auc_svm = roc_auc_score(y_train, y_scores_svm)\n",
    "\n",
    "print(f\"Random Forest ROC AUC: {roc_auc_forest}\")\n",
    "print(f\"Logistic Regression ROC AUC: {roc_auc_logistic}\")\n",
    "print(f\"SVM ROC AUC: {roc_auc_svm}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b1d5a5ab",
   "metadata": {},
   "source": [
    "## Test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "0acce282",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test = pipeline.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "55232688",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "============= Test Set Score ==========\n",
      "Accuracy: 0.909375\n",
      "Precision: 0.9708029197080292\n",
      "Recall: 0.4854014598540146\n",
      "F1 Score: 0.6472019464720195\n"
     ]
    }
   ],
   "source": [
    "# Random Forest\n",
    "rf_y_test_pred = forest_best.predict(X_test)\n",
    "score_check(y_test, rf_y_test_pred, 'Test')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "4d8a0a79",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "============= Test Set Score ==========\n",
      "Accuracy: 0.844375\n",
      "Precision: 0.6068376068376068\n",
      "Recall: 0.2591240875912409\n",
      "F1 Score: 0.3631713554987212\n"
     ]
    }
   ],
   "source": [
    "# Logistic regression\n",
    "logistic_y_test_pred = logistic_best.predict(X_test)\n",
    "score_check(y_test, logistic_y_test_pred, 'Test')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "153d6e90",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "============= Test Set Score ==========\n",
      "Accuracy: 0.958125\n",
      "Precision: 0.9156626506024096\n",
      "Recall: 0.8321167883211679\n",
      "F1 Score: 0.8718929254302102\n"
     ]
    }
   ],
   "source": [
    "# SVM\n",
    "svm_y_test_pred = svm_best.predict(X_test)\n",
    "score_check(y_test, svm_y_test_pred, 'Test')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "17b8e585",
   "metadata": {},
   "source": [
    "We observed SVM model has the best performance."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fe225229",
   "metadata": {},
   "source": [
    "## Confusion Matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "64e9ec6a",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "import seaborn as sns;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "16a30ba4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Random Forest\n",
    "\n",
    "cf_matrix = confusion_matrix(y_test, rf_y_test_pred)\n",
    "sns.heatmap(cf_matrix, annot=True, fmt='.4g');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "f9ca1f1f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Logistic Regression\n",
    "\n",
    "cf_matrix = confusion_matrix(y_test, logistic_y_test_pred)\n",
    "sns.heatmap(cf_matrix, annot=True, fmt='.4g');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "fa5789f0",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[CV] END bootstrap=False, max_depth=21, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=10; total time=   0.4s\n",
      "[CV] END bootstrap=False, max_depth=10, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=50; total time=   1.7s\n",
      "[CV] END bootstrap=False, max_depth=32, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=30; total time=   1.3s\n",
      "[CV] END bootstrap=True, max_depth=15, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=40; total time=   1.1s\n",
      "[CV] END bootstrap=True, max_depth=54, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=70; total time=   2.1s\n",
      "[CV] END bootstrap=False, max_depth=15, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=70; total time=   3.9s\n",
      "[CV] END bootstrap=True, max_depth=54, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=20; total time=   0.6s\n",
      "[CV] END bootstrap=True, max_depth=54, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=40; total time=   1.4s\n",
      "[CV] END bootstrap=True, max_depth=26, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time=   3.5s\n",
      "[CV] END bootstrap=True, max_depth=43, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=70; total time=   2.5s\n",
      "[CV] END bootstrap=True, max_depth=32, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=30; total time=   1.1s\n",
      "[CV] END bootstrap=False, max_depth=21, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=60; total time=   3.7s\n",
      "[CV] END bootstrap=True, max_depth=21, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=50; total time=   1.7s\n",
      "[CV] END bootstrap=True, max_depth=21, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=50; total time=   1.9s\n",
      "[CV] END bootstrap=False, max_depth=10, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=60; total time=   2.8s\n",
      "[CV] END bootstrap=False, max_depth=43, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=80; total time=   4.7s\n",
      "[CV] END bootstrap=False, max_depth=43, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=70; total time=   3.6s\n",
      "[CV] END bootstrap=False, max_depth=15, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=20; total time=   1.1s\n",
      "[CV] END bootstrap=False, max_depth=60, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=30; total time=   1.7s\n",
      "[CV] END bootstrap=False, max_depth=54, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=90; total time=   5.2s\n",
      "[CV] END bootstrap=True, max_depth=37, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=90; total time=   2.9s\n",
      "[CV] END bootstrap=False, max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=40; total time=   2.3s\n",
      "[CV] END bootstrap=False, max_depth=37, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=70; total time=   4.2s\n",
      "[CV] END bootstrap=True, max_depth=37, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=70; total time=   2.2s\n",
      "[CV] END bootstrap=False, max_depth=43, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time=   5.8s\n",
      "[CV] END bootstrap=True, max_depth=43, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=50; total time=   1.6s\n",
      "[CV] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=50; total time=   1.3s\n",
      "[CV] END bootstrap=False, max_depth=26, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time=   5.6s\n",
      "[CV] END bootstrap=False, max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=60; total time=   3.4s\n",
      "[CV] END bootstrap=False, max_depth=21, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=50; total time=   2.7s\n",
      "[CV] END bootstrap=False, max_depth=43, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=30; total time=   1.7s\n",
      "[CV] END bootstrap=False, max_depth=21, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=40; total time=   2.2s\n",
      "[CV] END bootstrap=True, max_depth=15, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=60; total time=   1.9s\n",
      "[CV] END bootstrap=False, max_depth=54, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=30; total time=   1.5s\n",
      "[CV] END bootstrap=True, max_depth=37, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=40; total time=   1.4s\n",
      "[CV] END bootstrap=False, max_depth=37, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=70; total time=   3.9s\n",
      "[CV] END bootstrap=True, max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=90; total time=   2.3s\n",
      "[CV] END bootstrap=False, max_depth=32, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=30; total time=   1.4s\n",
      "[CV] END bootstrap=True, max_depth=32, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=20; total time=   0.5s\n",
      "[CV] END bootstrap=True, max_depth=32, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=20; total time=   0.6s\n",
      "[CV] END bootstrap=True, max_depth=37, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=10; total time=   0.3s\n",
      "[CV] END bootstrap=True, max_depth=15, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=40; total time=   1.1s\n",
      "[CV] END bootstrap=False, max_depth=None, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=90; total time=   4.7s\n",
      "[CV] END bootstrap=True, max_depth=54, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=40; total time=   1.3s\n",
      "[CV] END bootstrap=False, max_depth=37, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=90; total time=   5.3s\n",
      "[CV] END bootstrap=False, max_depth=48, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time=   6.1s\n",
      "[CV] END bootstrap=True, max_depth=21, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=50; total time=   1.7s\n",
      "[CV] END bootstrap=False, max_depth=10, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=60; total time=   2.8s\n",
      "[CV] END bootstrap=False, max_depth=15, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=90; total time=   5.1s\n",
      "[CV] END bootstrap=False, max_depth=37, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=60; total time=   3.1s\n",
      "[CV] END bootstrap=False, max_depth=15, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=80; total time=   4.3s\n",
      "[CV] END bootstrap=False, max_depth=54, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=90; total time=   5.0s\n",
      "[CV] END bootstrap=True, max_depth=32, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=70; total time=   2.5s\n",
      "[CV] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=40; total time=   1.7s\n",
      "[CV] END bootstrap=False, max_depth=43, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=20; total time=   1.0s\n",
      "[CV] END bootstrap=False, max_depth=37, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=70; total time=   4.0s\n",
      "[CV] END bootstrap=False, max_depth=43, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=90; total time=   5.0s\n",
      "[CV] END bootstrap=True, max_depth=48, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=60; total time=   2.1s\n",
      "[CV] END bootstrap=True, max_depth=48, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=60; total time=   2.2s\n",
      "[CV] END bootstrap=False, max_depth=60, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=10; total time=   0.5s\n",
      "[CV] END bootstrap=True, max_depth=21, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time=   3.1s\n",
      "[CV] END bootstrap=False, max_depth=37, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=40; total time=   2.4s\n",
      "[CV] END bootstrap=False, max_depth=15, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=20; total time=   1.0s\n",
      "[CV] END bootstrap=False, max_depth=48, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time=   6.1s\n",
      "[CV] END bootstrap=False, max_depth=48, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=40; total time=   2.3s\n",
      "[CV] END bootstrap=False, max_depth=15, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=30; total time=   1.6s\n",
      "[CV] END bootstrap=False, max_depth=15, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=60; total time=   3.2s\n",
      "[CV] END bootstrap=False, max_depth=54, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=30; total time=   1.5s\n",
      "[CV] END bootstrap=True, max_depth=37, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=40; total time=   1.5s\n",
      "[CV] END bootstrap=False, max_depth=37, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=70; total time=   3.9s\n",
      "[CV] END bootstrap=False, max_depth=21, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=10; total time=   0.4s\n",
      "[CV] END bootstrap=False, max_depth=54, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=30; total time=   1.3s\n",
      "[CV] END bootstrap=False, max_depth=37, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=40; total time=   1.9s\n",
      "[CV] END bootstrap=False, max_depth=26, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=60; total time=   2.7s\n",
      "[CV] END bootstrap=False, max_depth=None, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=90; total time=   4.9s\n",
      "[CV] END bootstrap=False, max_depth=37, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=90; total time=   5.0s\n",
      "[CV] END bootstrap=True, max_depth=43, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=70; total time=   2.4s\n",
      "[CV] END bootstrap=True, max_depth=32, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=30; total time=   1.1s\n",
      "[CV] END bootstrap=True, max_depth=37, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=30; total time=   1.1s\n",
      "[CV] END bootstrap=True, max_depth=37, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=30; total time=   1.1s\n",
      "[CV] END bootstrap=False, max_depth=15, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=90; total time=   4.7s\n",
      "[CV] END bootstrap=False, max_depth=10, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=60; total time=   2.9s\n",
      "[CV] END bootstrap=False, max_depth=43, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=80; total time=   4.6s\n",
      "[CV] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=90; total time=   3.9s\n",
      "[CV] END bootstrap=False, max_depth=15, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=20; total time=   1.1s\n",
      "[CV] END bootstrap=True, max_depth=43, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=60; total time=   2.3s\n",
      "[CV] END bootstrap=True, max_depth=21, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=10; total time=   0.3s\n",
      "[CV] END bootstrap=True, max_depth=32, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=50; total time=   1.7s\n",
      "[CV] END bootstrap=False, max_depth=48, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=30; total time=   1.7s\n",
      "[CV] END bootstrap=True, max_depth=37, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=60; total time=   2.0s\n",
      "[CV] END bootstrap=True, max_depth=37, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=70; total time=   2.4s\n",
      "[CV] END bootstrap=False, max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=40; total time=   2.3s\n",
      "[CV] END bootstrap=True, max_depth=21, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=30; total time=   1.0s\n",
      "[CV] END bootstrap=True, max_depth=21, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=30; total time=   1.1s\n",
      "[CV] END bootstrap=True, max_depth=26, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time=   3.4s\n",
      "[CV] END bootstrap=False, max_depth=43, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time=   5.5s\n",
      "[CV] END bootstrap=True, max_depth=43, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=40; total time=   1.4s\n",
      "[CV] END bootstrap=False, max_depth=60, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=10; total time=   0.5s\n",
      "[CV] END bootstrap=True, max_depth=21, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time=   3.2s\n",
      "[CV] END bootstrap=True, max_depth=15, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=80; total time=   2.6s\n",
      "[CV] END bootstrap=False, max_depth=15, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=20; total time=   1.0s\n",
      "[CV] END bootstrap=False, max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=60; total time=   3.3s\n",
      "[CV] END bootstrap=False, max_depth=37, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=80; total time=   4.8s\n",
      "[CV] END bootstrap=False, max_depth=21, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=40; total time=   2.2s\n",
      "[CV] END bootstrap=False, max_depth=15, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=60; total time=   3.2s\n",
      "[CV] END bootstrap=True, max_depth=37, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=40; total time=   1.4s\n",
      "[CV] END bootstrap=True, max_depth=37, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=50; total time=   1.7s\n",
      "[CV] END bootstrap=True, max_depth=32, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=10; total time=   0.3s\n",
      "[CV] END bootstrap=False, max_depth=32, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=20; total time=   1.0s\n",
      "[CV] END bootstrap=False, max_depth=32, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=20; total time=   1.1s\n",
      "[CV] END bootstrap=True, max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=90; total time=   2.4s\n",
      "[CV] END bootstrap=True, max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=20; total time=   0.5s\n",
      "[CV] END bootstrap=True, max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=20; total time=   0.5s\n",
      "[CV] END bootstrap=True, max_depth=15, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=40; total time=   1.1s\n",
      "[CV] END bootstrap=True, max_depth=54, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=70; total time=   2.2s\n",
      "[CV] END bootstrap=True, max_depth=15, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=30; total time=   0.9s\n",
      "[CV] END bootstrap=True, max_depth=15, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=30; total time=   1.0s\n",
      "[CV] END bootstrap=True, max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=20; total time=   0.7s\n",
      "[CV] END bootstrap=True, max_depth=26, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time=   3.5s\n",
      "[CV] END bootstrap=True, max_depth=26, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time=   3.7s\n",
      "[CV] END bootstrap=False, max_depth=48, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time=   5.9s\n",
      "[CV] END bootstrap=False, max_depth=26, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=80; total time=   4.8s\n",
      "[CV] END bootstrap=True, max_depth=32, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=50; total time=   2.1s\n",
      "[CV] END bootstrap=True, max_depth=60, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=40; total time=   1.5s\n",
      "[CV] END bootstrap=True, max_depth=10, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=50; total time=   1.4s\n",
      "[CV] END bootstrap=True, max_depth=43, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=10; total time=   0.4s\n",
      "[CV] END bootstrap=True, max_depth=54, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=10; total time=   0.4s\n",
      "[CV] END bootstrap=False, max_depth=37, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=60; total time=   3.2s\n",
      "[CV] END bootstrap=False, max_depth=43, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=70; total time=   3.8s\n",
      "[CV] END bootstrap=False, max_depth=60, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=30; total time=   1.8s\n",
      "[CV] END bootstrap=True, max_depth=48, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=60; total time=   2.2s\n",
      "[CV] END bootstrap=False, max_depth=48, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=30; total time=   1.8s\n",
      "[CV] END bootstrap=True, max_depth=37, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=90; total time=   2.9s\n",
      "[CV] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=40; total time=   1.7s\n",
      "[CV] END bootstrap=False, max_depth=None, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=80; total time=   4.2s\n",
      "[CV] END bootstrap=False, max_depth=43, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=90; total time=   5.1s\n",
      "[CV] END bootstrap=False, max_depth=10, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time=   4.2s\n",
      "[CV] END bootstrap=False, max_depth=54, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=40; total time=   2.1s\n",
      "[CV] END bootstrap=False, max_depth=None, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=20; total time=   1.0s\n",
      "[CV] END bootstrap=False, max_depth=37, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=40; total time=   2.3s\n",
      "[CV] END bootstrap=True, max_depth=15, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=80; total time=   2.6s\n",
      "[CV] END bootstrap=False, max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=60; total time=   3.3s\n",
      "[CV] END bootstrap=False, max_depth=21, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=50; total time=   2.7s\n",
      "[CV] END bootstrap=False, max_depth=15, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=10; total time=   0.5s\n",
      "[CV] END bootstrap=False, max_depth=43, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=30; total time=   1.7s\n",
      "[CV] END bootstrap=False, max_depth=10, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=90; total time=   3.7s\n",
      "[CV] END bootstrap=False, max_depth=15, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=70; total time=   3.7s\n",
      "[CV] END bootstrap=True, max_depth=54, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=90; total time=   3.1s\n",
      "[CV] END bootstrap=False, max_depth=21, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=10; total time=   0.4s\n",
      "[CV] END bootstrap=False, max_depth=54, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=30; total time=   1.3s\n",
      "[CV] END bootstrap=False, max_depth=37, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=40; total time=   1.8s\n",
      "[CV] END bootstrap=True, max_depth=15, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=40; total time=   1.1s\n",
      "[CV] END bootstrap=True, max_depth=37, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=10; total time=   0.3s\n",
      "[CV] END bootstrap=True, max_depth=37, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=10; total time=   0.3s\n",
      "[CV] END bootstrap=True, max_depth=15, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=40; total time=   1.1s\n",
      "[CV] END bootstrap=False, max_depth=15, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=70; total time=   3.8s\n",
      "[CV] END bootstrap=True, max_depth=26, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time=   3.6s\n",
      "[CV] END bootstrap=False, max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=70; total time=   3.8s\n",
      "[CV] END bootstrap=True, max_depth=32, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=30; total time=   1.2s\n",
      "[CV] END bootstrap=False, max_depth=21, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=60; total time=   3.4s\n",
      "[CV] END bootstrap=False, max_depth=15, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=90; total time=   4.8s\n",
      "[CV] END bootstrap=True, max_depth=32, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=50; total time=   2.0s\n",
      "[CV] END bootstrap=True, max_depth=60, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=40; total time=   1.5s\n",
      "[CV] END bootstrap=False, max_depth=43, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=80; total time=   4.8s\n",
      "[CV] END bootstrap=False, max_depth=43, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=70; total time=   3.7s\n",
      "[CV] END bootstrap=True, max_depth=43, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=60; total time=   2.1s\n",
      "[CV] END bootstrap=True, max_depth=21, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=10; total time=   0.3s\n",
      "[CV] END bootstrap=True, max_depth=21, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=10; total time=   0.3s\n",
      "[CV] END bootstrap=True, max_depth=32, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=50; total time=   1.8s\n",
      "[CV] END bootstrap=False, max_depth=48, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=30; total time=   1.7s\n",
      "[CV] END bootstrap=True, max_depth=37, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=60; total time=   2.1s\n",
      "[CV] END bootstrap=True, max_depth=37, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=70; total time=   2.3s\n",
      "[CV] END bootstrap=False, max_depth=None, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=40; total time=   2.5s\n",
      "[CV] END bootstrap=True, max_depth=21, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=30; total time=   1.1s\n",
      "[CV] END bootstrap=True, max_depth=26, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time=   3.5s\n",
      "[CV] END bootstrap=True, max_depth=37, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=70; total time=   2.3s\n",
      "[CV] END bootstrap=False, max_depth=10, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time=   4.2s\n",
      "[CV] END bootstrap=True, max_depth=43, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=50; total time=   1.6s\n",
      "[CV] END bootstrap=True, max_depth=21, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=100; total time=   3.3s\n",
      "[CV] END bootstrap=True, max_depth=15, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=80; total time=   2.6s\n",
      "[CV] END bootstrap=False, max_depth=48, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time=   5.8s\n",
      "[CV] END bootstrap=False, max_depth=48, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=40; total time=   2.3s\n",
      "[CV] END bootstrap=False, max_depth=15, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=30; total time=   1.5s\n",
      "[CV] END bootstrap=False, max_depth=10, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=90; total time=   3.7s\n",
      "[CV] END bootstrap=False, max_depth=15, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=70; total time=   3.7s\n",
      "[CV] END bootstrap=True, max_depth=54, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=90; total time=   3.2s\n",
      "[CV] END bootstrap=False, max_depth=10, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=50; total time=   1.6s\n",
      "[CV] END bootstrap=False, max_depth=54, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=30; total time=   1.4s\n",
      "[CV] END bootstrap=True, max_depth=None, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=20; total time=   0.5s\n",
      "[CV] END bootstrap=False, max_depth=26, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=60; total time=   2.6s\n",
      "[CV] END bootstrap=True, max_depth=15, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=40; total time=   1.2s\n",
      "[CV] END bootstrap=True, max_depth=15, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=30; total time=   0.9s\n",
      "[CV] END bootstrap=True, max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=20; total time=   0.7s\n",
      "[CV] END bootstrap=True, max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=20; total time=   0.8s\n",
      "[CV] END bootstrap=True, max_depth=26, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time=   3.5s\n",
      "[CV] END bootstrap=False, max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=70; total time=   3.8s\n",
      "[CV] END bootstrap=False, max_depth=48, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time=   5.7s\n",
      "[CV] END bootstrap=False, max_depth=26, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=80; total time=   5.2s\n",
      "[CV] END bootstrap=False, max_depth=15, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=90; total time=   5.1s\n",
      "[CV] END bootstrap=True, max_depth=54, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=10; total time=   0.4s\n",
      "[CV] END bootstrap=False, max_depth=37, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=60; total time=   3.3s\n",
      "[CV] END bootstrap=False, max_depth=15, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=80; total time=   4.3s\n",
      "[CV] END bootstrap=False, max_depth=54, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=90; total time=   5.0s\n",
      "[CV] END bootstrap=True, max_depth=32, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=70; total time=   2.5s\n",
      "[CV] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=40; total time=   1.7s\n",
      "[CV] END bootstrap=False, max_depth=43, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=20; total time=   1.1s\n",
      "[CV] END bootstrap=False, max_depth=37, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=70; total time=   3.9s\n",
      "[CV] END bootstrap=False, max_depth=43, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=90; total time=   5.2s\n",
      "[CV] END bootstrap=True, max_depth=48, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=60; total time=   2.0s\n",
      "[CV] END bootstrap=True, max_depth=43, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=40; total time=   1.3s\n",
      "[CV] END bootstrap=False, max_depth=54, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=40; total time=   2.0s\n",
      "[CV] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=50; total time=   1.4s\n",
      "[CV] END bootstrap=False, max_depth=26, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time=   5.7s\n",
      "[CV] END bootstrap=True, max_depth=15, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=10; total time=   0.3s\n",
      "[CV] END bootstrap=False, max_depth=37, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=80; total time=   4.6s\n",
      "[CV] END bootstrap=False, max_depth=15, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=10; total time=   0.5s\n",
      "[CV] END bootstrap=False, max_depth=15, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=10; total time=   0.5s\n",
      "[CV] END bootstrap=False, max_depth=43, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=30; total time=   1.8s\n",
      "[CV] END bootstrap=False, max_depth=10, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=90; total time=   3.8s\n",
      "[CV] END bootstrap=False, max_depth=54, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=30; total time=   1.6s\n",
      "[CV] END bootstrap=True, max_depth=37, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=50; total time=   1.6s\n",
      "[CV] END bootstrap=True, max_depth=54, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=90; total time=   3.2s\n",
      "[CV] END bootstrap=False, max_depth=10, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=50; total time=   1.6s\n",
      "[CV] END bootstrap=False, max_depth=37, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=40; total time=   1.9s\n",
      "[CV] END bootstrap=False, max_depth=26, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=60; total time=   2.7s\n",
      "[CV] END bootstrap=False, max_depth=None, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=90; total time=   4.7s\n",
      "[CV] END bootstrap=True, max_depth=54, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=20; total time=   0.6s\n",
      "[CV] END bootstrap=False, max_depth=37, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=90; total time=   4.9s\n",
      "[CV] END bootstrap=True, max_depth=43, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=70; total time=   2.5s\n",
      "[CV] END bootstrap=False, max_depth=21, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=60; total time=   3.4s\n",
      "[CV] END bootstrap=False, max_depth=15, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=90; total time=   5.1s\n",
      "[CV] END bootstrap=False, max_depth=15, max_features=sqrt, min_samples_leaf=2, min_samples_split=2, n_estimators=90; total time=   5.0s\n",
      "[CV] END bootstrap=True, max_depth=43, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=10; total time=   0.4s\n",
      "[CV] END bootstrap=True, max_depth=43, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=10; total time=   0.4s\n",
      "[CV] END bootstrap=True, max_depth=54, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=10; total time=   0.4s\n",
      "[CV] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=90; total time=   3.8s\n",
      "[CV] END bootstrap=False, max_depth=15, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=20; total time=   1.1s\n",
      "[CV] END bootstrap=True, max_depth=43, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=60; total time=   2.2s\n",
      "[CV] END bootstrap=False, max_depth=60, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=30; total time=   1.8s\n",
      "[CV] END bootstrap=True, max_depth=48, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=60; total time=   2.2s\n",
      "[CV] END bootstrap=True, max_depth=37, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=60; total time=   2.0s\n",
      "[CV] END bootstrap=True, max_depth=37, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=90; total time=   3.0s\n",
      "[CV] END bootstrap=False, max_depth=43, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=20; total time=   1.0s\n",
      "[CV] END bootstrap=False, max_depth=None, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=80; total time=   4.4s\n",
      "[CV] END bootstrap=True, max_depth=37, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=70; total time=   2.2s\n",
      "[CV] END bootstrap=False, max_depth=43, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time=   5.6s\n",
      "[CV] END bootstrap=True, max_depth=43, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=50; total time=   1.6s\n",
      "[CV] END bootstrap=False, max_depth=60, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=10; total time=   0.5s\n",
      "[CV] END bootstrap=True, max_depth=10, max_features=sqrt, min_samples_leaf=1, min_samples_split=10, n_estimators=50; total time=   1.3s\n",
      "[CV] END bootstrap=False, max_depth=None, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=20; total time=   1.1s\n",
      "[CV] END bootstrap=False, max_depth=37, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=40; total time=   2.3s\n",
      "[CV] END bootstrap=False, max_depth=15, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=20; total time=   1.0s\n",
      "[CV] END bootstrap=False, max_depth=48, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=100; total time=   5.7s\n",
      "[CV] END bootstrap=False, max_depth=21, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=50; total time=   2.8s\n",
      "[CV] END bootstrap=False, max_depth=15, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=30; total time=   1.6s\n",
      "[CV] END bootstrap=False, max_depth=15, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=60; total time=   3.2s\n",
      "[CV] END bootstrap=False, max_depth=15, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=70; total time=   3.8s\n",
      "[CV] END bootstrap=True, max_depth=32, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=10; total time=   0.4s\n",
      "[CV] END bootstrap=True, max_depth=32, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=10; total time=   0.4s\n",
      "[CV] END bootstrap=False, max_depth=32, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=20; total time=   1.0s\n",
      "[CV] END bootstrap=True, max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=90; total time=   2.3s\n",
      "[CV] END bootstrap=False, max_depth=32, max_features=auto, min_samples_leaf=1, min_samples_split=5, n_estimators=30; total time=   1.6s\n",
      "[CV] END bootstrap=True, max_depth=32, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=20; total time=   0.5s\n",
      "[CV] END bootstrap=True, max_depth=54, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=70; total time=   2.2s\n",
      "[CV] END bootstrap=False, max_depth=15, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=70; total time=   3.7s\n",
      "[CV] END bootstrap=True, max_depth=54, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=20; total time=   0.6s\n",
      "[CV] END bootstrap=True, max_depth=54, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=40; total time=   1.4s\n",
      "[CV] END bootstrap=True, max_depth=26, max_features=auto, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time=   3.4s\n",
      "[CV] END bootstrap=False, max_depth=None, max_features=sqrt, min_samples_leaf=2, min_samples_split=10, n_estimators=70; total time=   4.0s\n",
      "[CV] END bootstrap=True, max_depth=37, max_features=auto, min_samples_leaf=1, min_samples_split=10, n_estimators=30; total time=   1.1s\n",
      "[CV] END bootstrap=False, max_depth=48, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=10; total time=   0.5s\n",
      "[CV] END bootstrap=False, max_depth=48, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=10; total time=   0.5s\n",
      "[CV] END bootstrap=False, max_depth=48, max_features=sqrt, min_samples_leaf=4, min_samples_split=5, n_estimators=10; total time=   0.5s\n",
      "[CV] END bootstrap=False, max_depth=26, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=80; total time=   4.7s\n",
      "[CV] END bootstrap=True, max_depth=32, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=50; total time=   1.9s\n",
      "[CV] END bootstrap=True, max_depth=60, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=40; total time=   1.4s\n",
      "[CV] END bootstrap=True, max_depth=10, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=50; total time=   1.4s\n",
      "[CV] END bootstrap=True, max_depth=10, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=50; total time=   1.4s\n",
      "[CV] END bootstrap=False, max_depth=10, max_features=sqrt, min_samples_leaf=4, min_samples_split=10, n_estimators=90; total time=   3.8s\n",
      "[CV] END bootstrap=False, max_depth=15, max_features=sqrt, min_samples_leaf=1, min_samples_split=2, n_estimators=80; total time=   4.4s\n",
      "[CV] END bootstrap=True, max_depth=32, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=50; total time=   1.7s\n",
      "[CV] END bootstrap=True, max_depth=48, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=60; total time=   2.3s\n",
      "[CV] END bootstrap=True, max_depth=32, max_features=auto, min_samples_leaf=2, min_samples_split=10, n_estimators=70; total time=   2.5s\n",
      "[CV] END bootstrap=True, max_depth=37, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=70; total time=   2.5s\n",
      "[CV] END bootstrap=False, max_depth=None, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=80; total time=   4.1s\n",
      "[CV] END bootstrap=True, max_depth=26, max_features=auto, min_samples_leaf=2, min_samples_split=2, n_estimators=100; total time=   3.6s\n",
      "[CV] END bootstrap=False, max_depth=10, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=100; total time=   4.1s\n",
      "[CV] END bootstrap=True, max_depth=43, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=40; total time=   1.3s\n",
      "[CV] END bootstrap=False, max_depth=54, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=40; total time=   2.0s\n",
      "[CV] END bootstrap=False, max_depth=None, max_features=auto, min_samples_leaf=4, min_samples_split=5, n_estimators=20; total time=   1.0s\n",
      "[CV] END bootstrap=False, max_depth=26, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=100; total time=   5.4s\n",
      "[CV] END bootstrap=True, max_depth=15, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=10; total time=   0.3s\n",
      "[CV] END bootstrap=True, max_depth=15, max_features=sqrt, min_samples_leaf=4, min_samples_split=2, n_estimators=10; total time=   0.3s\n",
      "[CV] END bootstrap=False, max_depth=37, max_features=sqrt, min_samples_leaf=1, min_samples_split=5, n_estimators=80; total time=   4.7s\n",
      "[CV] END bootstrap=False, max_depth=48, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=40; total time=   2.4s\n",
      "[CV] END bootstrap=False, max_depth=21, max_features=sqrt, min_samples_leaf=2, min_samples_split=5, n_estimators=40; total time=   2.1s\n",
      "[CV] END bootstrap=True, max_depth=15, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=60; total time=   1.9s\n",
      "[CV] END bootstrap=True, max_depth=15, max_features=auto, min_samples_leaf=4, min_samples_split=2, n_estimators=60; total time=   1.9s\n",
      "[CV] END bootstrap=True, max_depth=37, max_features=auto, min_samples_leaf=4, min_samples_split=10, n_estimators=50; total time=   1.5s\n",
      "[CV] END bootstrap=False, max_depth=37, max_features=auto, min_samples_leaf=1, min_samples_split=2, n_estimators=70; total time=   4.0s\n"
     ]
    }
   ],
   "source": [
    "# SVM\n",
    "\n",
    "cf_matrix = confusion_matrix(y_test, svm_y_test_pred)\n",
    "sns.heatmap(cf_matrix, annot=True, fmt='.4g');"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "64985915",
   "metadata": {},
   "source": [
    "We can see the true negative (971) and true positive (136) are relatively high compared to the false positive (26) and false negative (67). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "60fac86f",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}