Classification-Methods.ipynb

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    "# Comparison of Classification Methods Assignment (Part 2)\n",
    "\n",
    "#### Course: 514A - Data Mining\n",
    "#### Instructor: Weixiong Zhang\n",
    "#### Author: Nurzhan Kanatzhanov\n",
    "#### Date: April 28, 2021"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Clump Thickness</th>\n",
       "      <th>Uniformity of Cell Size</th>\n",
       "      <th>Uniformity of Cell Shape</th>\n",
       "      <th>Marginal Adhesion</th>\n",
       "      <th>Single Epithelial Cell Size</th>\n",
       "      <th>Bare Nuclei</th>\n",
       "      <th>Bland Chromatin</th>\n",
       "      <th>Normal Nucleoli</th>\n",
       "      <th>Mitoses</th>\n",
       "      <th>Class</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
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       "      <th>1</th>\n",
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       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>10.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2.0</td>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      "text/plain": [
       "   Clump Thickness  Uniformity of Cell Size  Uniformity of Cell Shape  \\\n",
       "0                5                        1                         1   \n",
       "1                5                        4                         4   \n",
       "2                3                        1                         1   \n",
       "3                6                        8                         8   \n",
       "4                4                        1                         1   \n",
       "\n",
       "   Marginal Adhesion  Single Epithelial Cell Size  Bare Nuclei  \\\n",
       "0                  1                            2          1.0   \n",
       "1                  5                            7         10.0   \n",
       "2                  1                            2          2.0   \n",
       "3                  1                            3          4.0   \n",
       "4                  3                            2          1.0   \n",
       "\n",
       "   Bland Chromatin  Normal Nucleoli  Mitoses  Class  \n",
       "0                3                1        1      2  \n",
       "1                3                2        1      2  \n",
       "2                3                1        1      2  \n",
       "3                3                7        1      2  \n",
       "4                3                1        1      2  "
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import random\n",
    "import pandas as pd\n",
    "import math\n",
    "import numpy as np\n",
    "\n",
    "from sklearn.model_selection import KFold, StratifiedKFold, train_test_split, cross_validate, cross_val_score\n",
    "from sklearn.metrics import accuracy_score, classification_report\n",
    "\n",
    "# url and the header names for all eleven columns of the breast cancer data\n",
    "url = \"https://archive.ics.uci.edu/ml/machine-learning-databases/breast-cancer-wisconsin/breast-cancer-wisconsin.data\"\n",
    "names = [\"Sample code number\", \"Clump Thickness\", \"Uniformity of Cell Size\", \"Uniformity of Cell Shape\", \"Marginal Adhesion\", \n",
    "         \"Single Epithelial Cell Size\", \"Bare Nuclei\", \"Bland Chromatin\", \"Normal Nucleoli\", \"Mitoses\", \"Class\"]\n",
    "\n",
    "# reading the data and making all missing values indicated as '?' as NaN/None\n",
    "df = pd.read_csv(url, delimiter=\",\", names=names, na_values=['?'])\n",
    "df = df.drop('Sample code number', 1)\n",
    "# sample data excerpt\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The candidate methods to be analyzed and compared:\n",
    "##### 1. k-nearest-neighbors,\n",
    "##### 2. decision tree,\n",
    "##### 3. random forest, \n",
    "##### 4. polynomial kernel SVM of your choice,\n",
    "##### 5. Gaussian kernel SVM),\n",
    "##### 6. deep neural network with Sigmoid activation functions,\n",
    "##### 7. deep neural network with ReLu activation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Clump Thickness                 0\n",
       "Uniformity of Cell Size         0\n",
       "Uniformity of Cell Shape        0\n",
       "Marginal Adhesion               0\n",
       "Single Epithelial Cell Size     0\n",
       "Bare Nuclei                    16\n",
       "Bland Chromatin                 0\n",
       "Normal Nucleoli                 0\n",
       "Mitoses                         0\n",
       "Class                           0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# there are 16 instances of missing values -> let's fix them\n",
    "df.isin([np.nan]).sum(axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
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       "0"
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     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "# quickly fix the NaN values to be randomly chosen depending on the range of values in the column (Bare Nuclei)\n",
    "df['Bare Nuclei'] = df['Bare Nuclei'].apply(lambda x: np.random.choice(df['Bare Nuclei'].dropna().values) if np.isnan(x) else x)\n",
    "\n",
    "# check if any NaN values are left \n",
    "df['Bare Nuclei'].isin([np.nan]).sum(axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(699, 10)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Clump Thickness</th>\n",
       "      <th>Uniformity of Cell Size</th>\n",
       "      <th>Uniformity of Cell Shape</th>\n",
       "      <th>Marginal Adhesion</th>\n",
       "      <th>Single Epithelial Cell Size</th>\n",
       "      <th>Bare Nuclei</th>\n",
       "      <th>Bland Chromatin</th>\n",
       "      <th>Normal Nucleoli</th>\n",
       "      <th>Mitoses</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
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       "      <td>1</td>\n",
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       "      <td>1</td>\n",
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       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      "text/plain": [
       "   Clump Thickness  Uniformity of Cell Size  Uniformity of Cell Shape  \\\n",
       "0                5                        1                         1   \n",
       "1                5                        4                         4   \n",
       "2                3                        1                         1   \n",
       "3                6                        8                         8   \n",
       "4                4                        1                         1   \n",
       "\n",
       "   Marginal Adhesion  Single Epithelial Cell Size  Bare Nuclei  \\\n",
       "0                  1                            2          1.0   \n",
       "1                  5                            7         10.0   \n",
       "2                  1                            2          2.0   \n",
       "3                  1                            3          4.0   \n",
       "4                  3                            2          1.0   \n",
       "\n",
       "   Bland Chromatin  Normal Nucleoli  Mitoses  \n",
       "0                3                1        1  \n",
       "1                3                2        1  \n",
       "2                3                1        1  \n",
       "3                3                7        1  \n",
       "4                3                1        1  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# dividing dataset into inputs (X) and target (y)\n",
    "X = df.drop(columns=['Class'])\n",
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2, 2, 4, 4, 4])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# separate target (y) values\n",
    "y = df['Class'].values\n",
    "y[-5:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### First (1st): K-NN classification method"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 458 benign samples and  241 malignant samples in the dataset\n"
     ]
    }
   ],
   "source": [
    "# Let's see how many samples of each breast cancer type we have in our set\n",
    "samples = pd.DataFrame(df.groupby(\"Class\").size().reset_index()).rename(columns={0:\"samples\"})\n",
    "print(\"There are\", samples['samples'][0], \"benign samples and \", samples['samples'][1], \"malignant samples in the dataset\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.88571429 0.98571429 0.97142857 0.91428571 1.         0.97142857\n",
      " 0.97142857 1.         0.98571429 0.97101449]\n",
      "Cross validation scores mean: 0.965672877846791\n",
      "Time elapsed:  0.17720985412597656\n"
     ]
    }
   ],
   "source": [
    "# create K-NN Classifier model and do 10-cross validation\n",
    "\n",
    "import time\n",
    "start = time.time()\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.model_selection import cross_val_score\n",
    "\n",
    "# two neighbors since only 2 classes exist for the dataset\n",
    "knn = KNeighborsClassifier()\n",
    "\n",
    "# train model with cross-validation of 10\n",
    "# cross_val_score function splits our data into 5 groups and fits and scores our data 5 seperate times, \n",
    "# recording the accuracy score in an array each time\n",
    "cv_scores = cross_val_score(knn, X, y, cv=10)\n",
    "\n",
    "#print each cv score (accuracy) and average them\n",
    "print(cv_scores)\n",
    "print('Cross validation scores mean: {}'.format(np.mean(cv_scores)))\n",
    "end = time.time()\n",
    "print('Time elapsed: ', end - start)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "fig = plt.figure(figsize = (10, 5))\n",
    "folds = range(1, 11)\n",
    "scores = np.array(cv_scores)\n",
    "percentages = scores * 100\n",
    "bars = plt.bar(folds, percentages, color='maroon', width=0.5)\n",
    "plt.xlabel(\"# of the fold\")\n",
    "plt.ylabel(\"% accuracy of the model\")\n",
    "plt.title(\"Performance of each of the 10 folds with cross validation on the dataset\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Recall: 0.9543333333333335\n",
      "Precision: 0.9489552114552116\n",
      "F1: 0.9503872654590906\n",
      "Accuracy: 0.965672877846791\n"
     ]
    }
   ],
   "source": [
    "# getting all of the important metrics (performance) by averaging out 10 matrices\n",
    "from sklearn.metrics import make_scorer, accuracy_score, precision_score, recall_score, f1_score, SCORERS\n",
    "from sklearn.preprocessing import LabelBinarizer\n",
    "\n",
    "lb = LabelBinarizer()\n",
    "y_binary = np.array([number[0] for number in lb.fit_transform(y)])\n",
    "\n",
    "recall = cross_val_score(knn, X, y_binary, cv=10, scoring='recall')\n",
    "print('Recall:', np.mean(recall))\n",
    "precision = cross_val_score(knn, X, y_binary, cv=10, scoring='precision')\n",
    "print('Precision:', np.mean(precision))\n",
    "f1 = cross_val_score(knn, X, y_binary, cv=10, scoring='f1')\n",
    "print('F1:', np.mean(f1))\n",
    "accuracy = cross_val_score(knn, X, y_binary, cv=10, scoring='accuracy')\n",
    "print('Accuracy:', np.mean(accuracy))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy for Manhattan distance: 0.9642857142857142\n",
      "Accuracy for Minkowski distance: 0.962815734989648\n"
     ]
    }
   ],
   "source": [
    "manhattan_knn = KNeighborsClassifier(p=1)\n",
    "manhattan = cross_val_score(manhattan_knn, X, y_binary, cv=10, scoring='accuracy')\n",
    "print('Accuracy for Manhattan distance:', np.mean(manhattan))\n",
    "\n",
    "minkowski_knn = KNeighborsClassifier(p=3)\n",
    "minkowski = cross_val_score(minkowski_knn, X, y_binary, cv=10, scoring='accuracy')\n",
    "print('Accuracy for Minkowski distance:', np.mean(minkowski))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "distance_measures = np.array([np.mean(accuracy), np.mean(manhattan), np.mean(minkowski)])\n",
    "fig = plt.figure(figsize = (10, 5))\n",
    "bar_measures = [\"Eucledian\", \"Manhattan\", \"Minkowski\"]\n",
    "bars3 = plt.bar(bar_measures, distance_measures, color='green', width=0.5)\n",
    "plt.xlabel(\"Type of distance metric\")\n",
    "plt.ylabel(\"Accuracy of the model\")\n",
    "plt.title(\"Performance of different distance metrics/measures on the dataset\")\n",
    "plt.ylim(ymin=0.962)\n",
    "plt.ylim(ymax=0.967)\n",
    "for bar in bars3:\n",
    "    yval = bar.get_height()\n",
    "    plt.text(bar.get_x(), yval + 0.0001, yval)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Second (2nd): Decision Tree classification method"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.87142857 0.92857143 0.94285714 0.88571429 0.92857143 0.92857143\n",
      " 0.98571429 0.97142857 0.98571429 0.97101449]\n",
      "Cross validation scores mean for decision tree classifier: 0.9399585921325052\n",
      "Time elapsed:  0.0700840950012207\n"
     ]
    }
   ],
   "source": [
    "start = time.time()\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "\n",
    "dt = DecisionTreeClassifier()\n",
    "dt_scores = cross_val_score(dt, X, y, cv=10)\n",
    "\n",
    "#print each cv score (accuracy) and average them\n",
    "print(dt_scores)\n",
    "print('Cross validation scores mean for decision tree classifier: {}'.format(np.mean(dt_scores)))\n",
    "end = time.time()\n",
    "print('Time elapsed: ', end - start)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "fig = plt.figure(figsize = (10, 5))\n",
    "folds = range(1, 11)\n",
    "scores = np.array(dt_scores)\n",
    "percentages = scores * 100\n",
    "bars = plt.bar(folds, percentages, color='orange', width=0.5)\n",
    "plt.xlabel(\"# of the fold\")\n",
    "plt.ylabel(\"% accuracy of the model\")\n",
    "plt.title(\"Performance of each of the 10 folds with cross validation on the dataset (decision tree)\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Recall: 0.9168333333333332\n",
      "Precision: 0.9168874643874645\n",
      "F1: 0.9171277869638421\n",
      "Accuracy: 0.9471014492753623\n"
     ]
    }
   ],
   "source": [
    "recall = cross_val_score(dt, X, y_binary, cv=10, scoring='recall')\n",
    "print('Recall:', np.mean(recall))\n",
    "precision = cross_val_score(dt, X, y_binary, cv=10, scoring='precision')\n",
    "print('Precision:', np.mean(precision))\n",
    "f1 = cross_val_score(dt, X, y_binary, cv=10, scoring='f1')\n",
    "print('F1:', np.mean(f1))\n",
    "accuracy = cross_val_score(dt, X, y_binary, cv=10, scoring='accuracy')\n",
    "print('Accuracy:', np.mean(accuracy))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.9270186335403727\n"
     ]
    }
   ],
   "source": [
    "# entropy information gain\n",
    "dt2 = DecisionTreeClassifier(criterion=\"entropy\")\n",
    "accuracy2 = cross_val_score(dt2, X, y_binary, cv=10, scoring='accuracy')\n",
    "print('Accuracy:', np.mean(accuracy2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average Entropy Accuracy: 0.9350571842650119\n"
     ]
    }
   ],
   "source": [
    "sum = 0\n",
    "for i in range(1000):\n",
    "    dt2 = DecisionTreeClassifier(criterion=\"entropy\")\n",
    "    accuracy2 = cross_val_score(dt2, X, y_binary, cv=10, scoring='accuracy')\n",
    "    sum += np.mean(accuracy2)\n",
    "    \n",
    "print('Average Entropy Accuracy:', sum / 1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average Gini Accuracy: 0.9387469151138721\n"
     ]
    }
   ],
   "source": [
    "sum = 0\n",
    "for i in range(1000):\n",
    "    dt2 = DecisionTreeClassifier(criterion=\"gini\")\n",
    "    accuracy2 = cross_val_score(dt2, X, y_binary, cv=10, scoring='accuracy')\n",
    "    sum += np.mean(accuracy2)\n",
    "    \n",
    "print('Average Gini Accuracy:', sum / 1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "decision_trees = np.array([0.9379, 0.9427])\n",
    "fig = plt.figure(figsize = (8, 5))\n",
    "dt_measures = [\"Entropy / Information Gain\", \"Gini Impurity\"]\n",
    "bars4 = plt.bar(dt_measures, decision_trees, color='purple', width=0.5)\n",
    "plt.xlabel(\"Type of a split quality criterion\")\n",
    "plt.ylabel(\"Accuracy of the model\")\n",
    "plt.title(\"Performance of different split quality criteria for decision tree classifiers on the dataset\")\n",
    "plt.ylim(ymin=0.93)\n",
    "plt.ylim(ymax=0.945)\n",
    "for bar in bars4:\n",
    "    yval = bar.get_height()\n",
    "    plt.text(bar.get_x(), yval + 0.001, yval)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Third (3rd): Random Forest classification method"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.9        0.98571429 0.97142857 0.91428571 0.98571429 0.98571429\n",
      " 0.95714286 0.98571429 0.98571429 1.        ]\n",
      "Cross validation scores mean for random forest classifier: 0.9671428571428571\n",
      "Time elapsed:  1.3545379638671875\n"
     ]
    }
   ],
   "source": [
    "start = time.time()\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "# by default: 100 trees in the forest, gini impurity criterion, bootstrapping = True, \n",
    "# OOB samples = False, random_state = None\n",
    "forest = RandomForestClassifier()\n",
    "forest_scores = cross_val_score(forest, X, y, cv=10)\n",
    "\n",
    "#print each cv score (accuracy) and average them\n",
    "print(forest_scores)\n",
    "print('Cross validation scores mean for random forest classifier: {}'.format(np.mean(forest_scores)))\n",
    "end = time.time()\n",
    "print('Time elapsed: ', end - start)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Recall: 0.9460000000000001\n",
      "Precision: 0.9534070711896799\n",
      "F1: 0.9486221607122889\n",
      "Accuracy: 0.9628364389233954\n"
     ]
    }
   ],
   "source": [
    "recall = cross_val_score(forest, X, y_binary, cv=10, scoring='recall')\n",
    "print('Recall:', np.mean(recall))\n",
    "precision = cross_val_score(forest, X, y_binary, cv=10, scoring='precision')\n",
    "print('Precision:', np.mean(precision))\n",
    "f1 = cross_val_score(forest, X, y_binary, cv=10, scoring='f1')\n",
    "print('F1:', np.mean(f1))\n",
    "accuracy = cross_val_score(forest, X, y_binary, cv=10, scoring='accuracy')\n",
    "print('Accuracy:', np.mean(accuracy))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cross validation scores mean for random forest classifier: 0.9671221532091099\n"
     ]
    }
   ],
   "source": [
    "forest200 = RandomForestClassifier(n_estimators=5000)\n",
    "forest200_scores = cross_val_score(forest200, X, y, cv=10)\n",
    "print('Cross validation scores mean for random forest classifier: {}'.format(np.mean(forest200_scores)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "num_trees = []\n",
    "forest_builder = []\n",
    "\n",
    "num_trees.append(1)\n",
    "newForest = RandomForestClassifier(n_estimators=1)\n",
    "newForest_score = cross_val_score(newForest, X, y, cv=10)\n",
    "forest_builder.append(np.mean(newForest_score))\n",
    "\n",
    "# increments of 10 trees\n",
    "for i in range(10, 110, 10):\n",
    "    num_trees.append(i)\n",
    "    newForest = RandomForestClassifier(n_estimators=i)\n",
    "    newForest_score = cross_val_score(newForest, X, y, cv=10)\n",
    "    forest_builder.append(np.mean(newForest_score))\n",
    "    \n",
    "# after that, increments of 100\n",
    "for i in range(100, 1100, 100):\n",
    "    num_trees.append(i)\n",
    "    newForest = RandomForestClassifier(n_estimators=i)\n",
    "    newForest_score = cross_val_score(newForest, X, y, cv=10)\n",
    "    forest_builder.append(np.mean(newForest_score))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'num_trees' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-20-af780182b41b>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfigsize\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m5\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnum_trees\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mforest_builder\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mxlabel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Number of trees in a random forest\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mylabel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Accuracy of the model\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtitle\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Performance of a random forest with a set amount of estimators (trees) on the dataset\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'num_trees' is not defined"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 720x360 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (10, 5))\n",
    "plt.plot(num_trees, forest_builder)\n",
    "plt.xlabel(\"Number of trees in a random forest\")\n",
    "plt.ylabel(\"Accuracy of the model\")\n",
    "plt.title(\"Performance of a random forest with a set amount of estimators (trees) on the dataset\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average gini criterion accuracy: 0.9652805383022769\n",
      "Average entropy criterion accuracy: 0.9644527950310561\n"
     ]
    }
   ],
   "source": [
    "sumForest = 0\n",
    "for i in range(100):\n",
    "    newForest = RandomForestClassifier(criterion=\"gini\")\n",
    "    newForest_score = cross_val_score(newForest, X, y, cv=10)\n",
    "    sumForest += np.mean(newForest_score)\n",
    "    \n",
    "print(\"Average gini criterion accuracy:\", sumForest / 100)\n",
    "\n",
    "sumForest2 = 0\n",
    "for i in range(100):\n",
    "    newForest2 = RandomForestClassifier(criterion=\"entropy\")\n",
    "    forestscore = cross_val_score(newForest2, X, y, cv=10)\n",
    "    sumForest2 += np.mean(forestscore)\n",
    "    \n",
    "print(\"Average entropy criterion accuracy:\", sumForest2 / 100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "decision_trees = np.array([sumForest2/100, sumForest/100])\n",
    "fig = plt.figure(figsize = (8, 5))\n",
    "dt_measures = [\"Entropy / Information Gain\", \"Gini Impurity\"]\n",
    "bars4 = plt.bar(dt_measures, decision_trees, color='brown', width=0.5)\n",
    "plt.xlabel(\"Type of a split quality criterion\")\n",
    "plt.ylabel(\"Accuracy of the model\")\n",
    "plt.title(\"Performance of different split quality criteria for a random forest classifier on the dataset\")\n",
    "plt.ylim(ymin=0.93)\n",
    "plt.ylim(ymax=0.98)\n",
    "for bar in bars4:\n",
    "    yval = bar.get_height()\n",
    "    plt.text(bar.get_x(), yval + 0.001, yval)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average accuracy of random forest with bootstrapping technique: 0.9653652173913043\n",
      "Average accuracy of random forest with no bootstrapping: 0.9624534161490683\n"
     ]
    }
   ],
   "source": [
    "# now tweaking bootstrapping parameter\n",
    "sumForest = 0\n",
    "for i in range(50):\n",
    "    newForest = RandomForestClassifier(bootstrap=True)\n",
    "    newForest_score = cross_val_score(newForest, X, y, cv=10)\n",
    "    sumForest += np.mean(newForest_score)\n",
    "    \n",
    "print(\"Average accuracy of random forest with bootstrapping technique:\", sumForest / 50)\n",
    "\n",
    "sumForest2 = 0\n",
    "for i in range(50):\n",
    "    newForest2 = RandomForestClassifier(bootstrap=False)\n",
    "    forestscore = cross_val_score(newForest2, X, y, cv=10)\n",
    "    sumForest2 += np.mean(forestscore)\n",
    "    \n",
    "print(\"Average accuracy of random forest with no bootstrapping:\", sumForest2 / 50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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zY2I+XxE+39JzsbJzajzhS3lAzLOWmR2RId1uhHNvGVDDzP4A1C2daWY/NbMG8XhfGSevN7OjzayDhRrKVYQAez2bIdbMvgTcZmZ1zayamTU3s+9tRjbb8ruqwmtjBoMI3wPtAMxsdzM7I0vaJ4EfmtmR8XrzR8rHGZWdP+myZT3fLbzX8JQYnK8lNHcp/WwqK3NO++BbGyC5+3zCL7FrCQf9fMJTVpuzTTcQnlyYSngibVKcti3K9znhAvY44V73OYRf/7m6KpbpbUL14E2EtkQ5b7e7FwOnACcRfj3fA/Ry95k5luF6QlXuXMIJnq2WoVJxfxwPnEX45fMxGxvAZ0q/nhDMFcb1LwceIPzqzsWNhAvKSjO7KsP8lsArhJNqHHCPu49OzP8XYZs/iH+5HBelL9v8xMwmpWfGz+N0QpuDFYTq4acS898jXFBeITxNkn7J3S+AP5rZ54QL5OM5lAnC01zTgY/NbHli+nDCr9LhXvHj2xmPxRzXfSfQ08ILBv+2ucdBBncQ2iAuJzRaHZnjcqUuJfxo+ICwf/9FCABzdRXhXP6c8KWfS+CcNIRwTi0kNJR/M9cF3X05cAbh4YlPCMfw64kkDxLO0TGEc+Yrtu5HTUVlGQX8nlBLvphQw3hWBeknEB5iGUg49ucQzgMIn/0Awmf6MaEW59o4r7JzqvQ60YLwUM4CwnmV9iKhXcp7hP3/FeVva50ITDez1YRj9qzYvGIfwpf+KkKD4tfYssCkF+FHc+mTsU+SuVlENtvyu6rcOVlZYncfTjhHh1m4zTWN8J2SKe10wtOL/yIcFysIn0mpys6f/sDD8br9Yyo+36sRapgWEa5L3yNcI3Mpc3o9GZU+tSMikX1NL5mM63qI0Lhzs97ttQ3X/z6hKjrv2yoi8m3yra1BEpGtY2Y/IrRHeLWqyyIi8k2T1wDJzE40s1lmNsc2fUqI2LZluJlNNbPxZtY+Tm9lZkWJv1Vmdnmct4eZvWxms+P/+vncBpHtkYXuBu4FLkk95SgiIuTxFlts1PYe4UWGCwjtF8529xmJNLcAq939+thg6253PzZDPgsJrdg/NLObCQ1VB8Sgq767/yYvGyEiIiI7pHzWIHUF5rj7B7Fx6jBC4+KktoSXmxEbDjczs4apNMcSXtJX+g6IU4GH4/DDhDdmioiIiGwz+QyQGlP+KYEFbPqSpimEp3qIjy03BdKPq59FeMSxVMPSF9rF/3sjIiIisg3ls9fgTC9/St/PG0B422UR4dHFyYR3VYQMwnsUTiF0MbF5Kze7iNA1BrVr1z64devWlSwhIiIi3wYTJ05c7u4N8rmOfAZICyj/ltcCUm/+dPdVhE4rS98KOzf+lToJmOTuyTfBLjGzRu6+2MwaEfqc2YS73wfcB9ClSxefMGHCVm6OiIiIfBOY2dZ0fZWTfN5iextoGd/qWpNwq6zcixLNrJ5tfLvvBYTeoFclkpxN+dtrxDzOjcPnEjocFREREdlm8laD5O4lZtaP8AbT6sCD7j7dzPrG+YOANsAQM1tPeMNon9LlLfThchyht+2kAcDjZtaH8ObUbK88FxEREdkiO8SbtHWLTUREZPthZhPdvUs+16E3aYuIiIikKEASERERSVGAJCIiIpKiAElEREQkRQGSiIiISIoCJBEREZEUBUgiIiIiKQqQRERERFIUIImIiIikKEASERERSVGAJCIiIpKiAElEREQkRQGSiIiISIoCJBEREZEUBUgiIiIiKQqQRERERFIUIImIiIikKEASERERSVGAJCIiIpKiAElEREQkRQGSiMgObuTIkbRq1YoWLVowYMCATeavWLGCHj160LFjR7p27cq0adPK5q1cuZKePXvSunVr2rRpw7hx4wDo378/jRs3prCwkMLCQl544QUAxo8fXzatU6dODB8+vCyv4uJiLrroIg488EBat27Nv//9bwAeeughGjRoULbcAw88AEBRURGHHXYY7dq1o2PHjjz22GNleQ0cOJAWLVpgZixfvrxs+i233FKWT/v27alevTqffvopX331FV27dqVTp060a9eO6667rmyZKVOmcNhhh9GhQwe6d+/OqlWrAPjkk084+uijqVOnDv369Su3z0488cSyvPr27cv69esBWLt2LWeeeSYtWrTgkEMOYd68eeWWW7VqFY0bNy6XX58+fejUqRMdO3akZ8+erF69urKPVLYFd9/u/w4++GAXEZFNlZSU+AEHHODvv/++r1271jt27OjTp08vl+aqq67y/v37u7v7u+++68ccc0zZvF69evn999/v7u5r1671FStWuLv7dddd57fccssm6/viiy983bp17u6+aNEib9CgQdn4H/7wB/+///s/d3dfv369L1u2zN3dBw8e7Jdccskmec2aNcvfe+89d3dfuHCh77PPPmXrnzRpks+dO9ebNm1alk/aiBEj/Oijj3Z39w0bNvjnn3/u7u7FxcXetWtXHzdunLu7d+nSxUePHu3u7v/4xz/8d7/7nbu7r1692seOHev33nvvJuX77LPPyvI9/fTTfejQoe7ufvfdd/vPf/5zd3cfOnSo//jHPy633GWXXeZnn312ufxK83J3v+KKK/zGG2/MuD07EmCC5zl2UA2SiMgObPz48bRo0YIDDjiAmjVrctZZZ/HMM8+USzNjxgyOPfZYAFq3bs28efNYsmQJq1atYsyYMfTp0weAmjVrUq9evQrXt+uuu1KjRg0AvvrqK8ysbN6DDz7Ib3/7WwCqVavGXnvtVWFeBx54IC1btgRg3333Ze+992bZsmUAdO7cmWbNmlW4/NChQzn77LMBMDPq1KkDwLp161i3bl1Z2WbNmsVRRx0FwHHHHVdWs1W7dm2OPPJIatWqtUnedevWBaCkpITi4uKyvJ555hnOPfdcAHr27MmoUaMI3/cwceJElixZwvHHH58xL3dnzZo15faZ5I8CJBGRHdjChQtp0qRJ2XhBQQELFy4sl6ZTp0489dRTQAioPvzwQxYsWMAHH3xAgwYN6N27N507d+aCCy7giy++KFtu4MCBdOzYkfPPP58VK1aUTX/rrbdo164dHTp0YNCgQdSoUYOVK1cC8Pvf/56DDjqIM844gyVLlpQt8+9//7vsFtP8+fM32Y7x48dTXFxM8+bNc9ruL7/8kpEjR/KjH/2obNr69espLCxk77335rjjjuOQQw4BoH379owYMQKAJ554IuP6MznhhBPYe++92W233ejZsydQfn/XqFGD3XffnU8++YQNGzZw5ZVXcsstt2TMq3fv3uyzzz7MnDmTSy+9NKf1y9ZRgCQisgMrrb1IStdQXHPNNaxYsYLCwkLuuusuOnfuTI0aNSgpKWHSpElcfPHFTJ48mdq1a5e1Ybr44ot5//33KSoqolGjRlx55ZVl+R1yyCFMnz6dt99+mxtvvJGvvvqKkpISFixYwBFHHMGkSZM47LDDuOqqqwDo3r078+bNY+rUqXz/+98vq4EptXjxYn72s58xePBgqlXL7Wvt2Wef5YgjjmCPPfYom1a9enWKiopYsGAB48ePL2tr9eCDD3L33Xdz8MEH8/nnn1OzZs2c1vHiiy+yePFi1q5dy6uvvgpk39/33HMPJ598crlgNWnw4MEsWrSINm3alGtrJfmjAElEZAdWUFBQrkZkwYIF7LvvvuXS1K1bl8GDB1NUVMSQIUNYtmwZ+++/PwUFBRQUFJTVtPTs2ZNJkyYB0LBhQ6pXr061atW48MILGT9+/CbrbtOmDbVr12batGnsueee7LrrrvTo0QOAM844oyyvPffck5133hmACy+8kIkTJ5blsWrVKn7wgx9www03cOihh+a83cOGDSu7vZZWr149unXrxsiRI4FwW/Gll15i4sSJnH322TnXUgHUqlWLU045pey2ZXJ/l5SU8Nlnn7HHHnswbtw4Bg4cSLNmzbjqqqsYMmQI11xzTbm8qlevzplnnll2i0/ySwGSiMgO7Dvf+Q6zZ89m7ty5FBcXM2zYME455ZRyaVauXElxcTEADzzwAEcddRR169Zln332oUmTJsyaNQuAUaNG0bZtWyDU6pQaPnw47du3B2Du3LmUlJQA8OGHHzJr1iyaNWuGmdG9e3dGjx5dYV4jRoygTZs2QHjqrUePHvTq1Yszzjgj523+7LPPeO211zj11FPLpi1btqzsNt+aNWt45ZVXaN26NQBLly4FYMOGDdxwww307du3wvxXr15dVuaSkhJeeOGFsrxOOeUUHn74YQCefPJJjjnmGMyMRx99lI8++oh58+Zx66230qtXLwYMGIC7M2fOHCDUPj377LNleUme5bsV+DfhT0+xiYhk9/zzz3vLli39gAMO8BtuuMHd3e+9916/99573d39jTfe8BYtWnirVq28R48e/umnn5YtO3nyZD/44IO9Q4cOfuqpp5bN++lPf+rt27f3Dh06ePfu3X3RokXu7j5kyBBv27atd+rUyTt37uzDhw8vy2vevHn+3e9+1zt06ODHHHOMf/jhh+7ufs0113jbtm29Y8eO3q1bN3/33Xfd3f2RRx7xGjVqeKdOncr+Jk+e7O7ud955pzdu3NirV6/ujRo18j59+pStZ/DgwX7mmWeW2wdTpkzxwsJC79Chg7dr186vv/76snl33HGHt2zZ0lu2bOm/+c1vfMOGDWXzmjZt6vXr1/fatWt748aNffr06f7xxx97ly5dvEOHDt62bVvv169f2ZN6a9as8Z49e3rz5s39O9/5jr///vubfB7Jp/bWr1/vhx9+uLdv397btWvn55xzTrmn2nZUfA1PsZlnuB+6venSpYtPmDChqoshIiIi24CZTXT3Lvlch26xiYiIiKQoQBIRERFJUYAkIiIikqIASURERCRFAZKIiIhIigIkERERkRQFSCIiIiIpeQ2QzOxEM5tlZnPM7JoM8+ub2XAzm2pm482sfWJePTN70sxmmtm7ZnZYnN7fzBaaWVH8Ozmf2yAiIiI7nhr5ytjMqgN3A8cBC4C3zWyEu89IJLsWKHL3HmbWOqY/Ns67Exjp7j3NrCawa2K529391nyVXURkR3D99ddXdRFkO3bddddVdRG2Sj5rkLoCc9z9A3cvBoYBp6bStAVGAbj7TKCZmTU0s7rAUcA/4rxid1+Zx7KKiIiIlMlngNQYmJ8YXxCnJU0BTgcws65AU6AAOABYBgw2s8lm9oCZ1U4s1y/elnvQzOrnbQtERERkh5TPAMkyTEt3/DYAqG9mRcClwGSghHDr7yDgXnfvDHwBlLZhuhdoDhQCi4HbMq7c7CIzm2BmE5YtW7Z1WyIiIiI7lHwGSAuAJonxAmBRMoG7r3L33u5eCPQCGgBz47IL3P2tmPRJQsCEuy9x9/XuvgG4n3ArbxPufp+7d3H3Lg0aNNiGmyUiIiLbu3wGSG8DLc1s/9jI+ixgRDJBfFKtZhy9ABgTg6aPgflm1irOOxaYEZdplMiiBzAtj9sgIiIiO6C8PcXm7iVm1g94EagOPOju082sb5w/CGgDDDGz9YQAqE8ii0uBR2MA9QHQO06/2cwKCbfr5gE/z9c2iIiIyI4pbwESgLu/ALyQmjYoMTwOaJll2SKgS4bpP9u2pRQREREpT2/SFhEREUlRgCQiIiKSogBpGxg5ciStWrWiRYsWDBgwYJP5K1asoEePHnTs2JGuXbsybdrGduUrV66kZ8+etG7dmjZt2jBu3DgAfv3rX9O6dWs6duxIjx49WLlyZbk8P/roI+rUqcOtt258oXi3bt1o1aoVhYWFFBYWsnTpUgAGDRpEhw4dKCws5Mgjj2TGjBnl8lq1ahWNGzemX79+ZdMGDhxIixYtMDOWL19eLv3o0aMpLCykXbt2fO973yubfv7557P33nvTvn17Mrn11lvL5VdcXEzv3r3p0KEDnTp1YvTo0WVpJ06cSIcOHWjRogWXXXYZ7hvfEPH444/Ttm1b2rVrxznnnFM2/eqrr6Zdu3a0adOm3DJ9+vShU6dOdOzYkZ49e7J69eqM5RMRESmlAGkrrV+/nksuuYT//Oc/zJgxg6FDh24SgPzlL3+hsLCQqVOnMmTIEH75y1+WzfvlL3/JiSeeyMyZM5kyZQpt2rQB4LjjjmPatGlMnTqVAw88kBtvvLFcnldccQUnnXTSJuV59NFHKSoqoqioiL333huAc845h3feeYeioiKuvvpqfvWrX5Vb5ve//325QAfgiCOO4JVXXqFp06blpq9cuZJf/OIXjBgxgunTp/PEE0+UzTvvvPMYOXJkxv00f/58Xn75Zfbbb7+yaffffz8A77zzDi+//DJXXnklGzZsAODiiy/mvvvuY/bs2cyePbss39mzZ3PjjTfy+uuvM336dO644w4A3njjDV5//XWmTp3KtGnTePvtt3nttdcAuP3225kyZQpTp05lv/32Y+DAgRnLKCIiUkoB0lYaP348LVq04IADDqBmzZqcddZZPPPMM+XSzJgxg2OPDV3MtW7dmnnz5rFkyRJWrVrFmDFj6NMnPLxXs2ZN6tWrB8Dxxx9PjRqhDf2hhx7KggULyvJ7+umnOeCAA2jXrl1OZaxbt27Z8BdffIHZxnd4Tpw4kSVLlnD88ceXW6Zz5840a9Zsk7z+9a9/cfrpp5cFOqVBGMBRRx3FHnvskbEMV1xxBTfffHO5dSf3y9577029evWYMGECixcvZtWqVRx22GGYGb169eLpp58GQlB1ySWXUL9+/XLrNzO++uoriouLWbt2LevWraNhw4bltt/dWbNmTbkyiIiIZKIAaSstXLiQJk02vg+zoKCAhQsXlkvTqVMnnnrqKSAEVB9++CELFizggw8+oEGDBvTu3ZvOnTtzwQUX8MUXX2yyjgcffLCstuiLL77gpptuytoJYO/evSksLORPf/pTudtSd999N82bN+fqq6/mb3/7GwAbNmzgyiuv5JZbbsl5e9977z1WrFhBt27dOPjggxkyZEily4wYMYLGjRvTqVOnctM7derEM888Q0lJCXPnzmXixInMnz+fhQsXUlBQUJYuuU/fe+893nvvPY444ggOPfTQspqlww47jKOPPppGjRrRqFEjTjjhhLLauNL9ss8++zBz5kwuvfTSnLdXRER2TAqQtlIyCCmVrqG45pprWLFiBYWFhdx111107tyZGjVqUFJSwqRJk7j44ouZPHkytWvX3qQN05///Gdq1KjBT37yEyD0jnzFFVdQp06dTdb76KOP8s477zB27FjGjh3LI488Ujbvkksu4f333+emm27ihhtuAOCee+7h5JNPLhfgVaakpISJEyfy/PPP8+KLL/KnP/2J9957L2v6L7/8kj//+c/88Y9/3GTe+eefT0FBAV26dOHyyy/n8MMPp0aNGhXu05KSEmbPns3o0aMZOnQoF1xwAStXrmTOnDm8++67LFiwgIULF/Lqq68yZsyYsuUHDx7MokWLaNOmDY899ljO2ysiIjumvL4HaUdQUFDA/Pkb++RdsGAB++67b7k0devWZfDgwUAIqPbff3/2339/vvzySwoKCjjkkEMA6NmzZ7kA6eGHH+a5555j1KhRZQHCW2+9xZNPPsnVV1/NypUrqVatGrVq1aJfv340bhz6At5tt90455xzGD9+PL169SpXlrPOOouLL74YgHHjxjF27FjuueceVq9eTXFxMXXq1MnY0Dy5vXvttRe1a9emdu3aHHXUUUyZMoUDDzwwY/r333+fuXPnltUeLViwgIMOOojx48ezzz77cPvtt5elPfzww2nZsiX169cvd0sxuU8LCgo49NBD2Wmnndh///1p1apVWcB06KGHlgWOJ510Em+++SZHHXVUWT7Vq1fnzDPP5JZbbqF3796IiIhkoxqkrfSd73yH2bNnM3fuXIqLixk2bBinnHJKuTQrV66kuLgYgAceeICjjjqKunXrss8++9CkSRNmzZoFwKhRo2jbti0Qnoy76aabGDFiBLvuumtZXmPHjmXevHnMmzePyy+/nGuvvZZ+/fpRUlJS9nTYunXreO6558qeJps9e3bZ8s8//zwtW4Z3cz766KN89NFHzJs3j1tvvZVevXpVGBwBnHrqqYwdO5aSkhK+/PJL3nrrrXK3stI6dOjA0qVLy8pcUFDApEmT2Gefffjyyy/Lbim+/PLL1KhRg7Zt29KoUSN222033nzzTdydIUOGcOqppwJw2mmn8d///heA5cuX895773HAAQew33778dprr1FSUsK6det47bXXaNOmDe7OnDlzgBCcPvvss7Ru3brCbRQREVEN0laqUaMGAwcO5IQTTmD9+vWcf/75tGvXjkGDwgvD+/bty7vvvkuvXr2oXr06bdu25R//+EfZ8nfddRc/+clPKC4u5oADDiiraerXrx9r167luOOOA0JD7dI8M1m7di0nnHAC69atY/369Xz/+9/nwgsvBMIj+6+88go77bQT9evX5+GHH650u/72t79x88038/HHH9OxY0dOPvlkHnjgAdq0acOJJ55Ix44dqVatGhdccEFZIHb22WczevRoli9fTkFBAddff31ZA/RMli5dygknnEC1atVo3LhxuVuC9957L+eddx5r1qzhpJNOKmuDdcIJJ/DSSy/Rtm1bqlevzi233MKee+5Jz549efXVV+nQoQNmxoknnkj37t3ZsGED5557LqtWrcLd6dSpE/fee2+l2y8iIjs2y9TeY3vTpUsXnzBhQlUXQ0TkG+X666+v6iLIdizbw0TbgplNdPdNuiPblnSLTURERCRFAZKIiIhIigIkERERkRQFSCIiIiIpCpBEREREUhQgiYiIiKQoQBIRERFJ0Ysit5LeIyL5lM/3iIiISHaqQRIRERFJUYAkIiIikqIASURERCRFAZKIiIhIigIkERERkRQFSCIiIiIpCpBEREREUhQgiYiIiKQoQBIRERFJUYAkIiIikqIASURERCRFAZKIiIhIStbOas3soIoWdPdJ2744IiIiIlUva4AE3FbBPAeO2cZlEREREflGyBogufvRX2dBRERERL4pKm2DZGa7mtnvzOy+ON7SzH6Y/6KJiIiIVI1cGmkPBoqBw+P4AuCGvJVIREREpIrlEiA1d/ebgXUA7r4GsLyWSkRERKQK5RIgFZvZLoSG2ZhZc2BtLpmb2YlmNsvM5pjZNRnm1zez4WY21czGm1n7xLx6Zvakmc00s3fN7LA4fQ8ze9nMZsf/9XPaUhEREZEc5RIgXQeMBJqY2aPAKODqyhYys+rA3cBJQFvgbDNrm0p2LVDk7h2BXsCdiXl3AiPdvTXQCXg3Tr8GGOXuLWNZNgm8RERERLZGRY/5A+DuL5vZJOBQwq21X7r78hzy7grMcfcPAMxsGHAqMCORpi1wY1zPTDNrZmYNgTXAUcB5cV4xoR0UMY9ucfhhYDTwmxzKIyIiIpKTzXlR5OL4fz8z2y+HF0U2BuYnxhcAh6TSTAFOB/5nZl2BpkABsB5YBgw2s07AREJg9gXQ0N0XA7j7YjPbu5JyiIiIiGyWXF4UWQvoQghmDOgIvAUcWUnemRpye2p8AHCnmRUB7wCTgRJgJ+Ag4FJ3f8vM7iTcSvt9JevcuHKzi4CLAPbbb79cFxMRERHJ3gbJ3Y+OL4v8EDjI3bu4+8FAZ2BODnkvAJokxguARal1rHL33u5eSGiD1ACYG5dd4O5vxaRPEgImgCVm1ggg/l+apfz3xTJ3adCgQQ7FFREREQlyaaTd2t3fKR1x92lAYQ7LvQ20NLP9zawmcBYwIpkgPqlWM45eAIyJQdPHwHwzaxXnHcvGtksjgHPj8LnAMzmURURERCRnlTbSBt41sweAfxJukf2UjU+UZeXuJWbWD3gRqA486O7TzaxvnD8IaAMMMbP1hACoTyKLS4FHYwD1AdA7Th8APG5mfYCPgDNy2AYRERGRnOUSIPUGLgZ+GcfHAPfmkrm7vwC8kJo2KDE8DmiZZdkiQtun9PRPCDVKIiIiInmRy2P+X5nZ3cArhBqkWe6+Lu8lExEREakilQZIZtaN8L6heYQn05qY2bnuPiavJRMRERGpIrncYrsNON7dZwGY2YHAUODgfBZMREREpKrk8hTbTqXBEYC7v0d4T5GIiIjIdimXGqQJZvYP4JE4/hPCm61FREREtku5BEgXA5cAlxHaII0B7slnoURERESqUi5Psa0F/hr/RERERLZ7lbZBMrMfmtlkM/vUzFaZ2edmturrKJyIiIhIVcjlFtsdwOnAO+6e7mxWREREZLuTy1Ns84FpCo5ERERkR5FLDdLVwAtm9hqwtnSiu6tNkoiIiGyXcgmQ/gysBmoBNfNbHBEREZGql0uAtIe7H5/3koiIiIh8Q+TSBukVM1OAJCIiIjuMXAKkS4CRZrZGj/mLiIjIjiCXF0Xu9nUUREREROSbIpcaJBEREZEdigIkERERkRQFSCIiIiIpOQVIZnakmfWOww3MbP/8FktERESk6uTSWe11wG+A38ZJOwH/zGehRERERKpSLjVIPYBTgC8A3H0RoCfbREREZLuVS4BUHDuqdQAzq53fIomIiIhUrVwCpMfN7O9APTO7EHgFuD+/xRIRERGpOrm8KPJWMzsOWAW0Av7g7i/nvWQiIiIiVSSXzmqJAZGCIhEREdkh5PIU2+lmNtvMPlNfbCIiIrIjyKUG6Wagu7u/m+/CiIiIiHwT5NJIe4mCIxEREdmRZK1BMrPT4+AEM3sMeBpYWzrf3Z/Kb9FEREREqkZFt9i6J4a/BI5PjDugAElERES2S1kDJHcv7XvtCHd/PTnPzI7Id8FEREREqkoubZDuynGaiIiIyHahojZIhwGHAw3M7FeJWXWB6vkumIiIiEhVqagNUk2gTkyT7Jx2FdAzn4USERERqUoVtUF6DXjNzB5y9w+/xjKJiIiIVKlK2yBtTXBkZiea2Swzm2Nm12SYX9/MhpvZVDMbb2btE/Pmmdk7ZlZkZhMS0/ub2cI4vcjMTt7S8omIiIhkklNfbFvCzKoDdwPHAQuAt81shLvPSCS7Fihy9x5m1jqmPzYx/2h3X54h+9vd/dZ8lV1ERER2bFlrkMzspvj/jC3Muyswx90/cPdiYBhwaipNW2AUgLvPBJqZWcMtXJ+IiIjINlHRLbaTzWwn4LdbmHdjYH5ifEGcljQFOB3AzLoCTYGCOM+Bl8xsopldlFquX7wt96CZ1d/C8omIiIhkVFGANBJYDnQ0s1Vm9nnyfw55W4ZpnhofANQ3syLgUmAyUBLnHeHuBwEnAZeY2VFx+r1Ac6AQWAzclnHlZheZ2QQzm7Bs2bIciisiIiISZA2Q3P3X7r478Ly713X33ZL/c8h7AdAkMV4ALEqtY5W793b3QqAX0ACYG+ctiv+XAsMJt+xw9yXuvt7dNwD3l07PUP773L2Lu3dp0KBBDsUVERERCXJ5iu1UM2toZj+Mf7lGG28DLc1sfzOrCZwFjEgmMLN6cR7ABcAYd19lZrXNbLeYpjahH7hpcbxRIosepdNFREREtpVKn2KLjbRvBUYTbpvdZWa/dvcnK1rO3UvMrB/wIuHN2w+6+3Qz6xvnDwLaAEPMbD0wA+gTF28IDDez0jL+y91Hxnk3m1kh4XbdPODnOW+tiIiISA5yecz/d8B34q0uYg3SK0CFARKAu78AvJCaNigxPA5omWG5D4BOWfL8WQ5lFhEREdliuXRWW600OIo+yXE5ERERkW+lXGqQRprZi8DQOH4mqVohERERke1JpQGSu//azE4HjiS0QbrP3YfnvWQiIiIiVSSnrkbc/SngqTyXRUREROQbQW2JRERERFIUIImIiIikVBogxZdDKpASERGRHUYugc9ZwGwzu9nM2uS7QCIiIiJVLZeuRn4KdAbeBwab2bjYEexueS+diIiISBXI6daZu68C/g0MAxoR+kCbZGaX5rFsIiIiIlUilzZI3c1sOPAqsBPQ1d1PInQFclWeyyciIiLytcvlPUhnALe7+5jkRHf/0szOz0+xRERERKpOLgHSdcDi0hEz2wVo6O7z3H1U3komIiIiUkVyaYP0BLAhMb4+ThMRERHZLuUSINVw9+LSkThcM39FEhEREalauQRIy8zslNIRMzsVWJ6/IomIiIhUrVzaIPUFHjWzgYAB84FeeS2ViIiISBWqNEBy9/eBQ82sDmDu/nn+iyUiIiJSdXKpQcLMfgC0A2qZGQDu/sc8lktERESkyuTyoshBwJnApYRbbGcATfNcLhEREZEqk0sj7cPdvRewwt2vBw4DmuS3WCIiIiJVJ5cA6av4/0sz2xdYB+yfvyKJiIiIVK1c2iA9a2b1gFuASYAD9+ezUCIiIiJVqcIAycyqAaPcfSXwbzN7Dqjl7p99HYUTERERqQoV3mJz9w3AbYnxtQqOREREZHuXSxukl8zsR1b6fL+IiIjIdi6XNki/AmoDJWb2FeFRf3f3unktmYiIiEgVyeVN2rt9HQURERER+aaoNEAys6MyTXf3Mdu+OCIiIiJVL5dbbL9ODNcCugITgWPyUiIRERGRKpbLLbbuyXEzawLcnLcSiYiIiFSxXJ5iS1sAtN/WBRERERH5psilDdJdhLdnQwioCoEpeSyTiIiISJXKpQ3ShMRwCTDU3V/PU3lEREREqlwuAdKTwFfuvh7AzKqb2a7u/mV+iyYiIiJSNXJpgzQK2CUxvgvwSn6KIyIiIlL1cgmQarn76tKROLxrLpmb2YlmNsvM5pjZNRnm1zez4WY21czGm1n7xLx5ZvaOmRWZ2YTE9D3M7GUzmx3/18+lLCIiIiK5yiVA+sLMDiodMbODgTWVLWRm1YG7gZOAtsDZZtY2lexaoMjdOwK9gDtT849290J375KYdg0wyt1bEmq3Ngm8RERERLZGLgHS5cATZjbWzMYCjwH9cliuKzDH3T9w92JgGHBqKk1bQpCDu88EmplZw0ryPRV4OA4/DJyWQ1lEREREcpbLiyLfNrPWQCtCR7Uz3X1dDnk3BuYnxhcAh6TSTAFOB/5nZl2BpkABsITwaoGXzMyBv7v7fXGZhu6+OJZtsZntnUNZRERERHJWaQ2SmV0C1Hb3ae7+DlDHzH6RQ96WYZqnxgcA9c2sCLgUmEx4lQDAEe5+EOEW3SXZ+oSroNwXmdkEM5uwbNmyzVlUREREdnC53GK70N1Xlo64+wrgwhyWWwA0SYwXAIuSCdx9lbv3dvdCQhukBsDcOG9R/L8UGE64ZQewxMwaAcT/SzOt3N3vc/cu7t6lQYMGORRXREREJMglQKpmZmW1QbHxdc0clnsbaGlm+5tZTeAsYEQygZnVi/MALgDGuPsqM6ttZrvFNLWB44FpMd0I4Nw4fC7wTA5lEREREclZLi+KfBF43MwGEW6R9QVGVraQu5eYWb+4fHXgQXefbmZ94/xBQBtgiJmtB2YAfeLiDYHhMS6rAfzL3UvXOSCWpw/wEXBGTlsqIiIikqNcAqTfABcBFxPaFb0E3J9L5u7+AvBCatqgxPA4oGWG5T4AOmXJ8xPg2FzWLyIiIrIlKr3F5u4b3H2Qu/d09x8B04G78l80ERERkaqRSw0SZlYInA2cSWhE/VQeyyQiIiJSpbIGSGZ2IKFh9dnAJ4QXRJq7H/01lU1ERESkSlRUgzQTGAt0d/c5AGZ2xddSKhEREZEqVFEbpB8BHwP/NbP7zexYMr/8UURERGS7kjVAcvfh7n4m0BoYDVwBNDSze83s+K+pfCIiIiJfu1yeYvvC3R919x8S3oZdBFyT74KJiIiIVJVc3qRdxt0/dfe/u/sx+SqQiIiISFXbrABJREREZEegAElEREQkRQGSiIiISIoCJBEREZEUBUgiIiIiKQqQRERERFIUIImIiIikKEASERERSVGAJCIiIpKiAElEREQkRQGSiIiISIoCJBEREZEUBUgiIiIiKQqQRERERFIUIImIiIikKEASERERSVGAJCIiIpKiAElEREQkRQGSiIiISIoCJBEREZEUBUgiIiIiKQqQRERERFIUIImIiIikKEASERERSVGAJCIiIpKiAElEREQkRQGSiIiISIoCJBEREZEUBUgiIiIiKXkNkMzsRDObZWZzzOyaDPPrm9lwM5tqZuPNrH1qfnUzm2xmzyWm9TezhWZWFP9Ozuc2iIiIyI4nbwGSmVUH7gZOAtoCZ5tZ21Sya4Eid+8I9ALuTM3/JfBuhuxvd/fC+PfCNi66iIiI7ODyWYPUFZjj7h+4ezEwDDg1laYtMArA3WcCzcysIYCZFQA/AB7IYxlFRERENpHPAKkxMD8xviBOS5oCnA5gZl2BpkBBnHcHcDWwIUPe/eJtuQfNrP62LLSIiIhIPgMkyzDNU+MDgPpmVgRcCkwGSszsh8BSd5+YIY97geZAIbAYuC3jys0uMrMJZjZh2bJlW7YFIiIiskPKZ4C0AGiSGC8AFiUTuPsqd+/t7oWENkgNgLnAEcApZjaPcGvuGDP7Z1xmibuvd/cNwP2EW3mbcPf73L2Lu3dp0KDBtt0yERER2a7lM0B6G2hpZvubWU3gLGBEMoGZ1YvzAC4AxsSg6bfuXuDuzeJyr7r7T+MyjRJZ9ACm5XEbREREZAdUI18Zu3uJmfUDXgSqAw+6+3Qz6xvnDwLaAEPMbD0wA+iTQ9Y3m1kh4XbdPODneSi+iIiI7MDyFiABxEfwX0hNG5QYHge0rCSP0cDoxPjPtmkhRURERFL0Jm0RERGRFAVIIiIiIikKkERERERSFCCJiIiIpChAEhEREUlRgCQiIiKSogBJREREJEUBkoiIiEiKAiQRERGRFAVIIiIiIikKkERERERSFCCJiIiIpChAEhEREUlRgCQiIiKSogBJREREJEUBkoiIiEiKAiQRERGRFAVIIiIiIikKkERERERSFCCJiIiIpChAEhEREUlRgCQiIiKSogBJREREJEUBkoiIiEiKAiQRERGRFAVIIiIiIikKkERERERSFCCJiIiIpChAEhEREUlRgCQiIiKSogBJREREJEUBkoiIiEiKAiQRERGRFAVIIiIiIikKkERERERSFCCJiIiIpOQ1QDKzE81slpnNMbNrMsyvb2bDzWyqmY03s/ap+dXNbLKZPZeYtoeZvWxms+P/+vncBhEREdnx5C1AMrPqwN3ASUBb4Gwza5tKdi1Q5O4dgV7Anan5vwTeTU27Bhjl7i2BUXFcREREZJvJZw1SV2COu3/g7sXAMODUVJq2hCAHd58JNDOzhgBmVgD8AHggtcypwMNx+GHgtLyUXkRERHZY+QyQGgPzE+ML4rSkKcDpAGbWFWgKFMR5dwBXAxtSyzR098UA8f/e27TUIiIissOrkce8LcM0T40PAO40syLgHWAyUGJmPwSWuvtEM+u2RSs3uwi4KI6uNrNZW5KPbHN7AcuruhDfFv3796/qIojIRrp+bYY8X7+a5jNzyG+AtABokhgvABYlE7j7KqA3gJkZMDf+nQWcYmYnA7WAumb2T3f/KbDEzBq5+2IzawQszbRyd78PuG8bb5NsJTOb4O5dqrocIiKbS9evHUs+b7G9DbQ0s/3NrCYh6BmRTGBm9eI8gAuAMe6+yt1/6+4F7t4sLvdqDI6IeZwbh88FnsnjNoiIiMgOKG81SO5eYmb9gBeB6sCD7j7dzPrG+YOANsAQM1sPzAD65JD1AOBxM+sDfASckZcNEBERkR2WuaebBYnkj5ldFG9/ioh8q+j6tWNRgCQiIiKSoq5GRERERFIUIG0HzMzN7LbE+FVm1n8zlj/PzJaZWZGZTTezJ81s1y0sy2kZ3pieTtPNzA7fkvy3oDwPVFYeEfl6mNntZnZ5YvxFM3sgMX6bmf3KzE4p7Z4qfU0xs9FmVuGTZGbWzMzWxGvaFDN7w8xabWGZC+MT1ZWt75wtyX8LytPXzHp9Heva0SlA2j6sBU43s722Io/H3L3Q3dsBxcCZW5jPaYQ3pFekG5AxQDKzbfrggLtf4O4ztmWeIrLF3iCe+2ZWjfBeoXaJ+YcDr7v7CHcfEKedRuXXlEzej9e0ToReF67dwjIXAhUGSEAzIGOAlIdr2iB3H7It85TMFCBtH0oI73y6Ij3DzJqa2ajYIfAoM9uvooziyVwbWFHR8pmmx1qhU4Bb4i+35mZ2mZnNiOmGmVkzoC9wRUzzXTN7yMz+amb/BW4ys67xF9/k5C+/WNP1jJmNtNAJ8nVxejMzm2lmD8f1lNWAJX9tmtlqM/tz/EX5ZqJbm+Zx/G0z+6OZrd76j0REMnidjT+O2gHTgM8tdFy+M+HJ5snxXB+Y6ZoSlz3DQgfn75nZd3NYb102XtNqmdlgM3snXmOOzjbdwmto/gicGdd/ppl9Lw4XxXS7EZ6u/m6cdkUs/xNm9izwkpnVidfJSTH/U+M6K7p2zTOzm+J2jjezFnF6fzO7Kg6PTqQp2xdmtquZPR7zfMzM3rJKat0kA3fX37f8D1hNuADMA3YHrgL6x3nPAufG4fOBpzMsfx6wDCgClgBjgeoVLV/B9IeAnom8FwE7x+F68X9/4KpEmoeA5xLrrAvUiMPfB/6dKOdiYE9gF8LFtQvh15sDR8R0D5bmD4wGusRhB7rH4ZuB38Xh54Cz43BfYHVVf6b609/2+hevU/sBP4/n258INTRHEN6FV3quD4zD6WvKaOC2OHwy8EqGdTQD1sRr2vvxurFfnHclMDgOtya8LqZWBdPLyhLnPZu41tQhvC6nG/BcIs15hJcl7xHHawB14/BewBxCbxMVXbvmAf8Xh3uV5k/i+pltXxC+A/4eh9sTfkR3qerP/tv2pxqk7YSHt5IPAS5LzToM+FccfgQ4MksWj7l7IbAPoduXX1eyfK75TgUeNbOfEk7SbJ5w9/VxeHfgCTObBtxO+Sr4l939E3dfAzyVWO98d389Dv8zS3mKCcEQwETCxal0W56Iw/9CRPKptBbpcGBc/CsdfyPHPJ6K/5PncVrpLbbmwOVs7FnhSMI1Cw+dpH8IHFjB9Ezl/6uZXUb40Zftuvayu38ahw34i5lNBV4h9EvaMM6r6No1NPH/sCzrybQvjiR0EI+7TyNch2UzKUDavtxBeNlm7QrSVPheBw8/OZ4FjtrM5bNN/wFwN3AwMNGy34//IjH8J+C/7t4e6E74FZdtPV7J9KR1cfsA1pPfrnZEJLPSdkgdCLXAbxK+/A8nBB+5WBv/53oej2DjNS1TP6EVTS/HQ9uoCwi12G+aWessSZPXtJ8ADYCD4w/RJWy8rlV07co2nJRpX+S0LVIxBUjbkfhr5XHKv5H8DUJ3LRBO0v/lkNWRhGrpipbPNv1zYDcoa4TZxN3/C1wN1CNUSZelyWJ3YGEcPi817zgz28PMdiE03iy9oO5nZqW/sM4mt+0s9Sbwozh8VkUJRWSrvQ78EPjU3dfH61Y9QpA0LkP6yq4XuUhe08YQrlmY2YGE232zKphebv1m1tzd33H3m4AJhNtxuVzTlrr7utjmKdnRakXXrjMT/zPtm2z+B/w4lrctIRiVzaQAaftzG+Eed6nLgN6xavdnwC+zLFfaCHEq0JlQi1PR8tmmDwN+bWaTgZbAP83sHWAycLu7ryTUUPWI68vUwPJm4EYze53QTU3S/wjV4EWEtkkT4vR3gXNjefYA7s2ynZlcDvzKzMYDjYDPNmNZEdk87xCuUW+mpn3m7sszpC+7piQaaeeiebzGTAH+Qqj1AbgHqB6vS48B57n72gqm/xdoW9pIG7jczKbFfNcA/yHcwiqJD4Bs8rAM8CjQxcwmEIKwmYl5FV27djaztwjX10z5ZnMP0CDm+ZtYPl3XNpPepC3fGmZ2HqGhYb/U9GaEBozttzDfXYE17u5mdhahwfapW1teEZGKVHTtMrN5hOtdpqCxsnyrAzu5+1cxqBwFHOjuxVtZ5B2K2mCIhPZRA83MgJWEp/JERL6tdgX+a2Y7EdojXazgaPOpBklEREQkRW2QRERERFIUIImIiIikKEASERERSVGAJDu82B/StNS0sv6OsixT1tv4Nlh/pb2T55hPNwt9V1WUptKeyb8tKvuMttE6Njk2qipfK9/DfcZtT+ZrZl3M7G/bqLynxffp5I2Z9Yh9h800s/vjQxMiVUYBksgW8PK9jX9TdGNjR6DZFJKlZ/IK3nIu3wCbe8y5+wR3T3c9tKVOAzIGSNvwuPmE0B9b2/h3xDbKV2SLKEASqYSZXWZmM+Kv22Fx2nlmNjAOP2RmfzOzN8zsAzPrGadXM7N7zGy6mT1nZi+Uzsvgp3H5aWbWNS6/h5k9Hdf7ppl1zDY9vk+lL3BF6Qs4zeyM0hfamdkYy9wzeX8zu8/MXgKGxBqIsRZ6HZ9UWiMVa6fGmNnwuC8GWXhTenpfDUjsq1vjtO4WehOfbGavmFnDOL2/hV7MX7LQc/npZnazhd7OR8ZHlLP2ap5ab/O4zMRY/tZxerl9kGG5e8zslDg83MwejMN9zOyGmKx6rNGYHsu6SyXrzHg8ZFDDsvfivlcc7mJmo9PHXGobDo7bNw64JDG9m5k9l9jXD1qorfzAQj9ipel+b6HW5mUzG2qpmql4DJwC3BKPm+Yxn7+Y2WvAL2MZXov74kUza7S5n4u7j3H3zwldatQCvsqy30S+HlXdW67+9FfVf4QOHqelpvVnY4/Zi4Cd43C9+P88yvc2/gThB0dbYE6c3hN4IU7fB1hBolfyxLpGA/fH4aNKywLcBVwXh48BiiqZXlbmOP4O0DhbuRPLTAR2ieO7ArXicEtgQhzuRvjCOoDwdvOX09tCeAvwLDa+PqR0nfUT0y5gY+/j/QlvRt8J6AR8CZwU5w0HTovD86i8V/NRQMs4fAjwarZ9kCrzWcAtcXg88GYcHgycQDg2SoDCOP1x4KeVrPMhMhwPGY65inpx3ysOdwFGZzjmkts+FfheHL6FjcdPt9S+egPYmfAW60/ifu9CeCv9LoSuMmaTOIYS5X0o+XkTjtl74vBOMe8GcfxM4MEt/VwIb/F/oqqvC/rTn6rURSrvgHcq8KiZPQ08nSXt0+6+AZhRWkNC6P/piTj9YzP7bwVlGArhV7SZ1TWzenH5H8Xpr5rZnma2ewXT014HHjKzx9nY43cmI9x9TRzeifDSzEJC55fJ3szHu/sHAGY2NJbjycT8VYQg6gEzex54Lk4vAB6LtQo1gbmJZf7joX+qdwiB18g4/R3K99Ke7NX89mThzawO4dbiE7ax2crOOe6DsYSuI9oCM4D6sZyHEbrT2ROY6+5FMf1EoFkl64TMx0Nauhf3y4Bbs6TNKH7u9dz9tTjpEeCkLMmf99B1xlozW0roTf5I4JnSz9/Mnt2M1T8W/7cC2gMvx31RHVi8JZ+LmXUCehACN5EqpQBJJPyarp+atgcbv8h/QKjZOQX4vZm1y5DH2sSwpf7nIlOP3pmWr2h6+Qnufc3sEEL5i2LQk0my1/ErCD2NdyLUgCRvc1TU6zjuXmLh9uCxhJqZfoQarruAv7r7CDPrRqjNKLU2LrvBzNa5e2meGyh/faqoV/NqwEoPvaSXL2CGfeDunyTmLzSz+sCJhM5K9yB08rna3T83sz0p/9muJ9S2ZF1ncruibMdBtv1ZwsbmD7WomGXIJ5v0dtSooGy5KD1uDJju7oclZ5pZXTb/c+kAvObuur0mVU5tkGSH5+6rCb94j4XQxofwhfk/C+1smrj7f4GrCb2O18kx6/8BP7LQFqkh4ZZHNmfGdR9J6LTzM8r3Lt4NWO7uqyqYnqnX8bfc/Q/AcqBJOk0GuwOLY+3HzyjfWXBXM9s/7pMzKd/reGlNzu7u/gKhA+DCRJ4L4/C5Fay7Ill7NY/bPtfMzojlsFgTkW0fpI2L5R1DqFG6Kv7PqqJ1boZsvbjPI3R/A7GmsIJyrAQ+i8cNxONiM/wP6G5mteLn94Ms6So6bmYROkY9DMDMdjKzdlv4ubwO/GMzt0EkLxQgiQS9gN+ZWRHwKnC9u79PCBD+GW8BTQZuj19Kufg3sACYBvwdeIvsPWqvMLM3gEFAnzitP6EH8KnAADYGF9mmPwv0iA1pv0toVPuOhce+xwBT2LRn8rR7CD2Lv0m4vZasXRoX1zeNULs2PLXsbsBzsVyvsbH38f6E2yxjCV+GW6KyXs1/AvSx0MP6dKC0s+FM+yBtLFDD3ecAkwi1SBUGSJWsM1fZenG/Hrgz7q/1OeTTG7jbQiPtNZUlTnL3t4ERhP3yFDCBzMfoMODXFhraN0/lUUxob3dT3BdFbHyacnM/lw7A8ZuzDSL5or7YRPLIzOq4++p4q2Y8oVHux1Vdrs0Va6qucvcfVsG657GFvZpL5RLH6K6EYOUid59U1eUSqWpqgySSX8/FBtc1gT99G4Mj2e7dFxup1wIeVnAkEqgGSURERCRFbZBEREREUhQgiYiIiKQoQBIRERFJUYAkIiIikqIASURERCRFAZKIiIhIyv8DCnvCDRBvwmAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 648x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "decision_trees = np.array([sumForest2/50, sumForest/50])\n",
    "fig = plt.figure(figsize = (9, 5))\n",
    "dt_measures = [\"No Bootstrapping\", \"With Bootstrapping\"]\n",
    "bars4 = plt.bar(dt_measures, decision_trees, color='grey', width=0.5)\n",
    "plt.xlabel(\"Using bootstrap samples when building trees?\")\n",
    "plt.ylabel(\"Accuracy of the model\")\n",
    "plt.title(\"Performance of different split quality criteria for a random forest classifier on the dataset\")\n",
    "plt.ylim(ymin=0.94)\n",
    "plt.ylim(ymax=0.97)\n",
    "for bar in bars4:\n",
    "    yval = bar.get_height()\n",
    "    plt.text(bar.get_x(), yval + 0.001, yval)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# combining all so far\n",
    "allSoFar = np.array([0.9657, 0.9643, 0.9642, 0.9379, 0.9427, 0.9645, 0.9653, 0.9654, 0.9625])\n",
    "fig = plt.figure(figsize = (15, 7))\n",
    "measures = [\"Eucledian k-NN\", \"Manhattan k-NN\", \"Minkowski k-NN\", \"Entropy Decision Tree\", \"Gini Decision Tree\", \\\n",
    "               \"Entropy Random Forest\", \"Gini Random Forest\", \"Bootstrapping Random Forest\", \\\n",
    "               \"No Bootstrapping Random Forest\" ]\n",
    "bars4 = plt.bar(measures, allSoFar, color=['red', 'blue', 'purple', 'green', 'lavender', \"orange\", \"lightblue\", \"olive\", \"cyan\"], width=0.7)\n",
    "plt.xlabel(\"Classification Method\")\n",
    "plt.ylabel(\"Accuracy of the model\")\n",
    "plt.title(\"Performance of different different classifiers on the dataset\")\n",
    "fig.autofmt_xdate()\n",
    "plt.ylim(ymin=0.93)\n",
    "plt.ylim(ymax=0.968)\n",
    "for bar in bars4:\n",
    "    yval = bar.get_height()\n",
    "    plt.text(bar.get_x(), yval + 0.001, yval)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Fourth (4th): Polynomial Kernel SVM classification method"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.87142857 0.95714286 0.92857143 0.91428571 1.         0.95714286\n",
      " 0.94285714 0.98571429 0.97142857 0.91304348]\n",
      "Cross validation scores mean for polynomial kernel SVM classifier: 0.9441614906832297\n",
      "Time elapsed:  0.13155293464660645\n"
     ]
    }
   ],
   "source": [
    "start = time.time()\n",
    "from sklearn.svm import SVC\n",
    "polySVM = SVC(kernel='poly', degree=5)\n",
    "SVMscores = cross_val_score(polySVM, X, y, cv=10)\n",
    "#print each cv score (accuracy) and average them\n",
    "print(SVMscores)\n",
    "print('Cross validation scores mean for polynomial kernel SVM classifier: {}'.format(np.mean(SVMscores)))\n",
    "end = time.time()\n",
    "print('Time elapsed: ', end - start)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "poly_degrees = []\n",
    "poly_scores = []\n",
    "for i in range(1, 11):\n",
    "    polySVM = SVC(kernel='poly', degree=i)\n",
    "    SVMscores = cross_val_score(polySVM, X, y, cv=10)\n",
    "    poly_degrees.append(i)\n",
    "    poly_scores.append(round(np.mean(SVMscores), 4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# all different degrees\n",
    "fig = plt.figure(figsize = (9, 7))\n",
    "bars4 = plt.bar(poly_degrees, poly_scores, color=['red', 'blue', 'purple', 'green', 'lavender', \"orange\", \"lightblue\", \"olive\", \"cyan\", \"skyblue\"], width=0.7)\n",
    "plt.xlabel(\"Polynomial kernel degree\")\n",
    "plt.ylabel(\"Accuracy of the model\")\n",
    "plt.title(\"Performance of polynomial kernel SVM classifiers with different degrees on the dataset\")\n",
    "plt.ylim(ymin=0.93)\n",
    "plt.ylim(ymax=0.973)\n",
    "for bar in bars4:\n",
    "    yval = bar.get_height()\n",
    "    plt.text(bar.get_x(), yval + 0.001, yval)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cross validation scores mean for poly kernel SVM w/ degree=1 classifier: 0.9671221532091098\n",
      "Time elapsed:  0.06444478034973145\n"
     ]
    }
   ],
   "source": [
    "# checking linear kernel vs poly w/ degree=1 just to make sure\n",
    "start = time.time()\n",
    "from sklearn.svm import SVC\n",
    "#polySVM = SVC(kernel='linear')\n",
    "#SVMscores = cross_val_score(polySVM, X, y, cv=10)\n",
    "polySVM2 = SVC(kernel='poly', degree=1)\n",
    "SVMscores2 = cross_val_score(polySVM2, X, y, cv=10)\n",
    "\n",
    "\n",
    "#print('Cross validation scores mean for linear kernel SVM classifier: {}'.format(np.mean(SVMscores)))\n",
    "print('Cross validation scores mean for poly kernel SVM w/ degree=1 classifier: {}'.format(np.mean(SVMscores2)))\n",
    "\n",
    "end = time.time()\n",
    "print('Time elapsed: ', end - start)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Recall: 0.9586666666666668\n",
      "Precision: 0.9502838827838829\n",
      "F1: 0.9531468386685777\n",
      "Accuracy: 0.9671221532091098\n"
     ]
    }
   ],
   "source": [
    "recall = cross_val_score(polySVM2, X, y_binary, cv=10, scoring='recall')\n",
    "print('Recall:', np.mean(recall))\n",
    "precision = cross_val_score(polySVM2, X, y_binary, cv=10, scoring='precision')\n",
    "print('Precision:', np.mean(precision))\n",
    "f1 = cross_val_score(polySVM2, X, y_binary, cv=10, scoring='f1')\n",
    "print('F1:', np.mean(f1))\n",
    "accuracy = cross_val_score(polySVM2, X, y_binary, cv=10, scoring='accuracy')\n",
    "print('Accuracy:', np.mean(accuracy))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Fifth (5th): Gaussian Kernel SVM classification method"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.92857143 0.98571429 0.97142857 0.91428571 0.98571429 0.98571429\n",
      " 0.97142857 1.         0.97142857 0.98550725]\n",
      "Cross validation scores mean for Gaussian kernel SVM classifier: 0.9699792960662525\n",
      "Time elapsed:  0.08167028427124023\n"
     ]
    }
   ],
   "source": [
    "start = time.time()\n",
    "from sklearn.svm import SVC\n",
    "Gaussian_SVM = SVC(kernel='rbf')\n",
    "gauss_scores = cross_val_score(Gaussian_SVM, X, y, cv=10)\n",
    "#print each cv score (accuracy) and average them\n",
    "print(gauss_scores)\n",
    "print('Cross validation scores mean for Gaussian kernel SVM classifier: {}'.format(np.mean(gauss_scores)))\n",
    "end = time.time()\n",
    "print('Time elapsed: ', end - start)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.92857143 0.98571429 0.97142857 0.91428571 0.98571429 0.98571429\n",
      " 0.97142857 1.         0.97142857 0.98550725]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize = (10, 5))\n",
    "folds = range(1, 11)\n",
    "print(gauss_scores)\n",
    "scores = np.array(gauss_scores)\n",
    "percentages = scores * 100\n",
    "percentages = [round(num, 4) for num in percentages]\n",
    "bars = plt.bar(folds, percentages, color='skyblue', width=0.5)\n",
    "plt.xlabel(\"# of the fold\")\n",
    "plt.ylabel(\"% accuracy of the model\")\n",
    "plt.title(\"Performance of each of the 10 folds with cross validation on the dataset (Gaussian kernel)\")\n",
    "for bar in bars:\n",
    "    yval = bar.get_height()\n",
    "    plt.text(bar.get_x(), yval + 1, yval)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# comparing the two kernels\n",
    "fig = plt.figure(figsize = (10, 5))\n",
    "kernels = [\"Polynomial kernel with degree=1\", \"Gaussian kernel (RBF)\"]\n",
    "accs = [0.9671, 0.9700]\n",
    "bars = plt.bar(kernels, accs, color=['orange', 'purple'], width=0.5)\n",
    "plt.xlabel(\"Type of SVM kernel\")\n",
    "plt.ylabel(\"% accuracy of the model\")\n",
    "plt.title(\"Performance of different kernels on the dataset\")\n",
    "plt.ylim(ymin=0.96)\n",
    "plt.ylim(ymax=0.975)\n",
    "for bar in bars:\n",
    "    yval = bar.get_height()\n",
    "    plt.text(bar.get_x(), yval + 0.001, yval)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Recall: 0.9751666666666667\n",
      "Precision: 0.9422838827838828\n",
      "F1: 0.9578406816081587\n",
      "Accuracy: 0.9699792960662525\n"
     ]
    }
   ],
   "source": [
    "recall = cross_val_score(Gaussian_SVM, X, y_binary, cv=10, scoring='recall')\n",
    "print('Recall:', np.mean(recall))\n",
    "precision = cross_val_score(Gaussian_SVM, X, y_binary, cv=10, scoring='precision')\n",
    "print('Precision:', np.mean(precision))\n",
    "f1 = cross_val_score(Gaussian_SVM, X, y_binary, cv=10, scoring='f1')\n",
    "print('F1:', np.mean(f1))\n",
    "accuracy = cross_val_score(Gaussian_SVM, X, y_binary, cv=10, scoring='accuracy')\n",
    "print('Accuracy:', np.mean(accuracy))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 6. deep neural network with Sigmoid activation functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.92857143 0.94285714 0.95714286 0.91428571 1.         0.97142857\n",
      " 0.97142857 1.         0.98571429 1.        ]\n",
      "Cross validation scores mean for NN with Sigmoid function classifier: 0.9671428571428571\n",
      "Time elapsed:  7.157455921173096\n"
     ]
    }
   ],
   "source": [
    "start = time.time()\n",
    "from sklearn.neural_network import MLPClassifier\n",
    "\n",
    "# MLP classifier defaults:\n",
    "# default=100 number of neurons in the ith hidden layer\n",
    "# Activation function for the hidden layer = ReLu\n",
    "# The solver for weight optimization = \"adam\" (refers to a stochastic gradient-based optimizer proposed by \n",
    "# Kingma, Diederik, and Jimmy Ba)\n",
    "# L2 penalty (regularization term) parameter alpha=0.0001\n",
    "# hidden_layer_sizes\n",
    "\n",
    "mlp = MLPClassifier(activation=\"logistic\", max_iter=1000)\n",
    "mlp_scores = cross_val_score(mlp, X, y, cv=10)\n",
    "#print each cv score (accuracy) and average them\n",
    "print(mlp_scores)\n",
    "print('Cross validation scores mean for NN with Sigmoid function classifier: {}'.format(np.mean(mlp_scores)))\n",
    "end = time.time()\n",
    "print('Time elapsed: ', end - start)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Recall: 0.9501666666666665\n",
      "Precision: 0.9505317460317461\n",
      "F1: 0.952047553609335\n",
      "Accuracy: 0.9657142857142859\n"
     ]
    }
   ],
   "source": [
    "recall = cross_val_score(mlp, X, y_binary, cv=10, scoring='recall')\n",
    "print('Recall:', np.mean(recall))\n",
    "precision = cross_val_score(mlp, X, y_binary, cv=10, scoring='precision')\n",
    "print('Precision:', np.mean(precision))\n",
    "f1 = cross_val_score(mlp, X, y_binary, cv=10, scoring='f1')\n",
    "print('F1:', np.mean(f1))\n",
    "accuracy = cross_val_score(mlp, X, y_binary, cv=10, scoring='accuracy')\n",
    "print('Accuracy:', np.mean(accuracy))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [],
   "source": [
    "mlp_layers = []\n",
    "all_mlp_scores = []\n",
    "\n",
    "mlp = MLPClassifier(activation=\"logistic\", max_iter=1000, hidden_layer_sizes=(100,))\n",
    "mlp_scores = cross_val_score(mlp, X, y, cv=10)\n",
    "mlp_layers.append(1)\n",
    "all_mlp_scores.append(round(np.mean(mlp_scores), 4))\n",
    "\n",
    "mlp = MLPClassifier(activation=\"logistic\", max_iter=1000, hidden_layer_sizes=(100,100,))\n",
    "mlp_scores = cross_val_score(mlp, X, y, cv=10)\n",
    "mlp_layers.append(2)\n",
    "all_mlp_scores.append(round(np.mean(mlp_scores), 4))\n",
    "\n",
    "mlp = MLPClassifier(activation=\"logistic\", max_iter=1000, hidden_layer_sizes=(100,100,100,))\n",
    "mlp_scores = cross_val_score(mlp, X, y, cv=10)\n",
    "mlp_layers.append(3)\n",
    "all_mlp_scores.append(round(np.mean(mlp_scores), 4))\n",
    "\n",
    "mlp = MLPClassifier(activation=\"logistic\", max_iter=1000, hidden_layer_sizes=(100,100,100,100,))\n",
    "mlp_scores = cross_val_score(mlp, X, y, cv=10)\n",
    "mlp_layers.append(4)\n",
    "all_mlp_scores.append(round(np.mean(mlp_scores), 4))\n",
    "\n",
    "mlp = MLPClassifier(activation=\"logistic\", max_iter=1000, hidden_layer_sizes=(100,100,100,100,100,))\n",
    "mlp_scores = cross_val_score(mlp, X, y, cv=10)\n",
    "mlp_layers.append(5)\n",
    "all_mlp_scores.append(round(np.mean(mlp_scores), 4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "All NN scores for layers:  [0.9643, 0.9686, 0.9686, 0.9686, 0.9657]\n",
      "# of layers:  [1, 2, 3, 4, 5]\n"
     ]
    }
   ],
   "source": [
    "print(\"All NN scores for layers: \", all_mlp_scores)\n",
    "print(\"# of layers: \", mlp_layers)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# all different layers\n",
    "fig = plt.figure(figsize = (9, 7))\n",
    "bars4 = plt.bar(mlp_layers, all_mlp_scores, color=['red', 'blue', 'purple', 'green', 'lavender', \"orange\", \"lightblue\", \"olive\", \"cyan\", \"skyblue\"], width=0.7)\n",
    "plt.xlabel(\"Number of hidden layers in the neural network\")\n",
    "plt.ylabel(\"Accuracy of the model\")\n",
    "plt.title(\"Performance of deep NN with sigmoid function with different number of hidden layers on the dataset\")\n",
    "plt.ylim(ymin=0.93)\n",
    "plt.ylim(ymax=0.975)\n",
    "for bar in bars4:\n",
    "    yval = bar.get_height()\n",
    "    plt.text(bar.get_x(), yval + 0.001, yval)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [],
   "source": [
    "mlp_units = []\n",
    "unit_scores = []\n",
    "\n",
    "for i in range(10, 110, 10):\n",
    "    mlp = MLPClassifier(activation=\"logistic\", max_iter=1000, hidden_layer_sizes=(i,))\n",
    "    mlp_scores = cross_val_score(mlp, X, y, cv=10)\n",
    "    mlp_units.append(i)\n",
    "    unit_scores.append(round(np.mean(mlp_scores), 4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[10, 20, 30, 40, 50, 60, 70, 80, 90, 100]\n",
      "[0.9686, 0.9629, 0.9643, 0.9643, 0.97, 0.9629, 0.9657, 0.9643, 0.9657, 0.9686]\n"
     ]
    }
   ],
   "source": [
    "print(mlp_units)\n",
    "print(unit_scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# all different units\n",
    "fig = plt.figure(figsize = (9, 7))\n",
    "bars4 = plt.bar(mlp_units, unit_scores, color=['red', 'blue', 'purple', 'green', 'lavender', \"orange\", \"lightblue\", \"olive\", \"cyan\", \"skyblue\"], width=7)\n",
    "plt.xlabel(\"Number of units in a hidden layer in the neural network\")\n",
    "plt.ylabel(\"Accuracy of the model\")\n",
    "plt.title(\"Performance of deep NN with sigmoid function with different number of units in a hidden layer on the dataset\")\n",
    "plt.ylim(ymin=0.93)\n",
    "plt.ylim(ymax=0.975)\n",
    "for bar in bars4:\n",
    "    yval = bar.get_height()\n",
    "    plt.text(bar.get_x(), yval + 0.001, yval)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [],
   "source": [
    "# weight solver\n",
    "mlp_weights = [\"lbfgs\", \"sgd\", \"adam\"]\n",
    "weight_scores = []\n",
    "\n",
    "mlp = MLPClassifier(activation=\"logistic\", max_iter=1000, hidden_layer_sizes=(50,), solver=\"lbfgs\")\n",
    "mlp_scores = cross_val_score(mlp, X, y, cv=10)\n",
    "weight_scores.append(round(np.mean(mlp_scores), 4))\n",
    "\n",
    "mlp = MLPClassifier(activation=\"logistic\", max_iter=1000, hidden_layer_sizes=(50,), solver=\"sgd\")\n",
    "mlp_scores = cross_val_score(mlp, X, y, cv=10)\n",
    "weight_scores.append(round(np.mean(mlp_scores), 4))\n",
    "\n",
    "mlp = MLPClassifier(activation=\"logistic\", max_iter=1000, hidden_layer_sizes=(50,), solver=\"adam\")\n",
    "mlp_scores = cross_val_score(mlp, X, y, cv=10)\n",
    "weight_scores.append(round(np.mean(mlp_scores), 4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.927, 0.9571, 0.97]\n"
     ]
    }
   ],
   "source": [
    "print(weight_scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# all different units\n",
    "fig = plt.figure(figsize = (9, 7))\n",
    "bars4 = plt.bar(mlp_weights, weight_scores, color=['red', 'blue', 'purple', 'green', 'lavender', \"orange\", \"lightblue\", \"olive\", \"cyan\", \"skyblue\"], width=0.5)\n",
    "plt.xlabel(\"Type of solver for weight optimization\")\n",
    "plt.ylabel(\"Accuracy of the model\")\n",
    "plt.title(\"Performance of deep NN with sigmoid function with different weight optimization solvers on the dataset\")\n",
    "plt.ylim(ymin=0.92)\n",
    "plt.ylim(ymax=0.975)\n",
    "for bar in bars4:\n",
    "    yval = bar.get_height()\n",
    "    plt.text(bar.get_x(), yval + 0.001, yval)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [],
   "source": [
    "# different alphas\n",
    "alpha1 = 0.0001\n",
    "alpha2 = 0.0005\n",
    "alpha3 = 0.001\n",
    "alpha4 = 0.005\n",
    "alpha5 = 0.01\n",
    "\n",
    "alphas = [alpha1, alpha2, alpha3, alpha4, alpha5]\n",
    "alpha_scores = []\n",
    "\n",
    "mlp = MLPClassifier(activation=\"logistic\", max_iter=1000, hidden_layer_sizes=(50,), alpha=alpha1)\n",
    "mlp_scores = cross_val_score(mlp, X, y, cv=10)\n",
    "alpha_scores.append(round(np.mean(mlp_scores), 4))\n",
    "\n",
    "mlp = MLPClassifier(activation=\"logistic\", max_iter=1000, hidden_layer_sizes=(50,), alpha=alpha2)\n",
    "mlp_scores = cross_val_score(mlp, X, y, cv=10)\n",
    "alpha_scores.append(round(np.mean(mlp_scores), 4))\n",
    "\n",
    "mlp = MLPClassifier(activation=\"logistic\", max_iter=1000, hidden_layer_sizes=(50,), alpha=alpha3)\n",
    "mlp_scores = cross_val_score(mlp, X, y, cv=10)\n",
    "alpha_scores.append(round(np.mean(mlp_scores), 4))\n",
    "\n",
    "mlp = MLPClassifier(activation=\"logistic\", max_iter=1000, hidden_layer_sizes=(50,), alpha=alpha4)\n",
    "mlp_scores = cross_val_score(mlp, X, y, cv=10)\n",
    "alpha_scores.append(round(np.mean(mlp_scores), 4))\n",
    "\n",
    "mlp = MLPClassifier(activation=\"logistic\", max_iter=1000, hidden_layer_sizes=(50,), alpha=alpha5)\n",
    "mlp_scores = cross_val_score(mlp, X, y, cv=10)\n",
    "alpha_scores.append(round(np.mean(mlp_scores), 4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.9671, 0.9657, 0.9643, 0.9671, 0.9657]\n"
     ]
    }
   ],
   "source": [
    "print(alpha_scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [],
   "source": [
    "# learning_rate_init\n",
    "gamma1 = 0.001\n",
    "gamma2 = 0.005\n",
    "gamma3 = 0.01\n",
    "gamma4 = 0.05\n",
    "gamma5 = 0.1\n",
    "\n",
    "gammas = [gamma1, gamma2, gamma3, gamma4, gamma5]\n",
    "gamma_scores = []\n",
    "\n",
    "mlp = MLPClassifier(activation=\"logistic\", max_iter=1000, hidden_layer_sizes=(50,), learning_rate_init=gamma1)\n",
    "mlp_scores = cross_val_score(mlp, X, y, cv=10)\n",
    "gamma_scores.append(round(np.mean(mlp_scores), 4))\n",
    "\n",
    "mlp = MLPClassifier(activation=\"logistic\", max_iter=1000, hidden_layer_sizes=(50,), learning_rate_init=gamma2)\n",
    "mlp_scores = cross_val_score(mlp, X, y, cv=10)\n",
    "gamma_scores.append(round(np.mean(mlp_scores), 4))\n",
    "\n",
    "mlp = MLPClassifier(activation=\"logistic\", max_iter=1000, hidden_layer_sizes=(50,), learning_rate_init=gamma3)\n",
    "mlp_scores = cross_val_score(mlp, X, y, cv=10)\n",
    "gamma_scores.append(round(np.mean(mlp_scores), 4))\n",
    "\n",
    "mlp = MLPClassifier(activation=\"logistic\", max_iter=1000, hidden_layer_sizes=(50,), learning_rate_init=gamma4)\n",
    "mlp_scores = cross_val_score(mlp, X, y, cv=10)\n",
    "gamma_scores.append(round(np.mean(mlp_scores), 4))\n",
    "\n",
    "mlp = MLPClassifier(activation=\"logistic\", max_iter=1000, hidden_layer_sizes=(50,), learning_rate_init=gamma5)\n",
    "mlp_scores = cross_val_score(mlp, X, y, cv=10)\n",
    "gamma_scores.append(round(np.mean(mlp_scores), 4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.9671, 0.96, 0.96, 0.96, 0.9486]\n"
     ]
    }
   ],
   "source": [
    "print(gamma_scores)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Seventh (7th): ReLu activation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.88571429 0.92857143 0.94285714 0.91428571 1.         0.98571429\n",
      " 0.97142857 1.         1.         0.98550725]\n",
      "Cross validation scores mean for NN with ReLu function classifier: 0.9614078674948242\n",
      "Time elapsed:  8.22105598449707\n"
     ]
    }
   ],
   "source": [
    "start = time.time()\n",
    "from sklearn.neural_network import MLPClassifier\n",
    "\n",
    "# MLP classifier defaults:\n",
    "# default=100 number of neurons in the ith hidden layer\n",
    "# Activation function for the hidden layer = ReLu\n",
    "# The solver for weight optimization = \"adam\" (refers to a stochastic gradient-based optimizer proposed by \n",
    "# Kingma, Diederik, and Jimmy Ba)\n",
    "# L2 penalty (regularization term) parameter alpha=0.0001\n",
    "# hidden_layer_sizes\n",
    "\n",
    "mlp = MLPClassifier(activation=\"relu\", max_iter=1000)\n",
    "mlp_scores = cross_val_score(mlp, X, y, cv=10)\n",
    "#print each cv score (accuracy) and average them\n",
    "print(mlp_scores)\n",
    "print('Cross validation scores mean for NN with ReLu function classifier: {}'.format(np.mean(mlp_scores)))\n",
    "end = time.time()\n",
    "print('Time elapsed: ', end - start)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Recall: 0.9418333333333335\n",
      "Precision: 0.9512738927738928\n",
      "F1: 0.9404955826737906\n",
      "Accuracy: 0.9614078674948242\n"
     ]
    }
   ],
   "source": [
    "recall = cross_val_score(mlp, X, y_binary, cv=10, scoring='recall')\n",
    "print('Recall:', np.mean(recall))\n",
    "precision = cross_val_score(mlp, X, y_binary, cv=10, scoring='precision')\n",
    "print('Precision:', np.mean(precision))\n",
    "f1 = cross_val_score(mlp, X, y_binary, cv=10, scoring='f1')\n",
    "print('F1:', np.mean(f1))\n",
    "accuracy = cross_val_score(mlp, X, y_binary, cv=10, scoring='accuracy')\n",
    "print('Accuracy:', np.mean(accuracy))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [],
   "source": [
    "mlp_layers = []\n",
    "all_mlp_scores = []\n",
    "\n",
    "mlp = MLPClassifier(activation=\"relu\", max_iter=1000, hidden_layer_sizes=(100,))\n",
    "mlp_scores = cross_val_score(mlp, X, y, cv=10)\n",
    "mlp_layers.append(1)\n",
    "all_mlp_scores.append(round(np.mean(mlp_scores), 4))\n",
    "\n",
    "mlp = MLPClassifier(activation=\"relu\", max_iter=1000, hidden_layer_sizes=(100,100,))\n",
    "mlp_scores = cross_val_score(mlp, X, y, cv=10)\n",
    "mlp_layers.append(2)\n",
    "all_mlp_scores.append(round(np.mean(mlp_scores), 4))\n",
    "\n",
    "mlp = MLPClassifier(activation=\"relu\", max_iter=1000, hidden_layer_sizes=(100,100,100,))\n",
    "mlp_scores = cross_val_score(mlp, X, y, cv=10)\n",
    "mlp_layers.append(3)\n",
    "all_mlp_scores.append(round(np.mean(mlp_scores), 4))\n",
    "\n",
    "mlp = MLPClassifier(activation=\"relu\", max_iter=1000, hidden_layer_sizes=(100,100,100,100,))\n",
    "mlp_scores = cross_val_score(mlp, X, y, cv=10)\n",
    "mlp_layers.append(4)\n",
    "all_mlp_scores.append(round(np.mean(mlp_scores), 4))\n",
    "\n",
    "mlp = MLPClassifier(activation=\"relu\", max_iter=1000, hidden_layer_sizes=(100,100,100,100,100,))\n",
    "mlp_scores = cross_val_score(mlp, X, y, cv=10)\n",
    "mlp_layers.append(5)\n",
    "all_mlp_scores.append(round(np.mean(mlp_scores), 4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# all different layers\n",
    "fig = plt.figure(figsize = (9, 7))\n",
    "bars4 = plt.bar(mlp_layers, all_mlp_scores, color=['red', 'blue', 'purple', 'green', 'lavender', \"orange\", \"lightblue\", \"olive\", \"cyan\", \"skyblue\"], width=0.7)\n",
    "plt.xlabel(\"Number of hidden layers in the neural network\")\n",
    "plt.ylabel(\"Accuracy of the model\")\n",
    "plt.title(\"Performance of deep NN with ReLu activation function with different number of hidden layers on the dataset\")\n",
    "plt.ylim(ymin=0.93)\n",
    "plt.ylim(ymax=0.97)\n",
    "for bar in bars4:\n",
    "    yval = bar.get_height()\n",
    "    plt.text(bar.get_x(), yval + 0.001, yval)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [],
   "source": [
    "mlp_units = []\n",
    "unit_scores = []\n",
    "\n",
    "for i in range(10, 110, 10):\n",
    "    mlp = MLPClassifier(activation=\"relu\", max_iter=1000, hidden_layer_sizes=(i,))\n",
    "    mlp_scores = cross_val_score(mlp, X, y, cv=10)\n",
    "    mlp_units.append(i)\n",
    "    unit_scores.append(round(np.mean(mlp_scores), 4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# all different units\n",
    "fig = plt.figure(figsize = (9, 7))\n",
    "bars4 = plt.bar(mlp_units, unit_scores, color=['red', 'blue', 'purple', 'green', 'lavender', \"orange\", \"lightblue\", \"olive\", \"cyan\", \"skyblue\"], width=7)\n",
    "plt.xlabel(\"Number of units in a hidden layer in the neural network\")\n",
    "plt.ylabel(\"Accuracy of the model\")\n",
    "plt.title(\"Performance of deep NN with ReLu function with different number of units in a hidden layer on the dataset\")\n",
    "plt.ylim(ymin=0.93)\n",
    "plt.ylim(ymax=0.972)\n",
    "for bar in bars4:\n",
    "    yval = bar.get_height()\n",
    "    plt.text(bar.get_x(), yval + 0.001, yval)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/nurzhan/opt/anaconda3/lib/python3.8/site-packages/sklearn/neural_network/_multilayer_perceptron.py:582: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (1000) reached and the optimization hasn't converged yet.\n",
      "  warnings.warn(\n"
     ]
    }
   ],
   "source": [
    "# weight solver\n",
    "mlp_weights = [\"lbfgs\", \"sgd\", \"adam\"]\n",
    "weight_scores = []\n",
    "\n",
    "mlp = MLPClassifier(activation=\"relu\", max_iter=1500, hidden_layer_sizes=(50,), solver=\"lbfgs\")\n",
    "mlp_scores = cross_val_score(mlp, X, y, cv=10)\n",
    "weight_scores.append(round(np.mean(mlp_scores), 4))\n",
    "\n",
    "mlp = MLPClassifier(activation=\"relu\", max_iter=1500, hidden_layer_sizes=(50,), solver=\"sgd\")\n",
    "mlp_scores = cross_val_score(mlp, X, y, cv=10)\n",
    "weight_scores.append(round(np.mean(mlp_scores), 4))\n",
    "\n",
    "mlp = MLPClassifier(activation=\"relu\", max_iter=1500, hidden_layer_sizes=(50,), solver=\"adam\")\n",
    "mlp_scores = cross_val_score(mlp, X, y, cv=10)\n",
    "weight_scores.append(round(np.mean(mlp_scores), 4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# all different units\n",
    "fig = plt.figure(figsize = (9, 7))\n",
    "bars4 = plt.bar(mlp_weights, weight_scores, color=['red', 'blue', 'purple', 'green', 'lavender', \"orange\", \"lightblue\", \"olive\", \"cyan\", \"skyblue\"], width=0.5)\n",
    "plt.xlabel(\"Type of solver for weight optimization\")\n",
    "plt.ylabel(\"Accuracy of the model\")\n",
    "plt.title(\"Performance of deep NN with sigmoid function with different weight optimization solvers on the dataset\")\n",
    "plt.ylim(ymin=0.92)\n",
    "plt.ylim(ymax=0.97)\n",
    "for bar in bars4:\n",
    "    yval = bar.get_height()\n",
    "    plt.text(bar.get_x(), yval + 0.001, yval)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### FINAL COMPARISON"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# combining all so far\n",
    "allSoFar = np.array([0.9657, 0.9643, 0.9642, 0.9379, 0.9427, 0.9645, 0.9653, 0.9654, 0.9625, 0.9671, 0.97, 0.97, 0.96])\n",
    "fig = plt.figure(figsize = (15, 7))\n",
    "measures = [\"Eucledian k-NN\", \"Manhattan k-NN\", \"Minkowski k-NN\", \"Entropy Decision Tree\", \"Gini Decision Tree\", \\\n",
    "               \"Entropy Random Forest\", \"Gini Random Forest\", \"Bootstrapping Random Forest\", \\\n",
    "               \"No Bootstrapping Random Forest\", \"Polynomial kernel SVM (degree=1)\", \"Gaussian kernel SVM (RBF)\", \\\n",
    "               \"Sigmoid NN (50 units in hidden layer)\", \"ReLu NN (1 hidden layer w/ 100 units)\"]\n",
    "bars4 = plt.bar(measures, allSoFar, color=['red', 'blue', 'purple', 'green', 'lavender', \"orange\", \"lightblue\", \"olive\", \"cyan\", \"brown\", \"skyblue\"], width=0.7)\n",
    "plt.xlabel(\"Classification Method\")\n",
    "plt.ylabel(\"Accuracy of the model\")\n",
    "plt.title(\"Performance of different classifiers on the dataset\")\n",
    "fig.autofmt_xdate()\n",
    "plt.ylim(ymin=0.93)\n",
    "plt.ylim(ymax=0.975)\n",
    "for bar in bars4:\n",
    "    yval = bar.get_height()\n",
    "    plt.text(bar.get_x(), yval + 0.001, yval)\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}