{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "0be8b82c",
   "metadata": {},
   "source": [
    "## Package loading"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "35708b58",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.neural_network import MLPClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.svm import SVC\n",
    "from sklearn import svm\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.neighbors import RadiusNeighborsClassifier\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics import classification_report\n",
    "from sklearn.metrics import f1_score\n",
    "from sklearn.metrics import balanced_accuracy_score\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import plot_confusion_matrix\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.metrics import accuracy_score\n",
    "from sklearn.linear_model import Perceptron\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from IPython.core.interactiveshell import InteractiveShell\n",
    "%matplotlib inline\n",
    "plt.rcParams['figure.dpi'] = 100  # adjust fig size in notebook\n",
    "InteractiveShell.ast_node_interactivity = \"all\"   # allows for multiple outputs per cell to be shown in notebook\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7e0bdb63",
   "metadata": {},
   "source": [
    "## Data loading and subsetting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "dd6f69bd",
   "metadata": {},
   "outputs": [],
   "source": [
    "# load feature data\n",
    "df = pd.read_csv(\"C:/Users/franz/Documents/Bachelor Studium/BachelorArbeit/JupyterLabBA/files/DLCfiles_final_results/res_file.csv\")\n",
    "df = df[df[\"distance\"] != \"not_specified\"]\n",
    "\n",
    "# subsetting data\n",
    "MOPstroke_BL = df[df[\"group\"] == \"MOPstroke_BL\"].copy()\n",
    "MOPstroke_P3 = df[df[\"group\"] == \"MOPstroke_P3\"].copy()\n",
    "MOPstroke_P28 = df[df[\"group\"] == \"MOPstroke_P28\"].copy()\n",
    "MOSstroke_BL = df[df[\"group\"] == \"MOSstroke_BL\"].copy()\n",
    "MOSstroke_P3 = df[df[\"group\"] == \"MOSstroke_P3\"].copy()\n",
    "MOSstroke_P28 = df[df[\"group\"] == \"MOSstroke_P28\"].copy()\n",
    "WMstroke_BL = df[df[\"group\"] == \"WMstroke_BL\"].copy()\n",
    "WMstroke_P3 = df[df[\"group\"] == \"WMstroke_P3\"].copy()\n",
    "WMstroke_P28 = df[df[\"group\"] == \"WMstroke_P28\"].copy()\n",
    "MOPMOSsham_BL = df[df[\"group\"] == \"MOPMOSsham_BL\"].copy()\n",
    "MOPMOSsham_P3 = df[df[\"group\"] == \"MOPMOSsham_P3\"].copy()\n",
    "MOPMOSsham_P28 = df[df[\"group\"] == \"MOPMOSsham_P28\"].copy()\n",
    "WMsham_BL = df[df[\"group\"] == \"WMsham_BL\"].copy()\n",
    "WMsham_P3 = df[df[\"group\"] == \"WMsham_P3\"].copy()\n",
    "WMsham_P28 = df[df[\"group\"] == \"WMsham_P28\"].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "7149c489",
   "metadata": {},
   "outputs": [],
   "source": [
    "# name two or three files to compare with classifier\n",
    "df1 = WMsham_BL\n",
    "df2 = WMsham_P3\n",
    "df3 = pd.DataFrame()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "97809f2f",
   "metadata": {},
   "source": [
    "## Data pre-processing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "e6283614",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "0.0"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check for NaN values\n",
    "df1.isnull().sum().sum()\n",
    "df2.isnull().sum().sum()\n",
    "df3.isnull().sum().sum()\n",
    "\n",
    "# drop rows with missing values\n",
    "df1.dropna(inplace=True)\n",
    "df2.dropna(inplace=True)\n",
    "df3.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "d23d669e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "WMsham_P3    42\n",
       "WMsham_BL    39\n",
       "Name: group, dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# concat dataframes\n",
    "df = pd.concat([df1, df2, df3])\n",
    "df[\"group\"].value_counts()\n",
    "\n",
    "# remove unnecessary columns\n",
    "df = df.drop(['file_name', 'reason_end'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "861b80f8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LabelEncoder()"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "array([ 9,  9,  9,  9,  9,  9,  9,  9,  9,  9,  9,  9,  9,  9,  9,  9,  9,\n",
       "        9,  9,  9,  9,  9,  9,  9,  9,  9,  9,  9,  9,  9,  9,  9,  9,  9,\n",
       "        9,  9,  9,  9,  9, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11,\n",
       "       11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11,\n",
       "       11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# separate data in response and feature variables\n",
    "X = df.drop(\"group\", axis=1)\n",
    "y = df[\"group\"]\n",
    "# make sure all series in X are numeric\n",
    "X = X.apply(pd.to_numeric)\n",
    "# encode labels in y as numerals\n",
    "le = LabelEncoder()\n",
    "le.fit([\"MOPstroke_BL\", \"MOPstroke_P3\", \"MOPstroke_P28\", \"MOSstroke_BL\", \"MOSstroke_P3\", \"MOSstroke_P28\", \"WMstroke_BL\", \"WMstroke_P3\", \"WMstroke_P28\", \"MOPMOSsham_BL\", \"MOPMOSsham_P3\", \"MOPMOSsham_P28\", \"WMsham_BL\", \"WMsham_P3\", \"WMsham_P28\"])\n",
    "y = le.transform(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "cc4cf0cc",
   "metadata": {},
   "outputs": [],
   "source": [
    "# split data in train and test set\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)\n",
    "# Scaling data\n",
    "sc = StandardScaler()\n",
    "X_train = sc.fit_transform(X_train)\n",
    "X_test = sc.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "60d50376",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Automatic parameter search?\n",
    "# https://scikit-learn.org/stable/getting_started.html"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f2cfe588",
   "metadata": {},
   "source": [
    "## Machine Learning Classification\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "caea1270",
   "metadata": {},
   "source": [
    "### Random Forest Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "9304239f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           9       0.64      1.00      0.78         7\n",
      "          11       1.00      0.60      0.75        10\n",
      "\n",
      "    accuracy                           0.76        17\n",
      "   macro avg       0.82      0.80      0.76        17\n",
      "weighted avg       0.85      0.76      0.76        17\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x1dfe99500a0>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x400 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x800 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Random Forest Classifier\n",
    "rfc = RandomForestClassifier(n_estimators=2000, bootstrap=True).fit(X_train, y_train)\n",
    "pred_rfc = rfc.predict(X_test)\n",
    "print(classification_report(y_test, pred_rfc))\n",
    "plot_confusion_matrix(rfc, X_test, y_test, display_labels=le.inverse_transform(rfc.classes_), cmap=plt.cm.Blues, normalize='true')\n",
    "scoring_RF = [accuracy_score(y_test, pred_rfc), balanced_accuracy_score(y_test, pred_rfc), f1_score(y_test, pred_rfc, average='weighted')]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "49d26842",
   "metadata": {},
   "source": [
    "### Support-Vector Machine"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "b58690b2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           9       0.64      1.00      0.78         7\n",
      "          11       1.00      0.60      0.75        10\n",
      "\n",
      "    accuracy                           0.76        17\n",
      "   macro avg       0.82      0.80      0.76        17\n",
      "weighted avg       0.85      0.76      0.76        17\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x1dfe98ece20>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x400 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# SVM Classifier\n",
    "sv = svm.SVC(kernel = 'linear').fit(X_train, y_train)\n",
    "pred_sv = sv.predict(X_test)\n",
    "print(classification_report(y_test, pred_sv))\n",
    "\n",
    "plot_confusion_matrix(sv, X_test, y_test, display_labels=le.inverse_transform(sv.classes_), cmap=plt.cm.Blues, normalize='true')\n",
    "scoring_SV = [accuracy_score(y_test, pred_sv), balanced_accuracy_score(y_test, pred_sv), f1_score(y_test, pred_sv, average='weighted')]\n",
    "#plot_feature_importances(sv.coef[0], labels)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bd12d899",
   "metadata": {},
   "source": [
    "### Multi-layer Perceptron Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "e2b1d80a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           9       0.64      1.00      0.78         7\n",
      "          11       1.00      0.60      0.75        10\n",
      "\n",
      "    accuracy                           0.76        17\n",
      "   macro avg       0.82      0.80      0.76        17\n",
      "weighted avg       0.85      0.76      0.76        17\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x1dfe98e09a0>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x400 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Neural Network\n",
    "mlpc = MLPClassifier(hidden_layer_sizes=(8,8), max_iter=1000, learning_rate_init = 0.001).fit(X_train, y_train)\n",
    "pred_mlpc = mlpc.predict(X_test)\n",
    "print(classification_report(y_test, pred_mlpc))\n",
    "\n",
    "plot_confusion_matrix(mlpc, X_test, y_test, display_labels=le.inverse_transform(mlpc.classes_), cmap=plt.cm.Blues, normalize='true')\n",
    "scoring_MLPC = [accuracy_score(y_test, pred_mlpc), balanced_accuracy_score(y_test, pred_mlpc), f1_score(y_test, pred_mlpc, average='weighted')]\n",
    "# find out how to get feature importances\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "a34518cf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1dfeb6af0d0>]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Loss Curve')"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(mlpc.loss_curve_)\n",
    "plt.title(\"Loss Curve\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9082337c",
   "metadata": {},
   "source": [
    "### Perceptron"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "ee1df400",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           9       0.70      1.00      0.82         7\n",
      "          11       1.00      0.70      0.82        10\n",
      "\n",
      "    accuracy                           0.82        17\n",
      "   macro avg       0.85      0.85      0.82        17\n",
      "weighted avg       0.88      0.82      0.82        17\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x1dfeb673580>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Olv8N4Ztl1gvAQd/MzOpWB+f0XyvadS4wtm1d9QF2B/6tqO7dwL7tnOLTwBPAdyQdDywGbgN+GBFLy+1nWUE/IrYpt0EzM7MmNgRozb0uleVvAvQCZheVzwY2b6fdbYH9gWXAUVkbVwLvA8qe1696Tj/7prINMDkiVlbbjpmZWU+i7L9Kj8m0RsTCMg+L1ZpZvaygJdt3XEQsAJB0FvDfkr5WbrZf8ep9Sf0kXQcsAV4Ahmbll0v6bqXtmZmZ9SSFhXyVbhWYC6xi9ax+EKtn/wVvAK8XAn7mJdIXhSHlnriaS/YuBD4CjCQNMxTcAxxbRXtmZmY9RldfshcR7wBPAqOKdo0CHmrnsAeBLSQNyJVtD7zL6usI2lVN0D8S+OeI+BtthyFeBLaroj0zM7Meo7CQr9KtQpcAJ0s6SdIwSZeSRs6vTn3QhZJuzNW/GZgH3CBpJ0kHABcD13f6Qr4imwJzSpT3p/25CDMzs7rQHbfhjYhbJG0M/AgYDDwPHB4Rr2ZVBpNNn2f1F0kaBfyctIp/Hum6/R9Uct5qgv7jwCezE8M/Av0pwMNVtGdmZtZjdNdteCPiStIK/FL7Rpcom8DqUwIVqSbonwP8n6SdsuPPlPQhYB/SjQPMzMysB6p4Tj8iHgL2A/oBk4FDSKsN94mIJzu3e2ZmZt2ru+69XwtVXacfEc8BJ3RyX8zMzGqukZ+yV1XQl9SLdEegYaQ5/ZeAW32THjMzq3fdsZCvVioO+pJ2Bm4l3VRgYla8PfCmpE9nowBmZmZ1SVDh/fgqr18r1Vynfy3pTnxDImK3iNgN2Ap4Fvh/ndk5MzOz7uY5/bY+AuwREW8XCiLibUnfJ13OZ2ZmZj1QNZn+RGCzEuWDgEkd646ZmVltdcO992umrExf0vq5l98DLpc0FngkK9ubdFehszu1d2ZmZt2smuH6Rhven0/bW+yKdPu/yL0G+BPpGcFmZmZ1q05ieMXKDfoHdWkvzMzMeoimz/QjYnxXd8TMzKwnqGaOvqHm9EuR1I/0BKA++fKIeLajnTIzM6uVps/08yRtCtwAHNZOFc/pm5mZ9UDVXLJ3GbARacX+UuATpPvwvwJ8utN6ZmZmVgOqcqsH1QzvHwx8JiIel/Qu8GpE/EXSQtJjd2/v1B6amZl1o0a+9341mX5/YE7281vAptnPzwG7dUanzMzMaqXwlL1Kt3pQ7R35dsh+fgY4VdKWwGnAG53ULzMzs5rwvffbugwYnP18LnAXcBzwDjC6U3plZmZWI9Vk7nUS8ysP+hHxm9zPT0t6P7AjMD0i5nZi38zMzKwTVX2dfkFELAGe6oS+mJmZ1VwjL+Qr94E7l5TbYEScVX13zMzMasvD+zC8zHqx9ipmZmY9V9PfkS8i/MCdBvWba86m34CBte6GNZi9fnJPrbtgDWjV8sXdcp4WKr+0rZpL4Wqhw3P6ZmZmjaTpM30zM7NmoSqeslcnMb9uRiTMzMysg5zpm5mZ5bRUkelXWr9WHPTNzMxyGnlOv6rhfUnHS3pQ0kxJW2dlYyR9pnO7Z2Zm1r0KmX6lWz2oOOhLOh24BLgD2BDole2aD4zppH6ZmZnVhJ+y19bXgVMi4gJgVa78CWCXTumVmZlZjRRuw1vpVg+qCfrbAE+XKF8O9O9Yd8zMzKyrVBP0pwK7lig/DHixQ70xMzOrsZYqt3pQzer9i4ErJK0LCNhL0heAc4CTO7NzZmZm3c0P3MmJiBskrQP8FOgH3Ay8DpwZEb/t5P6ZmZl1qxaqeLQu9RH1q7pOPyKuAa6RtAnQEhFzOrdbZmZmteFMvx0RMbezOmJmZtYT+I58OZKmAtHe/ojYtkM9MjMzq6H0wJ1K78jXRZ3pZNVk+pcVve4NDAc+QVrkZ2ZmZj1QNQv5/qNUuaSvAXt0uEdmZmY11Mhz+p15aeGdwNGd2J6ZmVm3a+R773fmU/aOAd7qxPbMzMy6nbL/Kj2mHlSzkO9p2i7kE7A5sClwRif1y8zMrCa8er+tPxa9fhd4E7gvIiZ0uEdmZmY15KCfye7ENw24KyJmdUmPzMzMakgSqviSvfqI+hUt5IuIlcBVQN+u6Y6ZmZl1lWqG9x8lXZf/aif3xczMrOY8vN/WlcDPJA0BngQW53dGxLOd0TEzM7Na8HX6gKTrJa0P3AJsA1wOPAg8Azyd+9PMzKxutUhVbZWSdIakqZKWSXpS0ogyj9tP0kpJz1R6zkoy/ROA75ICvpmZWUPqjuF9SceSbmt/BimBPhW4U9JOETF9DcdtANwI/BXYrLKzVhb0BRARnss3M7PGVcXwfhX35jkLuC4irs1ej5F0KHA6cM4ajvslcDOwCjiy0pNWehvedp+uZ2ZmZgyUtH5uW+1qN0l9gN2Bu4t23Q3s217Dkk4EtgPOrbZzlS7ke1nSGgN/RLyv2s6YmZnVWguipcLUPVf/taJd5wJji8o2AXoBs4vKZ5PucLsaSR8E/g0YERErq70vQKVB/8fAgqrOZGZmVgc6uHp/CNCa27V8DYcVJ9EqUYakXqQh/R9HxMuV9aytSoP+byNiTkdOaGZm1pN1cCFfa0QsXEv1uaQ5+eKsfhCrZ/8AA0mPrh8u6ReFUwKStBI4JCLuLaeflQR9z+ebmVnDq+YSvErqR8Q7kp4ERgH/m9s1Cri1xCELgV2Kys4ADiY94XZqueeuePW+mZlZI+umm/NcAvxa0hPAw8BXgaHA1ak9XQhsGRFfjoh3gefbnk9zgGUR8TwVKDvoR0SlK/3NzMzqTgtVZPoV5sURcYukjYEfAYNJQf3w3GXxg0lfAjpVNbfhNTMzsw6KiCtJt7YvtW/0Wo4dy+pXBayVg76ZmVlOI99730HfzMwsp4XK71xXL/PfDvpmZmY5kqj05jfV3iynuznom5mZ5YjKL1erj5DvoG9mZtZGV1+nX0v1Mg1hZmZmHeRM38zMrEh95O2Vc9A3MzPL8SV7ZmZmTcKr983MzJqEr9M3MzNrEs70zczMmkQjX6dfLyMSZmZm1kHO9M3MzHI8vG9mZtYkvJDPzMysSTjTNzMzaxKNvJDPQd/MzCynke/IVy/TEGZmZtZBzvTNzMxyWhAtFQ7YV1q/Vhz0zczMchp5eN9B38zMLEfZf5UeUw8c9M3MzHKc6ZuZmTUJVTGn70zfzMysDjVypu9L9szMzJqEM30zM7OcRs70HfTNzMxyvHrfzMysSbQobZUeUw8c9M3MzHKc6ZuZmTWJRp7T9+p9MzOzJuFM38zMLEdUPlxfJ4m+g75ZwZ1/eZxb73iYt+e3stWWgzjpS4ew045bl6z70sTp3Pjbe3j9jXm8s3wFm26yAYccvDtHHLZ3N/fa6sHRewzh+H22ZuOBfZgyZzGX3v0yz0yfX7Lujz69E5/adYvVyqfMWcTnr36ki3tq4IV8DUvSNOCyiLisxl2xGvvbIy9ww013ccrowxm2/Vbcde9TnH/xzfzHRWew6SYbrFa/b9/eHD5qL7YeOoh1+/bhpYnTufqG2+nbtzeHHLx7DX4D66k+vtNmnHXo9vz0jgn8fcZ8jtptCJd9cVeOvfJhZi9cvlr9n901kSv+Oum9171axG9O/Sh/fWlOd3a7qTXyQr6azelLOk1Sq6R1cmUDJK2Q9EBR3RGSQtL2kqZlP3++RJsvZPtGd8Ov0GVyv2NIWiLpeUmn5vbvL+lBSfMkLZU0QdI3a9nnevenOx/mYyOHM+qg3Riy5aZ85fhD2XjjDbjrr0+UrL/t+wczYt+dGTpkEIM23ZAD9/8wu+6yHS9NnN7NPbee7ov7DOW2p2dy69MzmTZ3CZfe/TKzFyzn6D2GlKy/ePkq5i1+571t2BbrM3C93vzpmZnd3PPmVVjIV+lWD2q5kG8cMADYI1c2ApgF7CmpX658JDAzIl7OXs8ATsw3JmlvYHNgcVd1uJv9CBgMfBj4I3C1pGOzfYuBXwAHAMOA84HzJX21Bv2seytWrmLy1Df4yM7btSnfdedtmfDKjLLamDLtDSa+MqPd6QBrTuu0iB0HD+TRyfPalD86ZR4f3mrDstr49PAteGzKW8xasKwLemilqMqtHtQs6EfERGAmKaAXjARuBSYD+xaVj8u9/g1woKStcmUnZeUr8+eRNFbSdEnLJc2UdHlRV/pJuj4bdZheHDglXSTp5SzjniLpPEm9i9p/RtJJ2fGLJF0lqZek70iaJWmOpO9X8NcD0BoRsyJiUkT8AHgFOBIgIp6OiP+KiBciYlpE3ATcRfrSVJKkvpLWL2zAwAr707BaW5fw7rvBhhv0b1O+wQb9mT9/zd8hT/76pXxu9AV854fX8omP78mog3bryq5andmwX2/WaWlh3uJ32pS/tfgdNu7fZ63HbzygD/t8YGNue/r1ruqildCCaFGFW52E/VpfsncfcFDu9UFZ2fhCuaQ+wD60DfqzSUHuhKxOP+BY4Pp845KOAb4JnAp8kBQ0nyvqw7eAJ4DhwJXAVZJ2zO1vBUYDOwFnAqdkbeZtBxwGfAL4AukLyO3AEOBA4GxSJt6RVV7LgN6ldkgaTvqSNH4Nx58DLMhtr3WgLw2p1PDc2obsLvjhaC4+72ROPemT/PmuR3ngoee7pnPWUAREGfU+9ZEtWLRsJfdNeLOru2RNotYL+e4DLs3m9dcjBd77gV7AN7I6e2f7xhUdez3wM0kXAMcAkyPiGbX9lB5Kmi64JyJWANOBx4rauSMiroSU1ZMC+khgAkBEnJ+rO03Sz0hfMH6aK28BToqIVuBFSeOAHYDDI+JdYKKks7N2K1p+m/3dfAnYBbiqaN9rwKak/49jI+LaNTR1IXBJ7vVAHPgBGDiwHy0t4u2irH7BgsVsUJT9F9ts0EYAbL3VZsxfsIhb/mc8I/bducv6avVl/pIVrHz33dWy+o369+Gtouy/lCN23YI7n53FynfL+YpgnaWa4fr6yPNrn+mPA/oDe5KGpl+OiDmkjHVPSf1JgXJ6REwpOvZ20pqAA0iZ9fWs7vekLwxTJF0j6aj8wsHMs4UfIiJIXxIGFcokHSPpb9kw/SLgPNKXibxpWcAvmA28mAX8fNkgyndRdr6lwBXAxcAvi+qMIK2JOA0YI+kL7TUWEcsjYmFhI41gGNB7nV5st81g/v5827fY35+fwo4f3Kqdo1YXAStWrlx7RWsaK98NJrzRyl7bvq9N+V7bvo9nZ8xf47G7bb0RQzfux60e2u9+DTypX9NMPyImZdnqQcBGZMPTETFL0lRgv2zfvSWOXSnp18C5wEeBo0rUmSFpB2AU8HHS8P2/SDowy/wBVhQfRvZlKBuO/y3wY9J0wgLg86QpgbxSbbTbbpkuBn4FLAHeyL6QtG0wYmr243OSNgPGAv9VwTksc8Rh+3D5Vf/LB7YdzA4fGMLd455i7rwFHPKxdPndTbf8lXlvt3LmaUcC6Zr+TTZeny232ASAlybO4LY7HubwQ/as1a9gPdTND0/n3KM+xEtvtPLca+mSvc03WJf/eTIF8zMO3o5BA9dl7K0vtDnu08O34LnXFjDlzUZZm1w/GvmSvVoP70PK9keSgv7FufLxwKGk4f0b2jn2euDbwC0R8XapChGxFLgNuE3SFaRh+12Ap8ro237AqxFxQaFAUnctz54bEZPWXu09Avp2VWca3f57f4jW1iX87n/v5+35ixg6ZBDf/5cvMmiTDQF4e/4i5s5d8F79dyO46Xf3MufN+fRqaWGzQRvxpWM/5mv0bTX3vDibDfr15isHbMMmA/oyec4ivnnzM++txt9kQF8222DdNsf079uLg4cN4mf/N7EWXbZqLsGrj5jfY4L+FaRFavmFaONJc9jrsvp8PgAR8ZKkTUjZ8Gqy6/V7AY9mdY4nDZe/WmbfJgFDs3sCPA58khIjCt1N0tdI6xMmZEX7k778/LxmnWoAh43ak8NGlc7Uv37qZ9q8/uQhe/HJQ/bqjm5ZA/jDE6/xhydKL6H5yW0vrla2ePkqDriw5MeedYNGntPvKUF/PWBCRMzOlY8nLTabHBHtXiwdEfPa2wfMB75LWsDWi7Ry/4i1HJNv+1ZJl5Kuie9LWkdwHmkYvZZaSAvztiFdojiZ9HsWz/mbmVmlGjjqq8RUsTWB7Fr9Bf/9yCT6DfAl+9a5zr7l2bVXMqvQquWLefGnRwJskC1I7lSFz8V7/z6dAQPXr+jYRa0LOfgjQ7usb52lJ2T6ZmZmPUYjL+Sr9SV7TUfScdld+0ptL6y9BTMz60qNfO99Z/rd7zbSwsJSii/zMzOzbtbAU/oO+t0tu4mPb4xjZtZTNXDU9/C+mZlZjqr8r+LzSGdImippmaQnJa3poWn/JOkvkt6UtFDSw5IOrfScDvpmZmbdLHtU+mXABaTnzjwA3Cmp+DbvBQcAfwEOB3YnXe7+p+yBa2Xz8L6ZmVlONQvzcvUHFj34bXlELC9xyFnAdbkHpY3JMvfTSU9FbSMixhQVfU/SZ4AjgKfL7aczfTMzs5wOPm/nNdo+xny1AJ49Mn534O6iXXeTHpO+9j5KLaQb2L1V5q8FONM3MzNrq2ML+YbQdrF2qSx/E9JdYmcXlc8GNi/zjN8iPaX2d2X3EQd9MzOzNjp4c57WCu7IV3xLXJUoW/1c6THqY4HPZI+jL5uDvpmZWU4H5/TLMRdYxepZ/SBWz/6LzqNjgeuAz0bEPRWdFc/pm5mZtdHBOf21ioh3gCeBUUW7RgEPtduvlOH/CvhiRNxewSnf40zfzMys+10C/FrSE8DDwFeBocDVAJIuBLaMiC9nr78A3AicCTwiqTBKsDQiFpR7Ugd9MzOzvG64I19E3CJpY+BHwGDgeeDwiHg1qzKY9CWg4FRSzL4i2wr+Exhd7nkd9M3MzHK66yl7EXElcGU7+0YXvR5Z8QlKcNA3MzPL6YaFfDXjoG9mZpbTwM/bcdA3MzNro4Gjvi/ZMzMzaxLO9M3MzHK6ayFfLTjom5mZ5Xghn5mZWZNo4Cl9B30zM7M2GjjqO+ibmZnleE7fzMysWVQxp18nMd+X7JmZmTULZ/pmZmY5DTyl76BvZmbWRgNHfQd9MzOzHC/kMzMzaxK+OY+ZmVmTaODRfa/eNzMzaxbO9M3MzPIaONV30DczM8vxQj4zM7MmIapYyNclPel8DvpmZmY5DTy676BvZmaW50v2zMzMmkbj5vq+ZM/MzKxJONM3MzPL8fC+mZlZk2jcwX0HfTMzszac6ZuZmTUJ35zHzMysWTTw+L6DvpmZWU4Dx3xfsmdmZtYsnOmbmZnleCGfmZlZk/BCPjMzs2bRwJP6DvpmZmY5DRzzHfTNzMzyGnlO36v3zczMmoQzfTMzszYqX8hXLwP8DvpmZmY5Ht43MzOzuudM38zMLKeRM30HfTMzsxzfnMfMzKxJNHKm7zl9MzOzJuFM38zMLMd35DMzM2sWDRz1HfTNzMxyvJDPzMysSTTyQj4HfTMzs5wGHt336n0zM7NakHSGpKmSlkl6UtKItdQ/MKu3TNIUSadVek4HfTMzszxVuVVyCulY4DLgAmA48ABwp6Sh7dTfBrgjqzcc+FfgcklHV3JeB30zM7McVflfhc4CrouIayPipYgYA8wATm+n/mnA9IgYk9W/Frge+HYlJ/WcfpNbsri11l2wBrRq+eJad8Ea0KrlS7rlPK2tCytemNfaurDw40C1PXh5RCzPF0jqA+wO/FtRM3cD+7Zzin2y/Xl3AV+R1DsiVpTTTwf95jUQ4MsfG17rfpiZVWogsHCttSr3DjDrg9tstXmVxy8CXisqOxcYW1S2CdALmF1UPhto79ybt1N/nay9N8rpoIN+85oJDAGc6pdnIOkfs//OrLP5vVWZgaTPr04XEcuyufM+ndjs8jXsi6LXKlG2tvqlytvloN+kIiKA12vdj3qRG65rjYiuyDCsSfm9VbEu/TuKiGXAsq48BzAXWMXqWf0gVs/mC2a1U38lMK/cE3shn5mZWTeKiHeAJ4FRRbtGAQ+1c9jDJeofAjxR7nw+OOibmZnVwiXAyZJOkjRM0qXAUOBqAEkXSroxV/9qYGtJl2T1TwK+Avx7JSf18L5ZeZaTFuSsaX7OrBp+bzWhiLhF0sbAj4DBwPPA4RHxalZlMOlLQKH+VEmHA5cCXyOta/hGRPyhkvMqTe2amZlZo/PwvpmZWZNw0DczM2sSDvpmZmZNwkHfmpqkaZLG1Lof1nj83rKeyEHfakbSaZJaJa2TKxsgaYWkB4rqjpAUkrbPPkxD0udLtPlCtm90N/wKXSb3O4akVZJmSrpO0ka5OiOz/RvWsKs9kt9b7St6by2R9LykU3P795f0oKR5kpZKmiDpm7Xss3UeB32rpXHAAGCPXNkI0p2n9pTUL1c+EpgZES9nr2cAJ+Ybk7Q36Y5VjfK0l8KlPEOB44ADgMtr2qP64ffWmhXeWx8G/ghcnT3qFdLv+AvS+20YcD5wvqSv1qCf1skc9K1mImIi6VrTkbnikcCtwGTaPm1qJOmDvOA3wIGStsqVnZSVr8yfR9JYSdMlLc8y5uLA2U/S9VlmOL34w03SRZJezrKiKZLOk9S7qP1nsptsTJe0SNJVknpJ+o6kWZLmSPp+BX89kG7LOisiXo+IccCNwG4VttGU/N5aq8J7a1JE/AB4BTgSICKejoj/iogXImJaRNxEeprbiArPYT2Qg77V2n3AQbnXB2Vl4wvlSo+h3Ie2H8yzSR9EJ2R1+gHHkp4v/R5JxwDfBE4FPkj6YHuuqA/fAp4AhgNXAldJ2jG3vxUYDewEnAmckrWZtx1wGPAJ4AukIHE76SEqBwJnk7Klvdv/q2ifpC2BTwGPVnN8k7oPv7fKtQzoXWqHpOGkL0njO9C+9RQR4c1bzTbSh9wi0t0hBwIrSA+ROBZ4MKtzAOkpUttmr6cBY4DPAJNIT5r6MvBUtn8+MDr7+SxgItC7nfNPA36dey3Sh/5pa+jzv5Dud114PZY0JDowV/Z/wFSgJVc2AfhumX8v00h3aFsELM1+/0eADXN1RmblG5bTZrNtfm+t8b01Jvt5HdKXjgBOL6r3WvYeXAX8sNb/P711zuZM32ptHNAf2JM0fPhyRMwhZRV7SupPCm7TI2JK0bG3k+ZtDyBlP9ezut8D6wFTJF0j6aj84q7Ms4UfIn3azSIFByBldJL+lg2lLgLOI3d7zMy0iMg/FnU28GJEvFtUNojyXQzsSpp3/VhWdrukXhW00cz83mrfRdn5lgJXkN5rvyyqM4K0JuI0YIykL1TQvvVQDvpWUxExiZRRHJRt47PyWaRsZr+s/N4Sx64Efk26b/lHSXOuxXVmADuQ7lW9lDTEen9+3pSUAbY5jOzfRjZk+lvgTtLw+nDgAlZ/3napNtptt0xzI825vhIR95Iy0H1pO2Rt7fB7a40KXyi3BgZExHeKvkQQEVMj4rmIuIZ0v/exFbRvPZSDvvUE40gZ10jSnGvBeOBQYG/azrnmXU+a17w1It4uVSEilkbEbRHxjewc+wC7lNm3/YBXI+KCiHgiIl4hfVDWwqrsz/VqdP565PdWaYUvlDOzEYi1EdC3qztlXc9P2bOeYBxpiLE3bRcLjQeuAtalnQ/miHhJ0ibAklL7s2uqe5EWwC0BjidlZa+Wql/CJGBodt3248AngaPKPLajBkranPSBuxXwU2Auqz9vexdJ+eFfIuKZbulhz+f3VoUkfQ2YTlonALA/8G3g5zXrlHUaB33rCcaRstcJETE7Vz6etABrcjaUWlJEzFtD2/OB75KeXd2LtLr6iLUck2/7VqXnXP+ClOncTpp3HVvO8R30k2wDeJMUGEaV6Pv9JY5VV3asjvi9VbkW4EJgG9IlipNJv2fxnL/VIT9a18zMrEl4Tt/MzKxJOOibdTNJx2V3Viu1vVDr/ln98nvL1sbD+2bdTNJAYLN2dq+IiHIXgpm14feWrY2DvpmZWZPw8L6ZmVmTcNA3MzNrEg76ZmZmTcJB38zMrEk46Js1KUljJT2Te/0rSX+sQT/eLykk7bqGOtMkjamgzdGS5ndC30LSkR1tx6yncNA360GywBvZtkLSFEn/nj0GtqudSXq2+lqVE6jNrOfxvffNep7/A04kPSRmBHAt6bnwpxdXlNQ7Ioofs1qViFjQGe2YWc/lTN+s51keEbMiYkZE3Ex6lvuR8I8heUknSZoCLFeygaT/J2mOpIWS7pX0kXyjkr4rabakVknXkZ4wl9/fZnhfUouksyVNkrRc0nRJ3892T83+fDrL+O/LHXeipJckLZM0QdIZRefZS9LT2f4nSM+Rr4iksyQ9J2mxpBmSrpQ0oES9IyW9nJ3rL5K2Ktp/hKQns/1TJP1YkpMha1gO+mY931JS1l/wAeBzwNHArlnZ7cDmwOHA7sBTwF8lvQ9A0ueAc4HvA3sAbwBtgnEJFwJnk578thPwRaDwpLq9sj8/DgwG/ik7zynABdl5hgHfA86TdEK2vz/wZ2Bi1s+xwL+X99fQxrvAN4CdgROAg0mPHs7rl/XjBNKz69cHflvYKelQ4Cbg8uz3O5U0vfF9zBpVRHjz5q2HbMCvgD/mXu8FzAVuyV6PBd4BNs3VORhYAPQtamsS8NXs54eAq4r2PwI8U+rcpMfOLgNObqef7wcC2LWofDrwhaKyHwAPZT9/FZgH9MvtP61UW0VtTAPGrGH/Z4G5udejszY/mivbMSvbK3t9P3BOUTtfAmbmXgdwZK3fF968ddbmYSyznudTkhaR1tz0Bm4Fvp7b/2pEvJl7vTswAJgnKd/OesB22c/DgKuLzvMwcFA7fRhGesb7X8vttKRNga2A6yRdk9u1DulLSaHdv0fEkqJ+VETSQaRRhJ1IGfw6wLqS+kfE4qzaSuCJwjERMSFb0T8MeIz097ZnbsoCoFfWTr+iPpo1BAd9s55nHGnR3gpS1lm8UG9x0esW0nD9yBJtza+yD0urOKYwXXgK8GjRvlXZn6KDJG0N3EH6EvND4C1gf+A62k6DQMrUixXKWoAfA/9Tos6yjvbTrCdy0DfreRZHxKQK6j9Fms9fGRHT2qnzErA3cGOubO81tPkKKfB/jHT1QLF3sj97FQoiYrak14FtI+I37bT7InC8pPUiovDFYk39KGUP0mfXtyLiXXhvzUKxdbK6j2V1dgA2BCZk+58Cdqjw79qsrjnom9W/e0hD5H+UdDZpkdwWpEV9f4yIJ4D/AP4zWy3/N+A44EPAlFINRsQySRcBP5X0DvAgsCnwoYi4DphD+lLwCUmvAcsiXfI3Frhc0kLgTtIUwR7ARhFxCXAzaaHfdZLOJ60N+HaFv+9k0mfX1yX9ibRI77QS9VYAP5f0jeznXwCPRMRj2f6fAH+WNAP4PWlx4IeBXSLiBxX2yawuePW+WZ2LiCAF+PuB64GXSavU30+22j4ibiEFuYuAJ4GtgavW0vR5wM+y414CbgEGZe2tJK2ePxWYSVp3QERcC5xMWkj3HDA++3lqtn8RcARpLv5p0heAsyv8fZ8BzsqOe570BeacElWXZL/vzaQvRUuBz+fauQv4FDAKeJy0sPEswM+ct4al9HlhZmZmjc6ZvpmZWZNw0DczM2sSDvpmZmZNwkHfzMysSTjom5mZNQkHfTMzsybhoG9mZtYkHPTNzMyahIO+mZlZk3DQNzMzaxIO+mZmZk3i/wM6BCM7UaJJxwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 600x400 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pc = Perceptron().fit(X_train, y_train)\n",
    "pred_pc = pc.predict(X_test)\n",
    "print(classification_report(y_test, pred_pc))\n",
    "\n",
    "plot_confusion_matrix(pc, X_test, y_test, display_labels=le.inverse_transform(pc.classes_), cmap=plt.cm.Blues, normalize='true')\n",
    "scoring_PC = [accuracy_score(y_test, pred_pc), balanced_accuracy_score(y_test, pred_pc), f1_score(y_test, pred_pc, average='weighted')]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "21c06279",
   "metadata": {},
   "source": [
    "### Logistic Regression Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "897d0e49",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           9       0.64      1.00      0.78         7\n",
      "          11       1.00      0.60      0.75        10\n",
      "\n",
      "    accuracy                           0.76        17\n",
      "   macro avg       0.82      0.80      0.76        17\n",
      "weighted avg       0.85      0.76      0.76        17\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x1dfea55f5b0>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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62fZvCN9oY70AHPTNzKxmtXNOf0bRrouB0S3rqjewN/AfRXUfAg5s5RSfA54FviXpRGAxcC/wbxGxtK39bFPQj4gd2tqgmZlZAxsMNOdel8rytwB6ALOLymcDA1tpd0fg48Ay4PNZG9cDmwFtnteveE4/+6ayAzA5IlZV2o6ZmVl3ouy/co/JNEfEwjYeFms1s3ZZQVO274SIWAAg6Xzg/0o6p63Zftmr9yX1lXQzsAR4GRiSlV8r6dvltmdmZtadFBbylbuVYS6wmrWz+q1YO/sveBt4qxDwM6+SvigMbuuJK7lk73JgT2A4aZih4M/AcRW0Z2Zm1m109iV7EbECGAeMKNo1AniilcMeB7aR1D9XtjOwhrXXEbSqkqB/NPC1iPgrLYchXgF2qqA9MzOzbqOwkK/crUxXA1+RdKqkoZJ+Qho5vzH1QZdLui1X/w5gHnCrpF0lHQJcBdzS4Qv5imwJzClR3o/W5yLMzMxqQlfchjci7pS0OfB9YBDwEnBkREzLqgwimz7P6i+SNAL4GWkV/zzSdfvfK+e8lQT9Z4DPZCeGfwT604EnK2jPzMys2+iq2/BGxPWkFfil9o0sUTaBtacEylJJ0L8I+F9Ju2bHnydpN+AA0o0DzMzMrBsqe04/Ip4ADgL6ApOBT5FWGx4QEeM6tntmZmZdq6vuvV8NFV2nHxEvAid3cF/MzMyqrp6fsldR0JfUg3RHoKGkOf1XgXt8kx4zM6t1XbGQr1rKDvqSPgLcQ7qpwGtZ8c7AO5I+l40CmJmZ1SRBmffjK79+tVRynf5NpDvxDY6Ij0bER4HtgBeA/+zIzpmZmXU1z+m3tCewT0S8VyiIiPckfZd0OZ+ZmZl1Q5Vk+q8BW5co3wqY1L7umJmZVVcX3Hu/atqU6UvaKPfyO8C1kkYDT2Vl+5PuKnRhh/bOzMysi1UyXF9vw/vzaXmLXZFu/xe51wB/ID0j2MzMrGbVSAwvW1uD/mGd2gszM7NuouEz/YgY29kdMTMz6w4qmaOvqzn9UiT1JT0BqHe+PCJeaG+nzMzMqqXhM/08SVsCtwKfbqWK5/TNzMy6oUou2bsG2JS0Yn8pcATpPvyvA5/rsJ6ZmZlVgSrcakElw/ufAI6KiGckrQGmRcSfJC0kPXb3vg7toZmZWReq53vvV5Lp9wPmZD+/C2yZ/fwi8NGO6JSZmVm1FJ6yV+5WCyq9I98u2c/jgTMkbQucCbzdQf0yMzOrCt97v6VrgEHZzxcDDwInACuAkR3SKzMzsyqpJHOvkZhfftCPiN/kfn5e0geADwPTI2JuB/bNzMzMOlDF1+kXRMQS4LkO6IuZmVnV1fNCvrY+cOfqtjYYEedX3h0zM7Pq8vA+DGtjvVh/FTMzs+6r4e/IFxF+4E6duuRHX2PDfgOq3Q2rM8fc9HS1u2B1aOXSRV1ynibKv7StkkvhqqHdc/pmZmb1pOEzfTMzs0ahCp6yVyMxv2ZGJMzMzKydnOmbmZnlNFWQ6Zdbv1oc9M3MzHLqeU6/ouF9SSdKelzSTEnbZ2WjJB3Vsd0zMzPrWoVMv9ytFpQd9CWdBVwN3A9sAvTIds0HRnVQv8zMzKrCT9lr6evA6RFxGbA6V/4ssHuH9MrMzKxKCrfhLXerBZUE/R2A50uULwf6ta87ZmZm1lkqCfpTgL1KlH8aeKVdvTEzM6uypgq3WlDJ6v2rgJ9L2gAQ8DFJxwMXAV/pyM6ZmZl1NT9wJycibpXUE7gS6AvcAbwFnBcRv+vg/pmZmXWpJip4tC61EfUruk4/In4J/FLSFkBTRMzp2G6ZmZlVhzP9VkTE3I7qiJmZWXfgO/LlSJoCRGv7I2LHdvXIzMysitIDd8q9I18ndaaDVZLpX1P0uhcwDDiCtMjPzMzMuqFKFvL9tFS5pHOAfdrdIzMzsyqq5zn9jry08AHgmA5sz8zMrMvV8733O/Ipe8cC73Zge2ZmZl1O2X/lHlMLKlnI9zwtF/IJGAhsCZzdQf0yMzOrCq/eb+nuotdrgHeARyJiQrt7ZGZmVkUO+pnsTnxTgQcjYlan9MjMzKyKJKGyL9mrjahf1kK+iFgF3AD06ZzumJmZWWepZHj/b6Tr8qd1cF/MzMyqzsP7LV0P/FjSYGAcsDi/MyJe6IiOmZmZVYOv0wck3SJpI+BOYAfgWuBxYDzwfO5PMzOzmtUkVbSVS9LZkqZIWiZpnKSD23jcQZJWSRpf7jnLyfRPBr5NCvhmZmZ1qSuG9yUdR7qt/dmkBPoM4AFJu0bE9HUctzFwG/AXYOvyzlpe0BdARHgu38zM6lcFw/sV3JvnfODmiLgpez1K0uHAWcBF6zjuF8AdwGrg6HJPWu5teFt9up6ZmZkxQNJGuW2tq90k9Qb2Bh4q2vUQcGBrDUs6BdgJuLjSzpW7kG+ipHUG/ojYrNLOmJmZVVsToqnM1D1Xf0bRrouB0UVlWwA9gNlF5bNJd7hdi6QPAf8BHBwRqyq9L0C5Qf8HwIKKzmRmZlYD2rl6fzDQnNu1fB2HFSfRKlGGpB6kIf0fRMTE8nrWUrlB/3cRMac9JzQzM+vO2rmQrzkiFq6n+lzSnHxxVr8Va2f/AANIj64fJum6wikBSVoFfCoiHm5LP8sJ+p7PNzOzulfJJXjl1I+IFZLGASOA/5fbNQK4p8QhC4Hdi8rOBj5BesLtlLaeu+zV+2ZmZvWsi27OczVwu6RngSeBrwJDgBtTe7oc2DYiToqINcBLLc+nOcCyiHiJMrQ56EdEuSv9zczMak4TFWT6ZebFEXGnpM2B7wODSEH9yNxl8YNIXwI6VCW34TUzM7N2iojrSbe2L7Vv5HqOHc3aVwWsl4O+mZlZTj3fe99B38zMLKeJ8u9cVyvz3w76ZmZmOZIo9+Y3ld4sp6s56JuZmeWI8i9Xq42Q76BvZmbWQmdfp19NtTINYWZmZu3kTN/MzKxIbeTt5XPQNzMzy/Ele2ZmZg3Cq/fNzMwahK/TNzMzaxDO9M3MzBpEPV+nXysjEmZmZtZOzvTNzMxyPLxvZmbWILyQz8zMrEE40zczM2sQ9byQz0HfzMwsp57vyFcr0xBmZmbWTs70zczMcpoQTWUO2Jdbv1oc9M3MzHLqeXjfQd/MzCxH2X/lHlMLHPTNzMxynOmbmZk1CFUwp+9M38zMrAbVc6bvS/bMzMwahDN9MzOznHrO9B30zczMcrx638zMrEE0KW3lHlMLHPTNzMxynOmbmZk1iHqe0/fqfTMzswbhTN/MzCxHlD9cXyOJvoO+WcETj47nkb88Q/OCxWw9aHM+d8xh7PjBwes9bsrkt7jxp3ey9aAtOP+ik7qgp1ZrDh+6FUftMYhNN+zFm/OXcuuT03h19qJW6/dsEl/66LYcstPmbNK3F/MWr+Cu8TN5eOLcLux146rnhXwNPbwvaaqkUdXuh1Xf+HETuPeuMXzy8P0Y9e0T2WGnwdx8/f/w3rsL13nc0qXL+d3tD/DBnYd0UU+t1hy442acsv8Q7ho/k2/e/RKvzmrmu0fswhb9erd6zAWf/CC7b7MR1z82ha///gV+MmYyb81f1oW9bmyq8L9aULWgL+lMSc2SeubK+ktaKemxoroHSwpJO2eBOiR9uUSbL2f7RnbBr9Bpcr9jSFoi6SVJZ+T2f1zS45LmSVoqaYKkb1Szz7Xu0YfHse8Bu7PfgXuw9cDNOerYw9hk0wE8+djf13ncXb/9E8P2Gcr2O2zTRT21WvPZjwzk4Ynv8JfX3uGt+cu49anpzFu8gsOHblWy/l6DN2a3gQO47MHXeGHmQt5ZtIJJ7yzmtTmtjwxYxyos5Ct3qwXVzPTHAP2BfXJlBwOzgH0l9c2VDwdmRsTE7PWbwCn5xiTtDwwEFndWh7vY94FBwB7A3cCNko7L9i0GrgMOAYYClwKXSvpqFfpZ81atWs1bb85m56Hbtyjfeej2TJsys9XjnnnyJebNnc+ITx/Q2V20GtWzSey0RT/Gz2g5YvT3GQvYZev+JY/Zd8gmTJ67mKP3GMR/Hr8XP/viHpz0se3o3aNGokodUIVbLaha0I+I14CZpIBeMBy4B5gMHFhUPib3+jfAoZK2y5WdmpWvyp9H0mhJ0yUtlzRT0rVFXekr6ZZs1GF6ceCUdIWkiVnG/YakSyT1Kmp/vKRTs+MXSbpBUg9J35I0S9IcSd8t468HoDkiZkXEpIj4HvA6cDRARDwfEb+NiJcjYmpE/Bp4kPSlqSRJfSRtVNiAAWX2p24tXrSUNWuCAQP6tijvP6AfzQtLf4d8Z8573H/vY/zLyUfSo0dDz5LZOgzYoCc9msSCpStblM9fupJNNuxV8pitB/Thw1sPYLtN+3Lln1/n1ienccAOm/GVAz/QBT02gCZEk8rcaiTsV/vT6hHgsNzrw7KysYVySb2BA2gZ9GeTgtzJWZ2+wHHALfnGJR0LfAM4A/gQKWi+WNSHC4BngWHA9cANkj6c298MjAR2Bc4DTs/azNsJ+DRwBHA86QvIfcBg4FDgQlImvn/rfxXrtQwo+SkhaRjpS9LYdRx/EbAgt81oR1/qVNE/2oiSX9/XrFnDHb+6j08deSBbbr1Z13TNaloUvV7XULAkAvjpmMlMemcxz81YwK/+Np3Ddt7C2b61W7VX7z8C/CSb19+QFHgfBXoA52Z19s/2jSk69hbgx5IuA44FJkfEeLX81zSENF3w54hYCUwHni5q5/6IuB5SVk8K6MOBCQARcWmu7lRJPyZ9wbgyV94EnBoRzcArksYAuwBHRsQa4DVJF2btPtWmv5lM9nfzr8DuwA1F+2YAW5L+P46OiJvW0dTlwNW51wNw4AegX/8NaWoSzc0ts/pFi5YwYEC/teovX7aCGdNnM3PGHO7+/V8AiAgi4MJzr+b0c47lg7t4YZ9B87JVrF4Ta2X1G2/Qi/lF2X/Be0tW8u7iFSxZufr9shnzl9IksXm/3ry9cHmn9tkqG66vla9j1Q76Y4B+wL7ApsDEiJgjaSxwu6R+pEA5PSLeKDr2PuAXpHntUynK8jO/B0YBb0j6X+B+4A8RkZ8CeKHwQ0SEpFnA+ytsstGCUcAHSWsQegLFS7qnZgG/YDawOgv4+bLSK3dKu0LSpUAfYAVwFen3zTs469P+wH9ImhQRvy3VWEQsB97/tFCtrDrpAj179mDb7bbm9QnT2H3PD71fPnHCNHbb/YNr1e+zQR8u+M7JLcqeeGw8kyZO56TTPsdmm2/c6X222rBqTTB57mL23HYjnp723vvle2y7Mc/kXue9NruZA3fclA16NrFsVfoI2WajDVi9Jpi3eEWX9Lvh1XHUr2rQj4hJWbZ6GCnoj83KZ0maAhyU7Xu4xLGrJN0OXAzsB3y+RJ03Je0CjAD+iTR8/38kHZpl/gDFX7eDbNojG47/HfAD0nTCAuDLpCmBvFJttNpuG10F/ApYArwdEcUjhETElOzHFyVtDYwGSgZ9W7dDPrE3v7vtAQYP2Zrtd9iGvz3+AvPfbeaAg/cE4P57HmPBgkUcf9KnaWoSA7fZosXx/fv3pWfPnmuVm/3hpVmce+iOTJ6bVuCP2GUrtujfm4cmzAHghH0Gs1m/3vxsbMprHps8j2OHbcM5h+zInc/NYKMNenHSfkN4eOI7rFi91seAdQLfe79zjSFl85uSAl3BWOBwUhZ7ayvH3gJ8E7gzIkp+bY6IpcC9wL2Sfk4att8deK4NfTsImBYRlxUKJG2/jvodaW5ETCqjvkijAlaBvfb+MEsWL+PPDzzFwoWLGThoc047+wtsutlGACxcuJj567lm36yUJ954lwF9evLFYduyad9eTH9vKf/+4ETeWZSy9k379mKL/v+4Zn/ZqjX88IHXOO3A7bny6N1oXraKJ6a8y2+f9Wxcl6nkErzaiPndJuj/nLRILb8QbSxpDnsD1p7PByAiXpW0BSkbXkt2vX4P4G9ZnROBpcC0NvZtEjAkuyfAM8BnKDGi0NUknUNanzAhK/o46cvPz6rWqTpw4CF7ceAhe5Xc9+UTj1jnsZ/6zIF86jMHrrOONa4HX53Dg6/OKbnvukenrFX21oJl/PCB1zq7W9aKOh7d7zZBf0NgQkTMzpWPJS02mxwRb7Z2cETMW0fb84Fvkxaw9SCt3P/seo7Jt32PpJ+QronvQ1pHcAlpGL2amkgL83YgXaI4mfR7Fs/5m5lZueo46qvEVLE1gOxa/QVXPvh3NuznS/atYz3w4jvV7oLVoZVLF/Gn8z8JsHFEdPh8W+Fz8eG/T6f/gI3KOnZR80I+seeQTutbR+kOmb6ZmVm3Uc8L+ap9c56GI+mE7K59pbaXq90/M7NGV8/33nem3/XuJS0sLKX03TrMzKzL1PGUvoN+V8tu4tO83opmZlYddRz1PbxvZmaWowr/K/s80tmSpkhaJmmcpHU9NO0Lkv4k6R1JCyU9Kenwcs/poG9mZtbFskelXwNcRnruzGPAA5Jae3DHIcCfgCOBvUmXu/8he+Bam3l438zMLKeShXm5+gOKnm2yPHv2SbHzgZtzD0oblWXuZ5GeitpCRIwqKvqOpKOAzwLPt7WfzvTNzMxyVOGWmUHLx5ivFcCzR8bvDTxUtOsh0mPS199HqYl0A7t32/hrAc70zczMWmrfQr7BtFysXSrL34J0l9jZReWzgYFtPOMFpKfU/neb+4iDvpmZWQvtvDlPcxl35Cu+Ja5KlK19Lul40u3gj4qI0g91aIWDvpmZWU475/TbYi6wmrWz+q1YO/svOo+OA24GvhgRfy7rrHhO38zMrIV2zumvV0SsAMYBI4p2jQCeaLVfKcP/FfAvEXFfGad8nzN9MzOzrnc1cLukZ4Enga8CQ4AbASRdDmwbESdlr48HbgPOA56SVBglWBoRC9p6Ugd9MzOzvC64I19E3Clpc+D7wCDgJeDIiJiWVRlE+hJQcAYpZv882wr+CxjZ1vM66JuZmeV01VP2IuJ64PpW9o0sej287BOU4KBvZmaW0wUL+arGQd/MzCynjp+346BvZmbWQh1HfV+yZ2Zm1iCc6ZuZmeV01UK+anDQNzMzy/FCPjMzswZRx1P6DvpmZmYt1HHUd9A3MzPL8Zy+mZlZo6hgTr9GYr4v2TMzM2sUzvTNzMxy6nhK30HfzMyshTqO+g76ZmZmOV7IZ2Zm1iB8cx4zM7MGUcej+169b2Zm1iic6ZuZmeXVcarvoG9mZpbjhXxmZmYNQlSwkK9TetLxHPTNzMxy6nh030HfzMwsz5fsmZmZNYz6zfV9yZ6ZmVmDcKZvZmaW4+F9MzOzBlG/g/sO+mZmZi040zczM2sQvjmPmZlZo6jj8X0HfTMzs5w6jvm+ZM/MzKxRONM3MzPL8UI+MzOzBuGFfGZmZo2ijif1HfTNzMxy6jjmO+ibmZnl1fOcvlfvm5mZNQhn+mZmZi2Uv5CvVgb4HfTNzMxyPLxvZmZmNc+ZvpmZWU49Z/oO+mZmZjm+OY+ZmVmDqOdM33P6ZmZmDcKZvpmZWY7vyGdmZtYo6jjqO+ibmZnleCGfmZlZg6jnhXwO+mZmZjl1PLrv1ftmZmbVIOlsSVMkLZM0TtLB66l/aFZvmaQ3JJ1Z7jkd9M3MzPJU4VbOKaTjgGuAy4BhwGPAA5KGtFJ/B+D+rN4w4N+BayUdU855HfTNzMxyVOF/ZTofuDkiboqIVyNiFPAmcFYr9c8EpkfEqKz+TcAtwDfLOann9BvcssWLqt0Fq0Mrl/p9ZR1v1bLFXXKe5uaFZS/Ma25eWPhxgFoevDwilucLJPUG9gb+o6iZh4ADWznFAdn+vAeB0yT1ioiVbemng37jGgDw/S8cVO1+mJmVawCwcL21yrcCmPWhHbYbWOHxi4AZRWUXA6OLyrYAegCzi8pnA62de2Ar9Xtm7b3dlg466DeumcBgoLnaHakRA0j/mP13Zh3N763yDCB9fnW4iFiWzZ337sBml69jXxS9Vomy9dUvVd4qB/0GFREBvFXtftSK3HBdc0R0RoZhDcrvrbJ16t9RRCwDlnXmOYC5wGrWzuq3Yu1svmBWK/VXAfPaemIv5DMzM+tCEbECGAeMKNo1AniilcOeLFH/U8CzbZ3PBwd9MzOzarga+IqkUyUNlfQTYAhwI4CkyyXdlqt/I7C9pKuz+qcCpwE/KuekHt43a5vlpAU565qfM6uE31sNKCLulLQ58H1gEPAScGRETMuqDCJ9CSjUnyLpSOAnwDmkdQ3nRsRd5ZxXaWrXzMzM6p2H983MzBqEg76ZmVmDcNA3MzNrEA761tAkTZU0qtr9sPrj95Z1Rw76VjWSzpTULKlnrqy/pJWSHiuqe7CkkLRz9mEakr5cos2Xs30ju+BX6DS53zEkrZY0U9LNkjbN1Rme7d+kil3tlvzeal3Re2uJpJcknZHb/3FJj0uaJ2mppAmSvlHNPlvHcdC3ahoD9Af2yZUdTLrz1L6S+ubKhwMzI2Ji9vpN4JR8Y5L2J92xqmueytH5CpfyDAFOAA4Brq1qj2qH31vrVnhv7QHcDdyYPeoV0u94Hen9NhS4FLhU0ler0E/rYA76VjUR8RrpWtPhueLhwD3AZFo+bWo46YO84DfAoZK2y5WdmpWvyp9H0mhJ0yUtzzLm4sDZV9ItWWY4vfjDTdIVkiZmWdEbki6R1Kuo/fHZTTamS1ok6QZJPSR9S9IsSXMkfbeMvx5It2WdFRFvRcQY4Dbgo2W20ZD83lqvwntrUkR8D3gdOBogIp6PiN9GxMsRMTUifk16mtvBZZ7DuiEHfau2R4DDcq8Py8rGFsqVHkN5AC0/mGeTPohOzur0BY4jPV/6fZKOBb4BnAF8iPTB9mJRHy4AngWGAdcDN0j6cG5/MzAS2BU4Dzg9azNvJ+DTwBHA8aQgcR/pISqHAheSsqX9W/+raJ2kbYF/Bv5WyfEN6hH83mqrZUCvUjskDSN9SRrbjvatu4gIb96qtpE+5BaR7g45AFhJeojEccDjWZ1DSE+R2jF7PRUYBRwFTCI9aeok4Lls/3xgZPbz+cBrQK9Wzj8VuD33WqQP/TPX0ef/Q7rfdeH1aNKQ6IBc2f8CU4CmXNkE4Ntt/HuZSrpD2yJgafb7PwVskqszPCvfpC1tNtrm99Y631ujsp97kr50BHBWUb0Z2XtwNfBv1f7/6a1jNmf6Vm1jgH7AvqThw4kRMYeUVewrqR8puE2PiDeKjr2PNG97CCn7uYW1/R7YEHhD0i8lfT6/uCvzQuGHSJ92s0jBAUgZnaS/ZkOpi4BLyN0eMzM1IvKPRZ0NvBIRa4rKtqLtrgL2Is27fjIru09SjzLaaGR+b7Xuiux8S4Gfk95rvyiqczBpTcSZwChJx5fRvnVTDvpWVRExiZRRHJZtY7PyWaRs5qCs/OESx64Cbifdt3w/0pxrcZ03gV1I96peShpifTQ/b0rKAFscRvZvIxsy/R3wAGl4fRhwGWs/b7tUG62220ZzI825vh4RD5My0ANpOWRtrfB7a50KXyi3B/pHxLeKvkQQEVMi4sWI+CXpfu+jy2jfuikHfesOxpAyruGkOdeCscDhwP60nHPNu4U0r3lPRLxXqkJELI2IeyPi3OwcBwC7t7FvBwHTIuKyiHg2Il4nfVBWw+rszw2rdP5a5PdWaYUvlDOzEYj1EdCnsztlnc9P2bPuYAxpiLEXLRcLjQVuADaglQ/miHhV0hbAklL7s2uqe5AWwC0BTiRlZdNK1S9hEjAku277GeAzwOfbeGx7DZA0kPSBux1wJTCXtZ+3vbuk/PAvETG+S3rY/fm9VSZJ5wDTSesEAD4OfBP4WdU6ZR3GQd+6gzGk7HVCRMzOlY8lLcCanA2llhQR89bR9nzg26RnV/cgra7+7HqOybd9j9Jzrq8jZTr3keZdR7fl+Hb6YbYBvEMKDCNK9P3REseqMztWQ/zeKl8TcDmwA+kSxcmk37N4zt9qkB+ta2Zm1iA8p29mZtYgHPTNupikE7I7q5XaXq52/6x2+b1l6+PhfbMuJmkAsHUru1dGRFsXgpm14PeWrY+DvpmZWYPw8L6ZmVmDcNA3MzNrEA76ZmZmDcJB38zMrEE46Js1KEmjJY3Pvf6VpLur0I8PSApJe62jzlRJo8poc6Sk+R3Qt5B0dHvbMesuHPTNupEs8Ea2rZT0hqQfZY+B7WznkZ6tvl5tCdRm1v343vtm3c//AqeQHhJzMHAT6bnwZxVXlNQrIoofs1qRiFjQEe2YWfflTN+s+1keEbMi4s2IuIP0LPej4R9D8pJOlfQGsFzJxpL+U9IcSQslPSxpz3yjkr4tabakZkk3k54wl9/fYnhfUpOkCyVNkrRc0nRJ3812T8n+fD7L+B/JHXeKpFclLZM0QdLZRef5mKTns/3Pkp4jXxZJ50t6UdJiSW9Kul5S/xL1jpY0MTvXnyRtV7T/s5LGZfvfkPQDSU6GrG456Jt1f0tJWX/BB4EvAccAe2Vl9wEDgSOBvYHngL9I2gxA0peAi4HvAvsAbwMtgnEJlwMXkp78tivwL0DhSXUfy/78J2AQ8IXsPKcDl2XnGQp8B7hE0snZ/n7AH4HXsn6OBn7Utr+GFtYA5wIfAU4GPkF69HBe36wfJ5OeXb8R8LvCTkmHA78Grs1+vzNI0xvfxaxeRYQ3b966yQb8Crg79/pjwFzgzuz1aGAFsGWuzieABUCforYmAV/Nfn4CuKFo/1PA+FLnJj12dhnwlVb6+QEggL2KyqcDxxeVfQ94Ivv5q8A8oG9u/5ml2ipqYyowah37vwjMzb0embW5X67sw1nZx7LXjwIXFbXzr8DM3OsAjq72+8Kbt47aPIxl1v38s6RFpDU3vYB7gK/n9k+LiHdyr/cG+gPzJOXb2RDYKft5KHBj0XmeBA5rpQ9DSc94/0tbOy1pS2A74GZJv8zt6kn6UlJo9+8RsaSoH2WRdBhpFGFXUgbfE9hAUr+IWJxVWwU8WzgmIiZkK/qHAk+T/t72zU1ZAPTI2ulb1EezuuCgb9b9jCEt2ltJyjqLF+otLnrdRBquH16irfkV9mFpBccUpgtPB/5WtG919qdoJ0nbA/eTvsT8G/Au8HHgZlpOg0DK1IsVypqAHwD/U6LOsvb206w7ctA3634WR8SkMuo/R5rPXxURU1up8yqwP3Bbrmz/dbT5Oinwf5J09UCxFdmfPQoFETFb0lvAjhHxm1bafQU4UdKGEVH4YrGufpSyD+mz64KIWAPvr1ko1jOr+3RWZxdgE2BCtv85YJcy/67NapqDvlnt+zNpiPxuSReSFsltQ1rUd3dEPAv8FPivbLX8X4ETgN2AN0o1GBHLJF0BXClpBfA4sCWwW0TcDMwhfSk4QtIMYFmkS/5GA9dKWgg8QJoi2AfYNCKuBu4gLfS7WdKlpLUB3yzz951M+uz6uqQ/kBbpnVmi3krgZ5LOzX6+DngqIp7O9v8Q+KOkN4HfkxYH7gHsHhHfK7NPZjXBq/fNalxEBCnAPwrcAkwkrVL/ANlq+4i4kxTkrgDGAdsDN6yn6UuAH2fHvQrcCWyVtbeKtHr+DGAmad0BEXET8BXSQroXgbHZz1Oy/YuAz5Lm4p8nfQG4sMzfdzxwfnbcS6QvMBeVqLok+33vIH0pWgp8OdfOg8A/AyOAZ0gLG88H/Mx5q1tKnxdmZmZW75zpm5mZNQgHfTMzswbhoG9mZtYgHPTNzMwahIO+mZlZg3DQNzMzaxAO+mZmZg3CQd/MzKxBOOibmZk1CAd9MzOzBuGgb2Zm1iD+P1enQqv1vY5QAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 600x400 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# logistic regression\n",
    "lr = LogisticRegression().fit(X_train, y_train)\n",
    "pred_lr = lr.predict(X_test)\n",
    "print(classification_report(y_test, pred_lr))\n",
    "\n",
    "plot_confusion_matrix(lr, X_test, y_test, display_labels=le.inverse_transform(lr.classes_), cmap=plt.cm.Blues, normalize='true')\n",
    "scoring_LR = [accuracy_score(y_test, pred_lr), balanced_accuracy_score(y_test, pred_lr), f1_score(y_test, pred_lr, average='weighted')]\n",
    "\n",
    "#plot_feature_importances(lr.coef_[0])\n",
    "# Recall this is a classification problem with classes 0 and 1. \n",
    "# Notice that the coefficients are both positive and negative. \n",
    "# The positive scores indicate a feature that predicts class 1, whereas the negative scores indicate a feature that predicts class 0."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a0f8557e",
   "metadata": {},
   "source": [
    "### K Nearest Neighbor Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "84b706fc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           9       0.78      1.00      0.88         7\n",
      "          11       1.00      0.80      0.89        10\n",
      "\n",
      "    accuracy                           0.88        17\n",
      "   macro avg       0.89      0.90      0.88        17\n",
      "weighted avg       0.91      0.88      0.88        17\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x1dfec738340>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x400 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# KNN\n",
    "knn = KNeighborsClassifier(n_neighbors=5).fit(X_train, y_train)\n",
    "pred_knn = knn.predict(X_test)\n",
    "print(classification_report(y_test, pred_knn))\n",
    "plot_confusion_matrix(knn, X_test, y_test, display_labels=le.inverse_transform(knn.classes_), cmap=plt.cm.Blues, normalize='true')\n",
    "scoring_KNN = [accuracy_score(y_test, pred_knn), balanced_accuracy_score(y_test, pred_knn), f1_score(y_test, pred_knn, average='weighted')]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f23cfce2",
   "metadata": {},
   "source": [
    "### Radius Neighbor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "b526d77c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           9       0.41      1.00      0.58         7\n",
      "          11       0.00      0.00      0.00        10\n",
      "\n",
      "    accuracy                           0.41        17\n",
      "   macro avg       0.21      0.50      0.29        17\n",
      "weighted avg       0.17      0.41      0.24        17\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\franz\\Anaconda3\\envs\\KinematicAnalyses\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1248: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "C:\\Users\\franz\\Anaconda3\\envs\\KinematicAnalyses\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1248: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "C:\\Users\\franz\\Anaconda3\\envs\\KinematicAnalyses\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1248: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x1dfe98e79d0>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x400 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "rn = RadiusNeighborsClassifier(radius=10000.0).fit(X_train, y_train)\n",
    "pred_rn = rn.predict(X_test)\n",
    "print(classification_report(y_test, pred_rn))\n",
    "plot_confusion_matrix(rn, X_test, y_test, display_labels=le.inverse_transform(rn.classes_), cmap=plt.cm.Blues, normalize='true')\n",
    "scoring_RN = [accuracy_score(y_test, pred_rn), balanced_accuracy_score(y_test, pred_rn), f1_score(y_test, pred_rn, average='weighted')]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5c230124",
   "metadata": {},
   "source": [
    "### Gaussian Naive Bayes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "1bf2bf2b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           9       0.58      1.00      0.74         7\n",
      "          11       1.00      0.50      0.67        10\n",
      "\n",
      "    accuracy                           0.71        17\n",
      "   macro avg       0.79      0.75      0.70        17\n",
      "weighted avg       0.83      0.71      0.70        17\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x1dfeb68edc0>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x400 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "gnb = GaussianNB().fit(X_train, y_train)\n",
    "pred_gnb = gnb.predict(X_test)\n",
    "print(classification_report(y_test, pred_gnb))\n",
    "plot_confusion_matrix(gnb, X_test, y_test, display_labels=le.inverse_transform(gnb.classes_), cmap=plt.cm.Blues, normalize='true')\n",
    "scoring_GNB = [accuracy_score(y_test, pred_gnb), balanced_accuracy_score(y_test, pred_gnb), f1_score(y_test, pred_gnb, average='weighted')]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "021f95fa",
   "metadata": {},
   "source": [
    "## Model Evaluation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "93773a3c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "executions: 10.0\n"
     ]
    }
   ],
   "source": [
    "scores = [scoring_RF, scoring_SV, scoring_MLPC, scoring_PC, scoring_LR, scoring_KNN, scoring_RN, scoring_GNB]\n",
    "scores = pd.DataFrame(scores, index=[\"RF\",\"SV\",\"MLPC\",\"PC\",\"LR\",\"KNN\",\"RN\",\"GNB\"], columns = [\"Accuracy\",\"Weighted accuracy\", \"Weighted F1 score\"]) \n",
    "#scores.to_csv(\"C:/Users/franz/Documents/Bachelor Studium/BachelorArbeit/JupyterLabBA/files/model_scoring/model_scoring_{}.csv\".format(le.inverse_transform(rfc.classes_)),mode=\"a\")\n",
    "file = pd.read_csv(\"C:/Users/franz/Documents/Bachelor Studium/BachelorArbeit/JupyterLabBA/files/model_scoring/model_scoring_{}.csv\".format(le.inverse_transform(rfc.classes_)))\n",
    "print(\"executions: {}\".format((len(file)+1)/9))"
   ]
  }
 ],
 "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": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}