ModelCombo.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "7e6c3ad4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.7.0-dev20210830\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf \n",
    "from tensorflow import keras\n",
    "from keras.preprocessing.image import ImageDataGenerator\n",
    "import scipy\n",
    "import os\n",
    "import cv2\n",
    "import random\n",
    "from skimage import io\n",
    "import seaborn as sns\n",
    "from matplotlib import pyplot \n",
    "import pandas as pd\n",
    "import tensorflow.keras.backend as K\n",
    "import numpy as np\n",
    "np.random.seed(13)\n",
    "from sklearn.model_selection import train_test_split\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d85e2068",
   "metadata": {},
   "outputs": [],
   "source": [
    "NasNet = tf.keras.models.load_model('my_firstModel')\n",
    "MobileNet = tf.keras.models.load_model('my_secondModel')\n",
    "MobileNetV2 = tf.keras.models.load_model('my_thirdModel')\n",
    "EfficientNet = tf.keras.models.load_model('my_forthModel')\n",
    "TrainedModel_0 = [NasNet,MobileNet,MobileNetV2,EfficientNet]\n",
    "TrainedModel_1 = [NasNet,MobileNet]\n",
    "TrainedModel_2 = [MobileNetV2,EfficientNet]\n",
    "TrainedModel_3 = [NasNet,EfficientNet]\n",
    "TrainedModel_4 = [MobileNet,MobileNetV2]\n",
    "TrainedModel_5 = [NasNet,MobileNet,MobileNetV2]\n",
    "TrainedModel_6 = [MobileNet,MobileNetV2,EfficientNet]\n",
    "TrainedModel_7 = [NasNet,MobileNet,EfficientNet]\n",
    "TrainedModel_8 = [NasNet,MobileNetV2,EfficientNet]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "81248ea1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 3929 entries, 0 to 3928\n",
      "Data columns (total 3 columns):\n",
      " #   Column      Non-Null Count  Dtype \n",
      "---  ------      --------------  ----- \n",
      " 0   image_path  3929 non-null   object\n",
      " 1   mask_path   3929 non-null   object\n",
      " 2   diagnosis   3929 non-null   object\n",
      "dtypes: object(3)\n",
      "memory usage: 92.2+ KB\n"
     ]
    }
   ],
   "source": [
    "#Changed to working directory to the location of the dataset and loaded the dataset description for local host.\n",
    "os.chdir(\"..\\kaggle_3m\")\n",
    "brain_df = pd.read_csv('data_frame.csv')\n",
    "\n",
    "#For google colab\n",
    "#brain_df = pd.read_csv('/content/drive/MyDrive/kaggle_3m/Gdrive.csv')\n",
    "# Here we decided to drop unncessary coloums from the dataset\n",
    "brain_df_train = brain_df.drop(columns=['Unnamed: 0', 'patient'], axis=1)\n",
    "brain_df_train['diagnosis'] = brain_df['diagnosis'].apply(lambda x: str(x)) #changes the type of the values of the column to sting\n",
    "brain_df_train.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "1b2a75ef",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 3006 validated image filenames belonging to 2 classes.\n",
      "Found 333 validated image filenames belonging to 2 classes.\n",
      "Found 590 validated image filenames belonging to 2 classes.\n"
     ]
    }
   ],
   "source": [
    "train, test = train_test_split(brain_df_train, test_size=0.15) #splits the data into training and testing sets\n",
    "datagen = ImageDataGenerator(rescale=1./255., validation_split=0.1)\n",
    "\n",
    "train_generator = datagen.flow_from_dataframe(train,\n",
    "                                              directory='./',\n",
    "                                              x_col='image_path',\n",
    "                                              y_col='diagnosis',\n",
    "                                              subset='training',\n",
    "                                              class_mode='categorical',\n",
    "                                              batch_size=16,\n",
    "                                              shuffle=True,\n",
    "                                              target_size=(224,224)\n",
    "                                             )\n",
    "valid_generator = datagen.flow_from_dataframe(train,\n",
    "                                              directory='./',\n",
    "                                              x_col='image_path',\n",
    "                                              y_col='diagnosis',\n",
    "                                              subset='validation',\n",
    "                                              class_mode='categorical',\n",
    "                                              batch_size=16,\n",
    "                                              shuffle=True,\n",
    "                                              target_size=(224,224)\n",
    "                                             )\n",
    "test_datagen = ImageDataGenerator(rescale=1./255.)\n",
    "test_generator = test_datagen.flow_from_dataframe(test,\n",
    "                                                  directory='./',\n",
    "                                                  x_col='image_path',\n",
    "                                                  y_col='diagnosis',\n",
    "                                                  class_mode='categorical',\n",
    "                                                  batch_size=16,\n",
    "                                                  shuffle=False,\n",
    "                                                  target_size=(224,224)\n",
    "                                                 )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "31b216b5",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "f3eb3815",
   "metadata": {},
   "outputs": [],
   "source": [
    "#For TrainedModel_0\n",
    "labels_0 = [] \n",
    "for m in TrainedModel_0:\n",
    "    predicts = np.argmax(m.predict(test_generator), axis=1)\n",
    "    labels_0.append(predicts)\n",
    "# Ensemble with voting\n",
    "labels_0 = np.array(labels_0)\n",
    "labels_0 = np.transpose(labels_0, (1, 0))\n",
    "labls_0 = scipy.stats.mode(labels_0,axis=1)[0]\n",
    "labls_0 = np.squeeze(labls_0)\n",
    "\n",
    "\n",
    "#For TrainedModel_1\n",
    "labels_1 = [] \n",
    "for m in TrainedModel_1:\n",
    "    predicts = np.argmax(m.predict(test_generator), axis=1)\n",
    "    labels_1.append(predicts)\n",
    "# Ensemble with voting\n",
    "labels_1 = np.array(labels_1)\n",
    "labels_1 = np.transpose(labels_1, (1, 0))\n",
    "labls_1 = scipy.stats.mode(labels_1,axis=1)[0]\n",
    "labls_1 = np.squeeze(labls_1)\n",
    "\n",
    "#For TrainedModel_2\n",
    "labels_2 = [] \n",
    "for m in TrainedModel_2:\n",
    "    predicts = np.argmax(m.predict(test_generator), axis=1)\n",
    "    labels_2.append(predicts)\n",
    "# Ensemble with voting\n",
    "labels_2 = np.array(labels_2)\n",
    "labels_2 = np.transpose(labels_2, (1, 0))\n",
    "labls_2 = scipy.stats.mode(labels_2,axis=1)[0]\n",
    "labls_2 = np.squeeze(labls_2)\n",
    "\n",
    "#For TrainedModel_3\n",
    "labels_3 = [] \n",
    "for m in TrainedModel_3:\n",
    "    predicts = np.argmax(m.predict(test_generator), axis=1)\n",
    "    labels_3.append(predicts)\n",
    "# Ensemble with voting\n",
    "labels_3 = np.array(labels_3)\n",
    "labels_3 = np.transpose(labels_3, (1, 0))\n",
    "labls_3 = scipy.stats.mode(labels_3,axis=1)[0]\n",
    "labls_3 = np.squeeze(labls_3)\n",
    "\n",
    "#For TrainedModel_4\n",
    "labels_4 = [] \n",
    "for m in TrainedModel_4:\n",
    "    predicts = np.argmax(m.predict(test_generator), axis=1)\n",
    "    labels_4.append(predicts)\n",
    "# Ensemble with voting\n",
    "labels_4 = np.array(labels_4)\n",
    "labels_4 = np.transpose(labels_4, (1, 0))\n",
    "labls_4 = scipy.stats.mode(labels_4,axis=1)[0]\n",
    "labls_4 = np.squeeze(labls_4)\n",
    "\n",
    "#For TrainedModel_5\n",
    "labels_5 = [] \n",
    "for m in TrainedModel_5:\n",
    "    predicts = np.argmax(m.predict(test_generator), axis=1)\n",
    "    labels_5.append(predicts)\n",
    "# Ensemble with voting\n",
    "labels_5 = np.array(labels_5)\n",
    "labels_5 = np.transpose(labels_5, (1, 0))\n",
    "labls_5 = scipy.stats.mode(labels_5,axis=1)[0]\n",
    "labls_5 = np.squeeze(labls_5)\n",
    "\n",
    "#For TrainedModel_6\n",
    "labels_6 = [] \n",
    "for m in TrainedModel_6:\n",
    "    predicts = np.argmax(m.predict(test_generator), axis=1)\n",
    "    labels_6.append(predicts)\n",
    "# Ensemble with voting\n",
    "labels_6 = np.array(labels_6)\n",
    "labels_6 = np.transpose(labels_6, (1, 0))\n",
    "labls_6 = scipy.stats.mode(labels_6,axis=1)[0]\n",
    "labls_6 = np.squeeze(labls_6)\n",
    "\n",
    "#For TrainedModel_7\n",
    "labels_7 = [] \n",
    "for m in TrainedModel_7:\n",
    "    predicts = np.argmax(m.predict(test_generator), axis=1)\n",
    "    labels_7.append(predicts)\n",
    "# Ensemble with voting\n",
    "labels_7 = np.array(labels_7)\n",
    "labels_7 = np.transpose(labels_7, (1, 0))\n",
    "labls_7 = scipy.stats.mode(labels_7,axis=1)[0]\n",
    "labls_7 = np.squeeze(labls_7)\n",
    "\n",
    "#For TrainedModel_8\n",
    "labels_8 = [] \n",
    "for m in TrainedModel_8:\n",
    "    predicts = np.argmax(m.predict(test_generator), axis=1)\n",
    "    labels_8.append(predicts)\n",
    "# Ensemble with voting\n",
    "labels_8 = np.array(labels_8)\n",
    "labels_8 = np.transpose(labels_8, (1, 0))\n",
    "labls_8 = scipy.stats.mode(labels_8,axis=1)[0]\n",
    "labls_8= np.squeeze(labls_8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "efcbf82b",
   "metadata": {},
   "outputs": [],
   "source": [
    "original = np.asarray(test['diagnosis']).astype('int')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "e51b2098",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\hi\\appdata\\local\\programs\\python\\python39\\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\\hi\\appdata\\local\\programs\\python\\python39\\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\\hi\\appdata\\local\\programs\\python\\python39\\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\\hi\\appdata\\local\\programs\\python\\python39\\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\\hi\\appdata\\local\\programs\\python\\python39\\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\\hi\\appdata\\local\\programs\\python\\python39\\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"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.91      0.99      0.95       380\n",
      "           1       0.98      0.81      0.89       210\n",
      "\n",
      "    accuracy                           0.93       590\n",
      "   macro avg       0.94      0.90      0.92       590\n",
      "weighted avg       0.93      0.93      0.93       590\n",
      "\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.96      0.97      0.97       380\n",
      "           1       0.95      0.92      0.94       210\n",
      "\n",
      "    accuracy                           0.96       590\n",
      "   macro avg       0.95      0.95      0.95       590\n",
      "weighted avg       0.96      0.96      0.96       590\n",
      "\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.64      1.00      0.78       380\n",
      "           1       0.00      0.00      0.00       210\n",
      "\n",
      "    accuracy                           0.64       590\n",
      "   macro avg       0.32      0.50      0.39       590\n",
      "weighted avg       0.41      0.64      0.50       590\n",
      "\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.64      1.00      0.78       380\n",
      "           1       0.00      0.00      0.00       210\n",
      "\n",
      "    accuracy                           0.64       590\n",
      "   macro avg       0.32      0.50      0.39       590\n",
      "weighted avg       0.41      0.64      0.50       590\n",
      "\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.91      0.99      0.95       380\n",
      "           1       0.97      0.82      0.89       210\n",
      "\n",
      "    accuracy                           0.93       590\n",
      "   macro avg       0.94      0.91      0.92       590\n",
      "weighted avg       0.93      0.93      0.93       590\n",
      "\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.91      0.99      0.95       380\n",
      "           1       0.97      0.82      0.89       210\n",
      "\n",
      "    accuracy                           0.93       590\n",
      "   macro avg       0.94      0.91      0.92       590\n",
      "weighted avg       0.93      0.93      0.93       590\n",
      "\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.96      0.88      0.92       380\n",
      "           1       0.81      0.93      0.87       210\n",
      "\n",
      "    accuracy                           0.90       590\n",
      "   macro avg       0.88      0.91      0.89       590\n",
      "weighted avg       0.91      0.90      0.90       590\n",
      "\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.91      0.99      0.95       380\n",
      "           1       0.97      0.82      0.89       210\n",
      "\n",
      "    accuracy                           0.93       590\n",
      "   macro avg       0.94      0.91      0.92       590\n",
      "weighted avg       0.93      0.93      0.93       590\n",
      "\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.96      0.97      0.97       380\n",
      "           1       0.95      0.92      0.94       210\n",
      "\n",
      "    accuracy                           0.96       590\n",
      "   macro avg       0.95      0.95      0.95       590\n",
      "weighted avg       0.96      0.96      0.96       590\n",
      "\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.90      0.90      0.90       380\n",
      "           1       0.81      0.81      0.81       210\n",
      "\n",
      "    accuracy                           0.87       590\n",
      "   macro avg       0.86      0.86      0.86       590\n",
      "weighted avg       0.87      0.87      0.87       590\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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5RSQYumxDRIKhyzZEJBiFPIem56GJSEzKkpVszOwhM6s0szcz6m4xswozWxSVIRn7RptZuZm9Y2YXNqbvCmgisr88DAyqo36cu/eOyjMAZtYLuBw4MTrnfjMrytaAApqIxKQSlmzc/SVgYyO7MRR4zN13uvt7QDnQL9tJCmgiEuMJy1641swWR0PStlFdV2BlxjGroroGKaCJSEzSOTQzKzWzhRmltBHNTQCOAXoDq4Gxe9N3rXKKSEzSC2vdvQwo28Nz1n6ybWYPAjOjlxVAt4xDS6K6BilDE5GYXM2h1cXMOme8vAT4ZAV0BnC5mR1sZj2AnsCCbO+nDE1EYjxH19Wa2aPA2UCxma0C/hU428x6k56Gex/4LoC7LzGzacBSoBoY6e412dpQQBORmFzdy+nuw+qontjA8XcAd+xJGwpoIhKjm9NFJBiFfOuTApqIxOhpGyISDA05RSQYCmgiEgzNoYlIMDSHJiLB0JBTRIKhIaeIBCNVwCFNN6eLSDCUoYlIjObQRCQYhTvgVEATkVqUoYlIMHQdmogEo5BXORXQRCSmcMOZApqI1KI5NBEJhoacIhKMwg1nCmgiUouGnCISDA05RSQYhRvOFNBEpBYNOUUkGF7AOZoCmojEKEMTkWAU8qKAHvAoIsFQhrYHHnjgbgYPPpd16zZw6qnnf1r/ve99m2uuGU5NTYpZs+Zy883/nsdehuuou/+FI87tS/WGLbx9/vd321/U+jCOuvv7HNy9E6mdVaz44a/4+K8r9qpNa9GM7uOu59DPH0P1pm28P/JuqlZV0urMk+ly03CseTN8VzUVdzzM9j/9Za/aaioKNz9ThrZHpkx5gi9/eXisbuDA07noogv4whcGccop53HvvQ/kqXfh2/DEHN4dfmu9+zuO/Do7li7j7QtHsfz6eym59epGv3eLkg4c+/jtu9W3v+x8arZsZ+lZ11D52xl0GT0CgOqNW3n3n+7g7QtGsfz6X9L93uv3/AM1USk8UcnGzB4ys0ozezOjrp2ZzTazv0U/20b1Zmb3mVm5mS02s1Ma03cFtD0wb94CNm3aHKv7zneuYMyY+6mqqgJg3boNeejZgeHDBUup2by93v0te3ZjW5Ql7Xy3ghYlHWhW3BqAtpcM5LgZd3P8rHF0u/N7cFDj/um3vuA0Njw5F4DNz7xMqwEnAbBjyXtUr90IwMd/XcFBLVtgLcIY8KQSlkZ4GBhUq+4mYI679wTmRK8BBgM9o1IKTGhMA4kDmpldmfTckPTs2YMBA/rx0kvTmT17GqeeelK+u3TA2vHWe7QZdDoAh57ckxZdO9C8czEHH1tC24vO4K9fuYl3Bl+P16Rod8nARr1n807t2PXB+vSLmhQ12z6kqG2r2DFthnyRHW8uw6uq9+nnyRdP+Cfr+7q/BGysVT0UmBRtTwIuzqif7Gl/BtqYWedsbezNfym3Ar/bi/OD0KxZM9q2bc1ZZw2lb9+TmTr1fk444Yx8d+uAtPb+pyi55TscP2scH7+znI+WLIOaFK0GnMShnz+W4/84BoCDWh5M9YYtAPQoG02Lbh2wFs1p0aWY42eNA2DdQzPZ+MScrG22PK4bXUYPp/xbt+Tsc+1v+/myjY7uvjraXgN0jLa7AiszjlsV1a2mAQ0GNDNbXN+ujIbrOq+UdJpIs2ZtKSo6vKFmClpFxWqmT38WgIUL3yCVcoqL27F+fe3/iCTXUtt3sOKH9336utfLZexcsYbD+vViw5NzWf2LKbud817pnUB6Du2osd+n/LKfxvbvWrOR5l2K2bVmAxQdRFGrw6jZtA2A5p3a06NsNMuvv5eq5Wty+Mn2r6QX1mb+3kfK3L2s0e26u5nt1ZpEtiFnR2A4cFEdpd7JIncvc/e+7t435GAGMGPG8wwcmB7mHHtsD1q0aK5glidFRxyGNU//H91+2Pl8uGApqe072P7yYtoM+SLN2qfn04paH07zrkc26j23zF5A+6+dA0CbIQPY9qfFn7Z1zMM/44O7JvPhwrdz8GnyJ+kcWubvfVQaE8zWfjKUjH5WRvUVQLeM40qiugZlG3LOBA5390W1d5jZfzeis0GZPPlXnHnm6RQXt6W8fD63334PkyY9TlnZ3bz22myqqqq4+uof5LubwTr6Vzdw+Omfo1nbIzhx/kRW3/PopwFswyPPcvCxJXS/ZxR4eqJ+xY9/BcDHf1vJ6jFTOeaRW7CDDsKrq1n50wfYVbEua5sbHp9N93uvp9dLv6F68zbevzY9bC0eMYQWR3em06jL6DTqMgDe/dYtnw5lC1nK9+uFGzOAEcBd0c/pGfXXmtljwGnAloyhab3Mc9z5li2PKuTLWg54r3Tok+8uSEJ9VkxP9P1N3+r+lUS/s48sf7rB9szsUeBsoBhYC/wr8J/ANOAoYDlwqbtvNDMDfk16VfQj4Ep3X5itD2GsM4vIPpOrW5/cfVg9u86t41gHRu5pGwpoIhKjp22ISDD0tA0RCUYhP21DAU1EYjTkFJFgaMgpIsHI9aVcuaSnbYhIMJShiUiMFgVEJBiaQxORYGiVU0SCoSGniASjkFc5FdBEJEZzaCISDM2hiUgwNIcmIsHQHJqIBEMZmogEQ3NoIhKM/fwlKfuUApqIxBRuOFNAE5FaNIcmIsFQQBORYBTyZRt6wKOIBEMZmojEaMgpIsHQdWgiEoxCnkNTQBORGA05RSQYytBEJBi5zNDM7H1gG1ADVLt7XzNrBzwOHA28D1zq7puSvL8u2xCRGE/4Zw98yd17u3vf6PVNwBx37wnMiV4nooAmIjEp90RlLwwFJkXbk4CLk76RApqIxOQ4Q3PgeTN7zcxKo7qO7r462l4DdEzad82hiUhM0mwrClClGVVl7l5W67Az3L3CzDoAs83s7cyd7u5mljjdU0ATkZikF9ZGwat2AKt9TEX0s9LM/gD0A9aaWWd3X21mnYHKRB1AQ04RqSVXc2hmdpiZtfpkG7gAeBOYAYyIDhsBTE/ad2VoIhKTw1ufOgJ/MDNIx57fu/uzZvYqMM3MrgKWA5cmbUABTURicvUIbndfBpxcR/0G4Nx90YYCmojE6OZ0EQmGeyrfXUhMiwIiEgxlaCISo6dtiEgw9LQNEQmGMjQRCYYyNBEJRq6uQ9sfFNBEJEbXoYlIMDTkFJFgaFFARIKhDE1EgqFFAREJhjI0EQmG5tBEJBjK0EQkGJpDE5Fg6MJaEQmGMjQRCUYhz6HpibUiEgxlaCISozk0EQlGIQ85FdBEJEYBTUSCUbjhDKyQo3FTYGal7l6W735IMvr7C4tWOfdeab47IHtFf38BUUATkWAooIlIMBTQ9p7mXwqb/v4CokUBEQmGMjQRCYYC2l4ws0Fm9o6ZlZvZTfnujzSemT1kZpVm9ma++yL7jgJaQmZWBIwHBgO9gGFm1iu/vZI98DAwKN+dkH1LAS25fkC5uy9z9yrgMWBonvskjeTuLwEb890P2bcU0JLrCqzMeL0qqhORPFFAE5FgKKAlVwF0y3hdEtWJSJ4ooCX3KtDTzHqYWQvgcmBGnvskckBTQEvI3auBa4HngLeAae6+JL+9ksYys0eBV4DjzWyVmV2V7z7J3tOdAiISDGVoIhIMBTQRCYYCmogEQwFNRIKhgCYiwVBAE5FgKKCJSDAU0EQkGP8PeVyj2dvzeb4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 360x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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Z2TakwbHLJM2T9Lqk41rSd2doZpZTxCHn7cAoYHyj8hsj4rqGBZL6AmcA+wG7Ao9L2isiaptqwBmamW0VEfEUsLSF1YcCEyJiTUT8DZgHDGjuJAc0M8upK3DbDBdImpUNSXfMynoC7zWosyAra5IDmpnlFDqHJqlS0owGW2ULmrsZ2BPoBywCrt+cvnsOzcxyCp1Di4gqoGoTz1n8+b6kW4FJ2ctqoHeDqr2ysiY5QzOznK055JTUo8HLU4DPV0AfBM6QtJ2k3YE+wPPNvZ8zNDPLKdadApLuBA4HukpaAFwBHC6pH/Wj1vnAeQARMUfSROBVoAYY3twKJzigmVkjUaTLNiLizA0Uj2mi/jXANZvShgOameX4Xk4zS4YDmpklw48PMrNk+GkbZpYMDznNLBkOaGaWDM+hmVkyPIdmZsnwkNPMkuEhp5klo66EQ5qftmFmyXCGZmY5nkMzs2SU7oDTAc3MGnGGZmbJ8HVoZpaMUl7ldEAzs5zSDWcOaGbWiOfQzCwZHnKaWTJKN5w5oJlZIx5ymlkyPOQ0s2SUbjhzQDOzRjzkNLNkRAnnaA5oZpbjDM3MklHKiwJ+wKOZbRWSxkpaIml2g7IukqZIejP7uWNWLkkjJc2TNEtS/5a04QythSq2q+Cuh8ZQUVFBWXkZjzz0OL/99S3c9dAYOnTsAMBO3brwykuzOf/sn7Ryb9O032/Po9sx/Vn74Qr+8s2Lv3B8tx+cyC6nHQZAm/IyOvTpybS+/4ea5asLblMV5ew/ajg7HLA765at4pXK3/HZex/Q5Rv7s9e/nYkqyom1Nbxx1R0sfWZOwe1sS4qYn90OjALGNyi7FJgaESMkXZq9vgQ4AeiTbQOBm7OfTXJAa6G1a9byL6dU8snqTykvL2fiH8fy5ON/5lsnDVtf56bbrmPKw0+2XicTt3DCn3h3zKPsP2r4Bo/Pv2kS82+aBEC3Y/vz5fOGtDiYtevdja/87vvMOPWqXHmvbx/BuuWreGbQj9nl5EPY69+/zazK37Fu6UpePuta1ixeRsd9etF/wuU81e8Hm/cBtxHFGnJGxFOSdmtUPBQ4PNsfBzxJfUAbCoyPiACmS+osqUdELGqqDQ85N8Enqz8FoLxtOeVty6n/b12vY8cOHDL4a0yZPK21upe8ZdNfY10LA9QupxzKovv+sv51j9MOY+AjVzNo6gj2vfZ70KZlD/3qdvzBLJz4FACLH3qOLoftB8DK2fNZs3gZAKteW0BZuwpUkUZ+UFfgVqDuDYLU+0D3bL8n8F6DeguysiYVHNAknVvouaWqTZs2TJo2gRfmTuXPT07nlZfWTwVwzJAj+MtTz7NqVeHDG9sy2rSvoOsRB7J40nMAdOizK7ucfAjPn3gF04+6FGrr6JENTZvTrkcXPqv+CICoraNm5ae07dIpV6f7iQNZ8de/EWtrtuwHaSVR4B9JlZJmNNgqN6nd+gxhs9LDzflfypXAbZvTeKmpq6vjxCPOoNMOHbll/A3stc+evPHaWwCcdOrxTPz9fa3cQwPoduxXWf7C6+uHm10G70+nA3Zn4KPXAFDWroK1H64A4MDbfkL7/7EzbdqW065XVwZNHQHAu7c+zMIJf2q2rQ5796LPv3+bF0//VZE+zdZXaLYVEVVA1SaetvjzoaSkHsCSrLwa6N2gXq+srElNBjRJszZ2iL+nhhs6rxKoBNipQy92aNe1uX6UlJUrVjH9mRl846iv88Zrb7Fjl84c2H8/zj/HiwHbgl1OPiQ33ESwcOJTzLtmwhfqvnLuDcDG59A+W7SUdj13Ys2ipaisDeWd2rNu6UoAtuvRhX63XcTsC0bz6TuLi/eBtrKtfGHtg8A5wIjs5wMNyi+QNIH6xYCPm5s/g+aHnN2Bs4GTNrB9tLGTIqIqIg6OiINTCWZddtqRTjt0BGC7dttx2DcH8vab8wE44Z+O5onHnmbtmrWt2EMDKO/Uni6H9OWDR2asL1v69Gy6nziQiq471Nfp3IF2vVr29/KDR19k19O/AUD3kwauX8ks32F7+t9xCW9e/QeWv/DGFv4UratYc2iS7gSeBfaWtEDSMOoD2TGS3gSOzl4DTAbeBuYBtwItWnFpbsg5CegYETM30LknW9JAKnbu3pVrR11FWVkb1KYNkx+YwhOPPQ3Aiaccxy2/+4cafbeK/W/5V7p8vS9tu3TiGy+P5q1r70blZQAsGP84ADsPGcCHf5pF7Sdr1p+3+o1q5o2YSP+7LkdtRKyrZe5lY/lswYfNtln9h2l8ZdRwDpv+W9YtX8Ws80YC0HvYcWy/e3f2uOg09rjoNABe+tav1g9lS1ldFG2V88yNHDpqA3UD2PBydhMURer85/boelDpXnZs3FK2d2t3wQp07OIJBX1/03e+fGpB/2Z//869rf59UWmsM5vZFlPKtz45oJlZjp+2YWbJ8NM2zCwZHnKaWTI85DSzZHjIaWbJKPalXMXkp22YWTKcoZlZjhcFzCwZnkMzs2R4ldPMkuEhp5klo5RXOR3QzCzHc2hmlgzPoZlZMjyHZmbJ8ByamSXDGZqZJcNzaGaWjGJ9ScrW4IBmZjmlG84c0MysEc+hmVkyHNDMLBmlfNmGH/BoZslwhmZmOR5ymlkyfB2amSWjmHNokuYDK4FaoCYiDpbUBbgL2A2YD5weEcsKeX/PoZlZTh1R0LYJjoiIfhFxcPb6UmBqRPQBpmavC+KAZmY5EVHQthmGAuOy/XHAyYW+kQOameUUOUML4DFJL0qqzMq6R8SibP99oHuhffccmpnlFLookAWoygZFVRFR1ajaYRFRLWlnYIqk13JtR4SkgtM9BzQzyyn05vQseDUOYI3rVGc/l0i6DxgALJbUIyIWSeoBLCmoA3jIaWaNRIF/miOpg6ROn+8DxwKzgQeBc7Jq5wAPFNp3Z2hmllPExwd1B+6TBPWx5w8R8YikF4CJkoYB7wCnF9qAA5qZ5RTrwtqIeBs4cAPlHwFHbYk2HNDMLMcPeDSzZPjWJzNLhjM0M0uGMzQzS0ZEXWt3oWC+Ds3MkuEMzcxy/IBHM0tGKX+ngAOameU4QzOzZDhDM7Nk+Do0M0uGr0Mzs2R4yGlmyfCigJklwxmamSXDiwJmlgxnaGaWDM+hmVkynKGZWTI8h2ZmyfCFtWaWDGdoZpaMUp5D8xNrzSwZztDMLMdzaGaWjFIecjqgmVmOA5qZJaN0wxmolKPxtkBSZURUtXY/rDD+/aXFq5ybr7K1O2Cbxb+/hDigmVkyHNDMLBkOaJvP8y+lzb+/hHhRwMyS4QzNzJLhgLYZJB0v6XVJ8yRd2tr9sZaTNFbSEkmzW7svtuU4oBVIUhkwGjgB6AucKalv6/bKNsHtwPGt3QnbshzQCjcAmBcRb0fEWmACMLSV+2QtFBFPAUtbux+2ZTmgFa4n8F6D1wuyMjNrJQ5oZpYMB7TCVQO9G7zulZWZWStxQCvcC0AfSbtLqgDOAB5s5T6Z/UNzQCtQRNQAFwCPAnOBiRExp3V7ZS0l6U7gWWBvSQskDWvtPtnm850CZpYMZ2hmlgwHNDNLhgOamSXDAc3MkuGAZmbJcEAzs2Q4oJlZMhzQzCwZ/x9RTwCNdpvzDQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 360x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAATQAAAEvCAYAAADLkD3HAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8rg+JYAAAACXBIWXMAAAsTAAALEwEAmpwYAAAYPklEQVR4nO3de3hV1Z3/8fc3CQEERBHKhMuM2sEbPohAEUqxURCV2kHor1R+KjyVmTiKra22Py91qra1iqPSQSmPaWGE3oAKjhZvXIRSKRXwUhTwEhmQxABF5CIoITnf3x/Z0rMhNzY5JGf5efHsh33W3vusdYz58l2XvY+5OyIiIchp6gaIiDQWBTQRCYYCmogEQwFNRIKhgCYiwVBAE5Fg5GW6ggPbN2hdSBZr3WVwUzdBEqqsKLMk1yX9nW3R8dRE9TWmjAc0EckyqaqmbkFiCmgiEueppm5BYgpoIhKXUkATkUC4MjQRCYYyNBEJhjI0EQmGZjlFJBhZnKHpTgERCYYyNBGJ06SAiIRCyzZEJBzK0EQkGMrQRCQYWrYhIsFQhiYiwdAYmogEQxmaiARDGZqIhMJdkwIiEgp1OUUkGOpyikgwlKGJSDC0sFZEgqEMTUSCkcVjaHrAo4gEQxmaiMSpyykiwcjiLqcCmojEKaCJSCh065OIhEMZmogEI4snBbRsQ0TiUqlkWz3MrJWZrTSzv5rZWjO7Oyo/xcxeMrMSM5ttZvlRecvodUl0/OT66lBAE5E4TyXb6rcfuNDdzwF6A5eY2QBgIjDJ3f8Z+BAYH50/HvgwKp8UnVcnBTQRictQhubVPopetog2By4EHo/KZwCXR/sjotdEx4eYmdVVhwKaiMRlLkPDzHLN7DVgG7AQeBfY6e6V0SmlQNdovyuwGSA6vgs4qa73V0ATkbiEGZqZFZnZ6rSt6NC3dvcqd+8NdAP6A2c0ZtM1yykicQmXbbh7MVDcwHN3mtkSYCBwgpnlRVlYN6AsOq0M6A6Umlke0B74oK73VYYmInEZ6nKaWSczOyHabw1cBKwHlgD/JzptHPBktP9U9Jro+Avu7nXVoQxNROIyt7C2AJhhZrlUJ1Nz3H2+ma0DZpnZT4BXgWnR+dOAX5lZCbADuKK+ChTQRCQuQwtr3X0NcG4N5RuoHk87tPwT4OtHUocCmojEZfGtTxpDE5FgKEMTkbgsvpdTAU1E4rK4y6mAJiJxCmgiEoy6l3o1awpoIhKnDE1EgqGAJiLB0CyniARDGZqIBEOTAiISDGVoIhIMBTQRCYYmBUQkFJ7SGJqIhEJdThEJhrqcIhKMLO5y6gGPIhIMZWgiEqcxNBEJRhYHtM9Ul3P//gqu+NcbGTXuekZceS2P/PJXtZ67cMmLnD3oUt5Y//ZR11v6/hbG/Nt3uHT0Ndz8H/dy4MABAGbMmse/XFnEyLHXMf7bt/L+lq1HXZfUrGXLlqxYPp+XVy/kr6+9wJ0/vBmACwoHsfKl53jt1cVMn/YzcnNzm7ilzYB7sq0Z+EwFtPz8FkyffB/zZvycx2dMYflLL/PXN9Yfdt7evfv49e+fpNdZpx/R+//P0wuZMu3Xh5VPmjqdq79xOc/Omc7x7doyd/7zAJzZ4/PMnjaZJ2ZO5aILvsSDU6Yn+2BSr/379zN02Gj69ruIvv2GcfGwQgYO6Mf0aT/jyquup/e5Q3jvvVLGXn1E35oWplQq2dYM1BvQzOwMM7vFzCZH2y1mduaxaFxjMzOOO641AJWVlVRWVmJmh5338C9mcs1VXye/Zf7BsqqqKh545Jd8Y/y3GTn2Oub8zzMNqtPdeenlvzKscDAAI4YP5YVlKwDo3/ccWrdqBcA5Pc9g69+2H9Xnk7rt3bsPgBYt8shr0YKqqioqKip4550NACxatIxRI4c3ZRObh5Qn25qBOgOamd0CzAIMWBltBvzOzG7NfPMaX1VVFV8bN4HzLxvDwC+cS6+eZ8SOr3urhC3btvPlL8a/93Te/Odp17YNs6dNZvYv/4vHn3qO0ve31Fvfzl27ade2DXl51V2Zzp06su1vHxx23rw/LGDwgH5H8cmkPjk5OaxetYDysjUsXryMlateJS8vj759egEwatRX6Na9SxO3shnwVLKtGahvUmA80NPdD6QXmtlDwFrgvkw1LFNyc3OZO2MKu/d8xI23/Zh3Nmykx6knA5BKpbj/4WLu+cHNh13355Wv8Pa7G1mw5EUAPtq7l02by2jb5jjGf/s2AHbt2cOBA5UHM7B7f/g9Op3Uod42/eH5F1j75ts8NuX+RvqUUpNUKkW/Lwyjffvjmfv7afTseTpXXnU9Dz5wFy1b5rNw0TKqqprHL2aTaibZVhL1BbQU0AXYdEh5QXSsRmZWBBQB/PzBn/CvY8ccTRsz4vh2benfpxcv/mX1wYC2d9/HlGzYxDdv+H8AbN/xId+65W4enngn7nD7d69j0Hl9D3uvuTOmANVjaGVbtjJh/FUHj7k7ez7aS2VlFXl5uWz923Y+1+mkg8dXrHqV4hmzeGzK/eTn5x/23tL4du3azdI/LufiYYU8NOlRCi8cBcBFQ8+nR49Tm7h1Tc+byXhYEvWNoX0HWGxmz5pZcbQ9BywGbqztIncvdvd+7t6vOQWzHR/uZPeejwD4ZP9+Vqx6lVP+qfvB4+3atuHFZ2azYO4MFsydQa+eZ/DwxDs5+8zTGHReH2Y/8TQHKisB2PheKfs+/qTeOs2M/n16sWDpnwB48plFXDh4IADr3y7h7vsn88jEOznpxBMa+dNKuo4dO9C+/fEAtGrViqFDzuett96lU/SPS35+Pt//3gSKi2uf+f7MyOIxtDozNHd/zsxOA/oDXaPiMmCVu1dlunGN7W8ffMgPfvIAVakUnnIuvnAwhYPO45FfzKTnGadxweABtV77ta9eQln5NkZ/81u4Oyee0J7J9/2wQfV+97pr+P6d9/Fw8UzOPO3zjLpsGAAPTpnGvo8/4aY7fgpAQedOPHL/XUf9OeVwBQWdo2UZOeTk5PD443/g6WcWMfHeOxj+laHk5OTw6KMzWbJ0eVM3tek1k/GwJMwzvH7kwPYNzSN0SyKtuwxu6iZIQpUVZYdP4TfA3h9dmeh3ts0Pf5OovsakOwVEJC6Lx9AU0EQkrpmMhyWhgCYicVk8hqaAJiJxytBEJBQhr0MTEckaCmgiEpehhbVm1t3MlpjZOjNba2Y3RuV3mVmZmb0WbcPTrrnNzErM7C0zu7i+OtTlFJG4zI2hVQI3u/srZtYOeNnMFkbHJrn7A+knm9lZwBVAT6pvwVxkZqfVtahfAU1E4jI0y+nu5UB5tL/HzNbz9zuQajICmOXu+4H/NbMSqu9aWlHbBepyikjcMbiX08xOBs4FXoqKbjCzNWY23cxOjMq6ApvTLiul7gCogCYicZ7yRJuZFZnZ6rStqKb3N7O2wFzgO+6+G5gKfB7oTXUG92DStqvLKSJxCcfQ3L0YKK7rHDNrQXUw+427z4uu25p2/BfA/OhlGdA97fJuUVmtlKGJSFyGvlPAqp93Pw1Y7+4PpZUXpJ02Engj2n8KuMLMWprZKUAPqp+aXStlaCISl7lZzkHA1cDrZvZaVHY7MMbMegMObASuBXD3tWY2B1hH9QzphPoeW6aAJiJxGQpo7v4i1d9Jcqhav3HI3e8B7mloHQpoIhKT6WckZpICmojE6eZ0EQmGApqIhMIV0EQkGApoIhKM7H0cmgKaiMSpyyki4cjigKZbn0QkGMrQRCROY2giEgqNoYlIOJShiUgolKGJSDiUoYlIKDL0HSnHhAKaiMQpoIlIKJShiUg4FNBEJBTK0EQkGApoIhIMBTQRCYfX9MVM2UEBTURilKGJSDA8pQxNRAKRzRmaHvAoIsFQhiYiMa5JAREJRTZ3ORXQRCRGkwIiEgzP3uc7KqCJSJwyNBEJhgKaiARDXU4RCYYyNBEJhtahiUgwsnkdmm59EpGYlFuirT5m1t3MlpjZOjNba2Y3RuUdzGyhmb0T/X1iVG5mNtnMSsxsjZn1qa8OBTQRiXG3RFsDVAI3u/tZwABggpmdBdwKLHb3HsDi6DXApUCPaCsCptZXgQKaiMR4yhJt9b6ve7m7vxLt7wHWA12BEcCM6LQZwOXR/ghgplf7C3CCmRXUVYfG0EQk5lgs2zCzk4FzgZeAzu5eHh3aAnSO9rsCm9MuK43KyqmFApqIxCRdtmFmRVR3DT9V7O7FNZzXFpgLfMfdd5v9vT53dzNLHFIV0EQkpiED/DWJgtdhASydmbWgOpj9xt3nRcVbzazA3cujLuW2qLwM6J52ebeorFYaQxORY8KqU7FpwHp3fyjt0FPAuGh/HPBkWvnYaLZzALArrWtaI2VoIhKTwYW1g4CrgdfN7LWo7HbgPmCOmY0HNgGjo2PPAMOBEmAf8M36KlBAE5GYTE0KuPuLQG3RckgN5zsw4UjqUEATkZikY2jNgQKaiMToXk4RCYYeHyQiwVCXsw6tuwzOdBWSQXM7fLmpmyDHmLqcIhIMZWgiEowsHkJTQBOROGVoIhIMjaGJSDCy+AncCmgiEue13p3U/CmgiUhMKotnBRTQRCQmpQxNREKRzV1OPeBRRIKhDE1EYjTLKSLByOYupwKaiMQoQxORYCigiUgw1OUUkWAk/J7hZkEBTURitLBWRIKRxXc+KaCJSJwmBUQkGClTl1NEAqEup4gEQ11OEQmGlm2ISDC0bENEgqExNBEJRjZ3OfWARxEJhjI0EYnRLKeIBENjaCISjGweQ1NAE5GYbO5yalJARGJSCbf6mNl0M9tmZm+kld1lZmVm9lq0DU87dpuZlZjZW2Z2cUParoAmIjFuybYGeAy4pIbySe7eO9qeATCzs4ArgJ7RNT83s9z6KlBAE5GYTGVo7r4M2NHAZowAZrn7fnf/X6AE6F/fRQpoIhKTqYBWhxvMbE3UJT0xKusKbE47pzQqq5MCmojEeMLNzIrMbHXaVtSA6qYCnwd6A+XAg0fTds1yikhM0mUb7l4MFB/hNVs/3TezXwDzo5dlQPe0U7tFZXVShiYiMceyy2lmBWkvRwKfzoA+BVxhZi3N7BSgB7CyvvdThiYiMZlah2ZmvwMKgY5mVgrcCRSaWW+qe60bgWsB3H2tmc0B1gGVwAR3r6qvDgU0EYnJ1K1P7j6mhuJpdZx/D3DPkdShgCYiMbr1SUSCkc23PimgiUiMnrYhIsFIZXFI07INEQmGMjQRidEYmogEI3s7nApoInIIZWgiEgytQxORYGTzLKcCmojEZG84U0ATkUNoDE1EgqEup4gEI3vDmQKaiBxCXU4RCYa6nCISjOwNZwpoInIIdTlFJBiexTmaApqIxChDE5FgZPOkgB7wKCLBUIbWQC1btmTpC3PJb9mSvLxc5s17mrt/9CAXFA5i4sT/ID+/Ba+88jr/VnQzVVX1fn2gJNB7UhH/cNG57N++myWFtxx2/J+vv4xuo74IgOXl0q5HV57teS0Hdu5NXGdOfh59Hr6O9r1O4cCHH7Hq2sl8vHk7nc4/m7N+MIac/FxSFVWs/dFv2L58XeJ6mpPszc+UoTXY/v37GTpsNH37XUTffsO4eFghAwf0Y/q0n3HlVdfT+9whvPdeKWOv/npTNzVYm2cvY8WYibUeL/n5fJYOvZ2lQ29n3T2z2b5ifYODWevuHRk0747Dyv/x/xZSsXMviwfexLuPPkvPO6q/WrJixx5eGvufLLngVl65cSp9Hrk+2YdqhlJ4oq05UEA7Anv37gOgRYs88lq0oKqqioqKCt55ZwMAixYtY9TI4U3ZxKB98Jc3qdj5UYPO7TZyIGVP/Pnvr782iPOf/TGFi37KOfePh5yGPfSr4OJ+bJ7zJwDen/8SHb90NgC73tjEJ1t3ArDnzVJyW+WTkx9GhyeVcGsOEgc0M/tmYzYkG+Tk5LB61QLKy9awePEyVq56lby8PPr26QXAqFFfoVv3Lk3cSsltnc/nLjiH959eCUDbHl3oOmIgf/rqXSwdejueStH9a19q0Hu1KjiRj9//AACvSlG5Zx/5HdrFzim4rD+7Xt9IqqKycT9IE/GEf5qDo/kn5W7gvxurIdkglUrR7wvDaN/+eOb+fho9e57OlVddz4MP3EXLlvksXLSMqqrm8m/VZ1fnYX3Ysertg93NToPP5oRep/Dl534MQG6rfPZv3w1A/+nf5bh/7EROfh6tu3akcNFPAdjwy+d5b9Yf662r3eld6XnHGP78jXsz9GmOvWz+P7jOgGZma2o7BHSu47oioAjActuTk9MmcQObo127drP0j8u5eFghD016lMILRwFw0dDz6dHj1CZunXQbMZDStO4mBu/NWcb6n84+7NyV10wCqsfQ+vzXv7N81E9ixz8p/5DWXU7ik/IdWG4Oee2Oo2LHHgBaFXSg//SbeOVbU9m3aVvmPtAx1lyyrSTq63J2BsYCX61h+6C2i9y92N37uXu/UIJZx44daN/+eABatWrF0CHn89Zb79Kp00kA5Ofn8/3vTaC4+FdN2czPvLx2rTlp4Jlsef7lg2Xb/7SWLpedR37H6p9fixPa0Lpbxwa935YFL9N99GAAulx2HtuXr62u5/jjGPDr77PunlnsWPV2I3+KppXNY2j1dTnnA23d/bVDD5jZ0kw0qLkqKOjM9Gk/Izc3h5ycHB5//A88/cwiJt57B8O/MpScnBwefXQmS5Yub+qmBqvv1Bvo+MUzye/QjmGvPMyb/zmXnBa5AGycuRiAguFfYNsfX6dq3/6D1+15u4z1E+fwxVm3Qk4OfqCKNbf9Nx+Xbq+3zk2/XUqfR65nyIqHOLBzL6uvfRiAU68ZRptTOnP6TSM5/aaRAPz5ivuoiLqy2Szl2ZuhmWe48Xn5XbP3v44wt8OXm7oJktCILb9N9P1NV/3TqES/s7/eNK/Jvy8qjHlmEWk0zWVNWRIKaCISk82TAgpoIhLTXAb4k1BAE5EYdTlFJBjZ3OXUvZwiEpOpdWhmNt3MtpnZG2llHcxsoZm9E/19YlRuZjbZzErMbI2Z9WlI2xXQRCTG3RNtDfAYcMkhZbcCi929B7A4eg1wKdAj2oqAqQ2pQAFNRI4Jd18G7DikeAQwI9qfAVyeVj7Tq/0FOMHMCuqrQ2NoIhJzjCcFOrt7ebS/hb/fI94V2Jx2XmlUVk4dlKGJSEzSMTQzKzKz1Wlb0ZHU69X91qOKpsrQRCQm6SynuxcDxUd42VYzK3D38qhL+eljS8qA7mnndYvK6qQMTURijvEjuJ8CxkX744An08rHRrOdA4BdaV3TWilDE5GYTD2wwsx+BxQCHc2sFLgTuA+YY2bjgU3A6Oj0Z4DhQAmwD2jQE7IV0EQkJlO3Prn7mFoODanhXAcmHGkdCmgiEpPNdwoooIlIjO7lFJFgZPqhr5mkgCYiMcrQRCQYGkMTkWBk85ekKKCJSEz2hjMFNBE5hMbQRCQYCmgiEoxsXrahm9NFJBjK0EQkRl1OEQmG1qGJSDCyeQxNAU1EYtTlFJFgKEMTkWAoQxORYGhSQESCoZvTRSQYytBEJBjK0EQkGMrQRCQYytBEJBjK0EQkGMrQRCQYytBEJBjuqaZuQmJ6wKOIBEMZmojE6F5OEQmGnrYhIsFQhiYiwVCGJiLB0Do0EQmG1qGJSDDU5RSRYGhSQESCkckMzcw2AnuAKqDS3fuZWQdgNnAysBEY7e4fJnl/3SkgIjEp90TbEbjA3Xu7e7/o9a3AYnfvASyOXieigCYiMe6eaDsKI4AZ0f4M4PKkb6SAJiIxKTzR1kAOLDCzl82sKCrr7O7l0f4WoHPStmsMTURikmZbUYAqSisqdvfiQ077kruXmdnngIVm9uYhdbuZJU73FNBEJCbpwtooeB0awA49pyz6e5uZPQH0B7aaWYG7l5tZAbAtUQNQl1NEDuEJ/9THzNqYWbtP94FhwBvAU8C46LRxwJNJ264MTURiMnjrU2fgCTOD6tjzW3d/zsxWAXPMbDywCRidtAIFNBGJydQ6NHffAJxTQ/kHwJDGqENdThEJhjI0EYnRzekiEgzdnC4iwVBAE5FgZG84A8vmaNwcmFlRDauhJUvo5xcWzXIevaL6T5FmTD+/gCigiUgwFNBEJBgKaEdP4y/ZTT+/gGhSQESCoQxNRIKhgHYUzOwSM3vLzErMLPFz0OXYM7PpZrbNzN5o6rZI41FAS8jMcoEpwKXAWcAYMzuraVslR+Ax4JKmboQ0LgW05PoDJe6+wd0rgFlUf9mDZAF3XwbsaOp2SONSQEuuK7A57XVpVCYiTUQBTUSCoYCWXBnQPe11t6hMRJqIAlpyq4AeZnaKmeUDV1D9ZQ8i0kQU0BJy90rgBuB5YD0wx93XNm2rpKHM7HfACuB0MyuNvqBDspzuFBCRYChDE5FgKKCJSDAU0EQkGApoIhIMBTQRCYYCmogEQwFNRIKhgCYiwfj/NcMLVQmHjq8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 360x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score, confusion_matrix, classification_report\n",
    "import seaborn as sns\n",
    "\n",
    "cm_0 = confusion_matrix(original, labls_0)\n",
    "cm_1 = confusion_matrix(original, labls_1)\n",
    "cm_2 = confusion_matrix(original, labls_2)\n",
    "cm_3 = confusion_matrix(original, labls_3)\n",
    "cm_4 = confusion_matrix(original, labls_4)\n",
    "cm_5 = confusion_matrix(original, labls_5)\n",
    "cm_6 = confusion_matrix(original, labls_6)\n",
    "cm_7 = confusion_matrix(original, labls_7)\n",
    "cm_8 = confusion_matrix(original, labls_8)\n",
    "\n",
    "report_0 = classification_report(original, labls_0, labels = [0,1])\n",
    "report_1 = classification_report(original, labls_1, labels = [0,1])\n",
    "report_2 = classification_report(original, labls_2, labels = [0,1])\n",
    "report_3 = classification_report(original, labls_3, labels = [0,1])\n",
    "report_4 = classification_report(original, labls_4, labels = [0,1])\n",
    "report_5 = classification_report(original, labls_5, labels = [0,1])\n",
    "report_6 = classification_report(original, labls_6, labels = [0,1])\n",
    "report_7 = classification_report(original, labls_7, labels = [0,1])\n",
    "report_8 = classification_report(original, labls_8, labels = [0,1])\n",
    "\n",
    "print(report_0)\n",
    "pyplot.figure(figsize = (5,5))\n",
    "sns.heatmap(cm_0, annot=True)\n",
    "\n",
    "print(report_1)\n",
    "pyplot.figure(figsize = (5,5))\n",
    "sns.heatmap(cm_1, annot=True)  \n",
    "\n",
    "print(report_2)\n",
    "pyplot.figure(figsize = (5,5))\n",
    "sns.heatmap(cm_2, annot=True)  \n",
    "\n",
    "print(report_3)\n",
    "pyplot.figure(figsize = (5,5))\n",
    "sns.heatmap(cm_3, annot=True) \n",
    "\n",
    "print(report_4)\n",
    "pyplot.figure(figsize = (5,5))\n",
    "sns.heatmap(cm_4, annot=True)\n",
    "\n",
    "\n",
    "print(report_5)\n",
    "pyplot.figure(figsize = (5,5))\n",
    "sns.heatmap(cm_5, annot=True)\n",
    "\n",
    "print(report_6)\n",
    "pyplot.figure(figsize = (5,5))\n",
    "sns.heatmap(cm_6, annot=True)\n",
    "\n",
    "print(report_7)\n",
    "pyplot.figure(figsize = (5,5))\n",
    "sns.heatmap(cm_7, annot=True)\n",
    "\n",
    "print(report_8)\n",
    "pyplot.figure(figsize = (5,5))\n",
    "sns.heatmap(cm_8, annot=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e4d1d403",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "925b86e5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1440 with 8 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Here we create a figure instance, and two subplots\n",
    "fig = plt.figure(figsize = (20,20)) # width x height\n",
    "ax1 = fig.add_subplot(2, 2, 1) # row, column, position\n",
    "ax2 = fig.add_subplot(2, 2, 2)\n",
    "ax3 = fig.add_subplot(2, 2, 3)\n",
    "ax4 = fig.add_subplot(2, 2, 4)\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "# We use ax parameter to tell seaborn which subplot to use for this plot\n",
    "sns.heatmap(cm_0, ax=ax1,  square=True, cbar_kws={'shrink': .3}, annot=True, annot_kws={'fontsize': 12})\n",
    "sns.heatmap(cm_1, ax=ax2,  square=True, cbar_kws={'shrink': .3}, annot=True, annot_kws={'fontsize': 12})\n",
    "sns.heatmap(cm_2, ax=ax3,  square=True, cbar_kws={'shrink': .3}, annot=True, annot_kws={'fontsize': 12})\n",
    "sns.heatmap(cm_3, ax=ax4,  square=True, cbar_kws={'shrink': .3}, annot=True, annot_kws={'fontsize': 12})\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "a7f4d664",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1440 with 8 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "fig = plt.figure(figsize = (20,20))\n",
    "ax1 = fig.add_subplot(2, 2, 1) \n",
    "ax2 = fig.add_subplot(2, 2, 2)\n",
    "ax3 = fig.add_subplot(2, 2, 3)\n",
    "ax4 = fig.add_subplot(2, 2, 4)\n",
    "\n",
    "sns.heatmap(cm_4, ax=ax1,  square=True, cbar_kws={'shrink': .3}, annot=True, annot_kws={'fontsize': 12})\n",
    "sns.heatmap(cm_5, ax=ax2,  square=True, cbar_kws={'shrink': .3}, annot=True, annot_kws={'fontsize': 12})\n",
    "sns.heatmap(cm_6, ax=ax3,  square=True, cbar_kws={'shrink': .3}, annot=True, annot_kws={'fontsize': 12})\n",
    "sns.heatmap(cm_7, ax=ax4,  square=True, cbar_kws={'shrink': .3}, annot=True, annot_kws={'fontsize': 12})\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "0611c956",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pyplot.figure(figsize = (5,5))\n",
    "sns.heatmap(cm_8, annot=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "d6c653a8",
   "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": [
    "#plot graphs\n",
    "data = {'Combo 1 ':93, 'Combo 2':96, 'Combo 3':64,\n",
    "        'Combo 4':64, 'Combo 5':93, 'Combo 6':90, 'Combo 7':93, 'Combo 8':96,'Combo 9':87}\n",
    "names = list(data.keys())\n",
    "values = list(data.values())\n",
    "  \n",
    "fig = plt.figure(figsize = (10, 5))\n",
    " \n",
    "# creating the bar plot\n",
    "plt.bar(names, values, color ='blue',\n",
    "        width = 0.4)\n",
    " \n",
    "plt.xlabel(\"The different Combinations\")\n",
    "plt.ylabel(\"Accuracy of the models\")\n",
    "plt.title(\"Graph of Accuracy vs several model combinations\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "0fa06fc3",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "fdbbeeaa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Backing off send_request(...) for 22.3s (requests.exceptions.ConnectionError: HTTPSConnectionPool(host='api.segment.io', port=443): Max retries exceeded with url: /v1/batch (Caused by NewConnectionError('<urllib3.connection.HTTPSConnection object at 0x0000010F7FB3C190>: Failed to establish a new connection: [Errno 11001] getaddrinfo failed')))\n",
      "Backing off send_request(...) for 501.4s (requests.exceptions.ConnectionError: HTTPSConnectionPool(host='api.segment.io', port=443): Max retries exceeded with url: /v1/batch (Caused by NewConnectionError('<urllib3.connection.HTTPSConnection object at 0x0000010F7FB3C160>: Failed to establish a new connection: [Errno 11001] getaddrinfo failed')))\n",
      "Giving up send_request(...) after 11 tries (requests.exceptions.ConnectionError: HTTPSConnectionPool(host='api.segment.io', port=443): Max retries exceeded with url: /v1/batch (Caused by NewConnectionError('<urllib3.connection.HTTPSConnection object at 0x0000010F7FB3C190>: Failed to establish a new connection: [Errno 11001] getaddrinfo failed')))\n",
      "error uploading: HTTPSConnectionPool(host='api.segment.io', port=443): Max retries exceeded with url: /v1/batch (Caused by NewConnectionError('<urllib3.connection.HTTPSConnection object at 0x0000010F7FB3C190>: Failed to establish a new connection: [Errno 11001] getaddrinfo failed'))\n"
     ]
    }
   ],
   "source": [
    "# set width of bar\n",
    "barWidth = 0.25\n",
    "fig = plt.subplots(figsize =(12, 8))\n",
    " \n",
    "# set height of bar\n",
    "Accuracy = [93, 96, 64, 64, 93, 90, 93, 96, 87]\n",
    "Precision = [91, 96, 64, 64, 91, 96, 91, 96, 90]\n",
    "Recall = [99, 97, 100, 100, 99, 88, 99, 97, 90]\n",
    " \n",
    "# Set position of bar on X axis\n",
    "br1 = np.arange(len(Recall))\n",
    "br2 = [x + barWidth for x in br1]\n",
    "br3 = [x + barWidth for x in br2]\n",
    "br4 = [x + barWidth for x in br3]\n",
    "br5 = [x + barWidth for x in br4]\n",
    "br6 = [x + barWidth for x in br5]\n",
    "br7 = [x + barWidth for x in br6]\n",
    "br8 = [x + barWidth for x in br7]\n",
    "br9 = [x + barWidth for x in br8]\n",
    " \n",
    "# Make the plot\n",
    "plt.bar(br1, Accuracy, color ='black', width = barWidth,\n",
    "        edgecolor ='grey', label ='Accuracy')\n",
    "plt.bar(br2, Recall, color ='yellow', width = barWidth,\n",
    "        edgecolor ='grey', label ='Recall')\n",
    "plt.bar(br3, Precision, color ='grey', width = barWidth,\n",
    "        edgecolor ='grey', label ='precision')\n",
    "\n",
    " \n",
    "# Adding Xticks\n",
    "plt.xlabel('Combinations', fontweight ='bold', fontsize = 15)\n",
    "plt.ylabel('Performance', fontweight ='bold', fontsize = 15)\n",
    "plt.xticks([r + barWidth for r in range(len(Recall))],\n",
    "        ['Combo1', 'Combo2', 'Combo3', 'Combo4', 'Combo5','Combo6', 'Combo7', 'Combo8', 'Combo9'])\n",
    "plt.grid() \n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4461ff55",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.9.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}