CNN_model_evaluation.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "d606fe64",
   "metadata": {},
   "source": [
    "# Model evaluation"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2263a2ae",
   "metadata": {},
   "source": [
    "## Import packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "2169c03f",
   "metadata": {},
   "outputs": [],
   "source": [
    "## import modules\n",
    "import pickle\n",
    "import matplotlib.pyplot as plt\n",
    "import keras\n",
    "import numpy as np\n",
    "from keras.utils import np_utils\n",
    "from Evaluation import * \n",
    "from Images import * \n",
    "import numpy as np\n",
    "from sklearn.metrics import classification_report, confusion_matrix\n",
    "from sklearn.metrics import confusion_matrix, classification_report, f1_score, recall_score, precision_score, accuracy_score, roc_auc_score\n",
    "from Images import * \n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.metrics import classification_report, confusion_matrix\n",
    "\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.metrics import classification_report, confusion_matrix, roc_auc_score\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5b449c5a",
   "metadata": {},
   "source": [
    "## Plot training and validation accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "99e6cd17",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## load history\n",
    "with open('history_best_model.pkl', 'rb') as f:\n",
    "    history = pickle.load(f)\n",
    "\n",
    "    \n",
    "## plot model accuracy\n",
    "plt.plot(history['accuracy'])\n",
    "plt.plot(history['val_accuracy'])\n",
    "plt.title('Model Accuracy', fontsize = 12)\n",
    "plt.xlabel('Epoch', fontsize = 12)\n",
    "plt.ylabel('Accuracy', fontsize = 12)\n",
    "plt.legend(['train', 'validation'], loc='upper left', fontsize = 12)\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "plt.savefig('RESULTS/history_accuracy_plot.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "094ffa41",
   "metadata": {},
   "source": [
    "## Plot training and validation loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "d39121b9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## plot model loss\n",
    "plt.plot(history['loss'])\n",
    "plt.plot(history['val_loss'])\n",
    "plt.title('Model loss', fontsize = 12)\n",
    "plt.xlabel('Epoch', fontsize = 12)\n",
    "plt.ylabel('Loss', fontsize = 12)\n",
    "plt.legend(['train', 'validation'], loc='upper left', fontsize = 12)\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "plt.savefig('RESULTS/history_loss_plot.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a6aeeaaf",
   "metadata": {},
   "source": [
    "## Loading data and plotting frequencies of training and testing data "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "cafac109",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(array([0, 1, 2]), array([528, 528, 528], dtype=int64))\n",
      "(array([0, 1, 2]), array([31,  9,  7], dtype=int64))\n",
      "(array([0, 1, 2]), array([31,  9,  8], dtype=int64))\n",
      "1584\n",
      "47\n",
      "48\n"
     ]
    }
   ],
   "source": [
    "## load data\n",
    "x_train = np.load(\"created_data/MIAS_X_train_roi_multi.npy\") / 255\n",
    "y_train = np.load(\"created_data/MIAS_y_train_roi_multi.npy\")\n",
    "x_valid = np.load(\"created_data/MIAS_X_valid_roi_multi.npy\") / 255\n",
    "y_valid = np.load(\"created_data/MIAS_y_valid_roi_multi.npy\") \n",
    "x_test = np.load(\"created_data/MIAS_X_test_roi_multi.npy\") / 255\n",
    "y_test = np.load(\"created_data/MIAS_y_test_roi_multi.npy\") \n",
    "\n",
    "## print counts per class\n",
    "print(np.unique(y_train, return_counts = True))\n",
    "print(np.unique(y_valid, return_counts = True))\n",
    "print(np.unique(y_test, return_counts = True))\n",
    "print(len(y_train))\n",
    "print(len(y_valid))\n",
    "print(len(y_test))\n",
    "## plot frequencies \n",
    "#plotfrequencies(y_train, per = True)\n",
    "#plotfrequencies(y_test, per = True)\n",
    "#plotfrequencies(y_train)\n",
    "#plotfrequencies(y_test)\n",
    "\n",
    "# One-hot encode labels\n",
    "num_classes = 3\n",
    "y_train = np_utils.to_categorical(y_train, num_classes)\n",
    "y_valid = np_utils.to_categorical(y_valid, num_classes)\n",
    "y_test = np_utils.to_categorical(y_test, num_classes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "6f41d858",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import cv2\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "img = x_test[36]\n",
    "desired_size = (564, 564)\n",
    "resized_img = cv2.resize(img, desired_size, interpolation=cv2.INTER_LANCZOS4)\n",
    "\n",
    "plt.imshow(resized_img)  # Plot the resized image\n",
    "plt.axis('off')  # Turn off axes\n",
    "plt.savefig(\"resized_image.jpg\", dpi = 570)  # Save the resized image\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "af2d101c",
   "metadata": {},
   "source": [
    "## Loading model "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "9b540c1a",
   "metadata": {},
   "outputs": [],
   "source": [
    "## load model \n",
    "model = keras.models.load_model('best_model.h5')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b9d125f0",
   "metadata": {},
   "source": [
    "## Model summary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "a8f01313",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_8\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " conv2d (Conv2D)             (None, 220, 220, 16)      1216      \n",
      "                                                                 \n",
      " max_pooling2d (MaxPooling2D  (None, 110, 110, 16)     0         \n",
      " )                                                               \n",
      "                                                                 \n",
      " conv2d_1 (Conv2D)           (None, 106, 106, 16)      6416      \n",
      "                                                                 \n",
      " max_pooling2d_1 (MaxPooling  (None, 53, 53, 16)       0         \n",
      " 2D)                                                             \n",
      "                                                                 \n",
      " conv2d_2 (Conv2D)           (None, 53, 53, 14)        2030      \n",
      "                                                                 \n",
      " max_pooling2d_2 (MaxPooling  (None, 26, 26, 14)       0         \n",
      " 2D)                                                             \n",
      "                                                                 \n",
      " max_pooling2d_3 (MaxPooling  (None, 13, 13, 14)       0         \n",
      " 2D)                                                             \n",
      "                                                                 \n",
      " flatten (Flatten)           (None, 2366)              0         \n",
      "                                                                 \n",
      " dense (Dense)               (None, 512)               1211904   \n",
      "                                                                 \n",
      " dense_1 (Dense)             (None, 256)               131328    \n",
      "                                                                 \n",
      " dense_2 (Dense)             (None, 128)               32896     \n",
      "                                                                 \n",
      " dropout_8 (Dropout)         (None, 128)               0         \n",
      "                                                                 \n",
      " dense_3 (Dense)             (None, 3)                 387       \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 1,386,177\n",
      "Trainable params: 1,386,177\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "f01c262c",
   "metadata": {},
   "outputs": [],
   "source": [
    "## save it to file\n",
    "def myprint(s):\n",
    "    with open('RESULTS/modelsummary.txt','a') as f:\n",
    "        print(s, file=f)\n",
    "\n",
    "model.summary(print_fn=myprint)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "331a8cb0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2/2 [==============================] - 2s 169ms/step\n"
     ]
    }
   ],
   "source": [
    "## get predictions on test set \n",
    "preds = model.predict(x_test)\n",
    "y_pred_classes = np.argmax(preds, axis=1)\n",
    "y_true = np.argmax(y_test, axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "19340a16",
   "metadata": {},
   "source": [
    "## Plot confusion matrix for test set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "4a7f4c6c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[29  1  1]\n",
      " [ 0  6  3]\n",
      " [ 1  4  3]]\n"
     ]
    }
   ],
   "source": [
    "## generate confusion matrix \n",
    "cf = confusion_matrix(y_true, y_pred_classes)\n",
    "print(cf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "8db4ae2f",
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_confusion_matrix(y_true, y_pred_classes):\n",
    "    # Compute confusion matrix\n",
    "    # Define class labels\n",
    "    class_names = ['Normal', 'Benign', 'Malignant']\n",
    "    cm = confusion_matrix(y_true, y_pred_classes)\n",
    "\n",
    "        # Create plot\n",
    "        # Resize colorbar to match matrix height\n",
    "    ax = plt.gca()\n",
    "\n",
    "    sns.heatmap(cm, annot=True, fmt='g', cmap='Blues', square=True, xticklabels=class_names, yticklabels=class_names,\n",
    "               annot_kws={\"fontsize\":12}, ax = ax)\n",
    "    plt.xlabel('Predicted classes', fontsize = 12)\n",
    "    plt.ylabel('True classes', fontsize = 12)\n",
    "    plt.title('Confusion matrix', fontsize = 12)\n",
    "    plt.tight_layout()\n",
    "    plt.savefig('RESULTS/confusion_matrix_model1.png')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "8d09a45b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAATsAAAEYCAYAAAAj5FFfAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAAsTAAALEwEAmpwYAAAqAUlEQVR4nO3dd5wV1fnH8c93F5YiRRQkqCgqID/sWCEWxBJN7FGjsWMkxmisMVETBTWWaGIvQNRYMRhJROyxRI0VsaCIHVRAUKSD6O4+vz/mXLis7N65y925c/c+b1/z2jvtnGeuuw9n5syckZnhnHPNXUWxA3DOuSR4snPOlQVPds65suDJzjlXFjzZOefKgic751xZ8GRXJiS1kfSgpHmS7luFco6Q9HghYysWSTtJeq/YcbhkyO+zSxdJPwfOAPoAC4A3gD+Z2fOrWO5RwCnAADOrXtU4006SAb3M7MNix+LSwVt2KSLpDOBq4BKgK7AecCOwfwGKXx94vxwSXRySWhQ7BpcwM/MpBRPQEVgIHNLANq2IkuH0MF0NtArrBgKfA2cCs4AZwHFh3TDgW+C7UMfxwFDgrqyyewAGtAjzxwIfE7UuPwGOyFr+fNZ+A4BXgXnh54Csdc8AFwH/C+U8DnSu59gy8Z+dFf8BwI+B94GvgXOztt8OeBGYG7a9HqgK654Nx7IoHO/Pssr/HfAFcGdmWdhno1BHvzC/NvAlMLDYvxs+FehvrNgB+BT+R8BeQHUm2dSzzYXAS8BaQBfgBeCisG5g2P9CoGVIEouBTmF93eRWb7IDVgPmAxuHdd2ATcLnZckOWAOYAxwV9js8zK8Z1j8DfAT0BtqE+cvqObZM/OeH+E8IyeYeoD2wCbAE2CBsvzWwQ6i3B/AucFpWeQb0XEn5lxP9o9EmO9mFbU4AJgFtgceAK4v9e+FT4SY/jU2PNYGvrOHTzCOAC81slpl9SdRiOypr/Xdh/Xdm9jBRq2bjRsZTC2wqqY2ZzTCzd1ayzU+AD8zsTjOrNrNRwGRg36xtbjOz981sCTAa2LKBOr8juj75HXAv0Bm4xswWhPonAVsAmNlrZvZSqHcKMBzYJcYxXWBmS0M8KzCzkcCHwMtECf68HOW5EuLJLj1mA51zXEtaG5iaNT81LFtWRp1kuRhol28gZraI6NTvRGCGpIck9YkRTyamdbLmv8gjntlmVhM+Z5LRzKz1SzL7S+otaZykLyTNJ7rO2bmBsgG+NLNvcmwzEtgUuM7MlubY1pUQT3bp8SKwlOg6VX2mE3U0ZKwXljXGIqLTtYwfZK80s8fMbA+iFs5koiSQK55MTNMaGVM+biKKq5eZdQDOBZRjnwZvPZDUjug66C3AUElrFCBOlxKe7FLCzOYRXa+6QdIBktpKailpb0l/DpuNAv4gqYukzmH7uxpZ5RvAzpLWk9QROCezQlJXSftLWo0oAS8kOgWs62Ggt6SfS2oh6WdAX2BcI2PKR3ui64oLQ6vzV3XWzwQ2zLPMa4DxZvYL4CHg5lWO0qWGJ7sUMbO/EN1j9weii/OfAScD/w6bXAyMB94CJgITwrLG1PUE8I9Q1musmKAqQhzTiXood+H7yQQzmw3sQ9QDPJuoJ3UfM/uqMTHl6Szg50S9vCOJjiXbUOB2SXMlHZqrMEn7E3USZY7zDKCfpCMKFrErKr+p2DlXFrxl55wrC57snHNlwZOdc64seLJzzpWFkngYus1WJ3svSgNmvnhtsUNwJa5D64pc9yjmJZ+/2SWvX1/QuutTEsnOOVdilL6TRk92zrnCUyKNtbx4snPOFZ637JxzZcFbds65slBRWewIvseTnXOu8Pw01jlXFvw01jlXFrxl55wrC96yc86VBW/ZOefKgvfGOufKgrfsnHNlobDjChSEJzvnXOF5y845Vxa8N9Y5Vxa8g8I5Vxb8NNY5Vxb8NNY5Vxa8ZeecKwvl2LKT1K+h9WY2oaljcM4lrExbdn9pYJ0BgxKIwTmXpHLsjTWzXZu6DudcypRpy24ZSZsCfYHWmWVmdkeSMTjnElCO1+wyJF0ADCRKdg8DewPPA57snGtuUtiySzKig4HdgC/M7DhgC6BjgvU755IixZ8SkmSyW2JmtUC1pA7ALKB7gvU755JSURl/ykFSd0lPS5ok6R1Jp4blQyVNk/RGmH7cUDlJXrMbL2l1YCTwGrAQeDHB+p1zCVFhW2zVwJlmNkFSe+A1SU+EdVeZ2ZVxCkks2ZnZSeHjzZIeBTqY2VtJ1e+cS04hk52ZzQBmhM8LJL0LrJNvOYleRZS0uaT9gH5AT0kHJVm/cy4hij9JGiJpfNY0pN5ipR7AVsDLYdHJkt6SdKukTg2FlGRv7K3A5sA7QG1YbMCYpGJwziUjn5admY0ARsQosx1wP3Camc2XdBNwEVEeuYjoAYbB9e2f5DW7Hcysb4L1rbKqli245txDGbR9Hzp1aMvHn3/F+deN5fH/TQLg2AP7c9axe9K1cwdeeP0jThx2NzO+nFfkqItn9Ki7GTf2X3z4wfvsufdPGHrRpcUOKVXK6fsp8DU7JLUkSnR3m9kYADObmbV+JDCuoTKSPI19UVJJJbsWlRV8/sVc9jj+arru9FuG3TCOuy4fzHrd1mCnrXsx7OT9OOT04ay9y9lMmT6b2y89ttghF1XnLl0YfMKJ7HeAX51YmXL6fioqKmJPuSjKnLcA75rZX7OWd8va7EDg7YbKSbJldwdRwvsCWEp0xm5mtnmCMeRl8Tff8qfhDy+bf+S5t5kybTb9+q7H9ptvwJgnXufdj78A4LKRj/Lx439ig3U788nnXxUr5KIatPueALw76W2+mTkzx9blp6y+n8I27H4IHAVMlPRGWHYucLikLYlOY6cAv2yokCST3S2EgFl+za6krLVGe3qtvxaTPprB9ptvsML9kJmPm/TsVrbJzrmMAvfGPs/K0+fDK1lWryRPY780s7Fm9omZTc1M9W2c3UNT/dU7CYa5ci1aVHDbJcdw14Mv8/6UmTz+wiR+ukc/Nu21Nq1bteScIXtTW1tL29ZVxQ7VuaKTFHtKSpItu9cl3QM8SHQaC0DmYmNd2T00bbY62RKJsB6SuPXiY/j2uxpOv3w0AE+//B4X3/wwo678Be1Xa8319zzDgkVLmTZzbjFDdS4VkkxicSWZ7NoQJbk9s5aVxK0nN19wBGut0Z4DTrmJ6urlZ+DDRz/L8NHPAtBzvbX4/S9+xDsfTi9WmM6lhsr1JdmSKoHZZnZWEvUV0rXnHUafDbry4xOv45ul3y1b3qqqBRt178Kkj2bQ/QeduOGPh3PDPc8wd8GSIkZbXNXV1dTU1FBTU0ttTQ1Lly6lsrKSFi189H8or++nbFt2ZlYj6YdJ1FVI63XrxAkH78g3S79jyn+W3xN1ysWjeOS5d/j7JceyYffOLFi0lDvHvsSwGxu8zafZu3XkzYy8+YZl84889CAnnPhrhvzq5CJGlR7l9P2kMdnJLJnLYeFu53WA+4BFmeX1XbPLVuxrdmk388Vrix2CK3EdWhf2vHOtwaNj/83OuvXQRDJjku3n1sBsVnznRElcs3PO5Sl9DbtERz05Lqm6nHPFlcbT2MTus5O0rqR/SZoVpvslrZtU/c655BTycbGCxZRYTXAbMBZYO0wPhmXOuWYmjTcVJ5nsupjZbWZWHaa/A10SrN85l5Q8xrNLSpLJbrakIyVVhulIog4L51wzU+4tu8HAocAXREMsHwx4p4VzzVAak12SvbFTgf2Sqs85Vzxl+biYpPMbWG1mdlFTx+CcS1Yabz1JomW3aCXLVgOOB9YkGjveOdeMlGWyM7O/ZD6Hdz6eSnSt7l6iF2Q455qZskx2AJLWAM4AjgBuB/qZ2Zwk6nbOJa8sk52kK4CDiAbi3MzMFjZ1nc65IktfrkukZXcm0aCdfwDOy8r4mRfudEggBudcgpJ8DCyuJK7Zpe+onXNNKoVnsYkO8eScKxNlec3OOVd+UpjrPNk55wrPW3bOubJQWenJzjlXBlLYsPNk55wrPD+Ndc6VhRTmukTHs3POlYlCjmcnqbukpyVNkvSOpFPD8jUkPSHpg/CzU0PleLJzzhVcRYViTzFUA2eaWV9gB+DXkvoCvweeNLNewJNhvv6YVvGYnHPuewrZsjOzGWY2IXxeALwLrAPsTzSwCOHnAQ2V49fsnHMF11TX7CT1ALYCXga6mtmMsOoLoGtD+3rLzjlXcPm07CQNkTQ+axpST5ntgPuB08xsfvY6MzPAGorJW3bOuYLLp2VnZiOIhoBroDy1JEp0d5vZmLB4pqRuZjZDUjdgVkNleMvOOVdwBe6NFXAL8K6Z/TVr1VjgmPD5GOCBhsrxlp1zruBi9rLG9UPgKGCipDfCsnOBy4DRko4HphK9qrVenuyccwVXyA4KM3ue+sc+3i1uOSWR7Oa8en2xQ0i1ydMXFDuEVOvcvqrYIaReh9atClpeGh8Xi3XNTtKukjYIn7tJul3SbZJ+0LThOedKkRR/SkrcDoobgZrw+S9AS6CWHD0ozrnyVMgOikKJexq7jpl9KqkF8CNgfeBbYHqTReacK1kF7qAoiLjJbr6krsCmwCQzWyipiqiF55xzK0jjNbu4ye464FWgCjgtLPshMLkJYnLOlbgU5rp4yc7MLpf0L6DGzD4Ki6cBv2iyyJxzJSuNLbt8nqD4BFhb0s/C/DTg48KH5JwrdSXbGytpM+B9YCTRYxsAuwC3NlFczrkSlsbe2Lgtu5uA882sD/BdWPZfYMcmico5V9IqKxR7SkrcDopNgLvCZwMws0WS2jRJVM65kpbCS3axW3ZTgK2zF0jaDviw0AE550pfGk9j47bs/gg8JOlmoErSOcCJwAlNFplzrmSl8J7ieC07MxsH7AV0IbpWtz5wkJk93oSxOedKVCm37DCz14GTmjAW51wzUZHCi3Zxbz05Q9KW4fMOkj6V9Imk/k0anXOuJFUo/pSUuC2701l+f92lwF+BBcDVwPaFD8s5V8rS+ARF3GTX0czmSWoPbAHsbmY1kv7ShLE550pUCnNd7GT3maQBRPfbPRsSXQeWj3HnnHPLpPGaXdxk91vgn0Rj2P00LNsHeKUpgnLOlbYU5rrYo548DKxdZ/F9YXLOuRWU7OCdkvoCs81sZngr92+JhmW/guXPysYppxLoml2vmX2aV8TOudQr5dPYUUTvZJwJXAlsDHwDDCd6n2NOkk4BLghl1IbFBmyeR7zOuRKQvlQXP9n1MLP3wpu5DwL6AkuIxriL61RgYzObnWeMzrkSU8q3nnwTbjvpC3xqZl+Fl++0zqOuz4B5+QbonCs9KbxkFzvZ3QM8BbQHMm+s7kd+LbuPgWckPQQszSw0s7/mUYZzrgSUbAeFmZ0uaU/gOzN7OiyuJXqyIq5Pw1QVJudcM1XKp7HUHeHEzMbnU5GZDctne+dc6Uphwy72rSctiEY82QXoTFZni5ntHLOMBwmjHGeZB4wHhpvZN3HKcc6lXxpbdnFHKr4K+CXwLNGIxfcDaxFdx4vrY2Ah0Ut7RgLziQYT6B3mnXPNhPKYcpYl3SpplqS3s5YNlTRN0hth+nGucuKexh4E9DezTyUNM7NrJD1GdJ/d0JhlDDCzbbPmH5T0qpltK+mdmGU450pAgW8q/jtRx+gddZZfZWZXxo4p5nZtiW4dAVgiqa2ZTQa2ilsR0E7SepmZ8LldmP02j3KKZt7cuZz2m1+z/TZbstfuu/LwuAeLHVLq/O/pxzh98MEcve+O/Obo/Xl34uvFDilVLrngHA75ySD2HdSfow/Zl4ceuL/YITWJigrFnnIxs2eBr1c1prgtu3eBbYke/B8PDJU0n+hF2XGdCTwv6SOi1usGwEmSVgNuz6Ocornk4gtp2bIlT//3f0ye/C6nnPRLevfpQ8+evYodWiq89dpL3PO36zj1vEvp2WcT5n79VbFDSp2fH3M8Z503jKqqKj6d8glnnDSYXhv/H7379C12aAWVT8NO0hBgSNaiEWY2IsauJ0s6mignnWlmcxraOG7L7lSgOnw+g+geu33rBNigMJhAL+A0lj9N8ZCZLTKzq+OWUyyLFy/mP088zq9POZW2q61Gv623YZddBzFu7APFDi017rtjBD898hf07rsZFRUVrNF5LdbovFaxw0qVHhv2pKoquvNKii7kT//8sxx7lZ4KKfZkZiPMbJusKU6iuwnYCNgSmAHkHFsz7n12r2Z9/gDYPc5+AJIGmdlTkg6qs2ojRQc6Jm5ZxTR16hRatKikR48Nli3beOM+jH/11Qb2Kh+1NTV89P4ktu6/M7855gC++/Zbth2wC0cOOZWqVvk8aNP8XfPni3nsobEsXfoNPXv3YfsBOxU7pIJr6s5YM5u5vC6NBMbl2qfeZCdpUMxKc/XI7kLUa7vvynYHSiLZLVm8mNVWa7fCsnbt2rN48aIiRZQuc+d8TU11NS8/+yTD/vo3Klu04Irzz2DM3bdw2OBfFzu8VDn17D9w8pnnMGnim7w5YTwtq1oWO6SCa+pbTyR1M7MZYfZA4O2GtoeGW3a3NLAuw4ANG9zA7ILw87gY5S2TfR5//Y3DOf6E2GfMTaJN27YsWrRwhWULFy2kbdvVihRRulS1agXAXgf8jE5rdgZgn4OP8GRXj8rKSjbbsh//eewhxt4/moN+dkSxQyqoygImO0mjgIFAZ0mfE42eNDC8BMyAKUS3xjWo3mRnZhvUt64xJLUiGuW4ByuOZ3dhPfWPAEYAfFP9vZuRE7f++j2orq5h6tQprL9+DwDef28yG/XsWdzAUqJd+w6s2aVrnRun0ndjadrUVFczfVpzvGZXuLLM7PCVLI7TGFtB3Fcpbimpe51l3SVtkUddDwD7E3V0LMqaSkLbtm3ZbY89uPG6a1m8eDGvT3iNZ556kn3227/YoaXGwD335dF/j2benK9ZuGA+D4+5h347NL/rUY015+vZPPXEIyxZvJiamhpefel/PP3EI/Tbpvm9oK+UX6V4F7BfnWVVwJ3EH3xzXTPbK25gaXTeHy7ggj+ey647D2D1jqtz3h+H+m0nWQ468hfMnz+X0447iJZVrei/y+4c+PPBxQ4rNSTx4JjRXH35xVhtLV27deOk085mwM67Fju0gkvj42Iyy32GKGm+mXWIu7yeMkYA15nZxHyDTMNpbJpNnr6g2CGkWuf2PshOLut2alXQ7PTbce/F/pu9Yp+NE8mMcVt2n0vqZ2YTMgsk9QOm51HXjsCxkj4hGs9OgJmZD8vuXDOTwoZd7GR3FfCApD8DHxHdzHcW8Kc86to7z9iccyWqRQqzXdybikdKmgscD3Qnek72TDP7Z9yKzGyqpB2BXmZ2m6QuLH821jnXjKQw1+U1eOcqvSdW0gXANkRvJrsNaEnU8fHDxpbpnEunUn6VYiEcSDRKygQAM5seXuLjnGtmUpjrEk1235qZSTKAMNqJc64ZKtlh2QtktKThwOqSTgAG4yMUO9csVaYw2yWW7MzsSkl7EA3HvjFwvpk9kVT9zrnkpDDXxX7hTivgfOBwYE0z6xherdjbzK5veO/lQnJ7QlJnYHZjAnbOpZ9S+Fx0Pi/c2RQ4guVvCHsH+FWuHSXtIOkZSWMkbRVemvE2MFNSST8+5pxbuVJ+NvZAoKeZLZJUC2Bm0yStE2Pf64FzgY5E49rtbWYvSeoDjAIebUTczrkUK9nTWKIX4qywbbgpOM6paIvMC7YlXWhmLwGY2eQ0PizsnFt1afzbjnsaex9wu6QNIBollKjFdm+MfWuzPi+ps84f8HeuGaqsiD8lJW7L7lzgcmAi0WsVPyC6bWRYjH23CG8iE9AmfCbM+8sJnGuGSvYJCjP7FjgdOD2cvn5lccaGivatXIX4nHMlqGSv2Umq+56J9plzcjP7uNBBOedKWwobdrFPYz8kur6WfQiZlp233JxzK6hI4X12cU9jV7iMKOkHRG/4ea4pgnLOlbYkOx7iatTjYmb2haTTgPeBewoakXOu5JVsB0U9NibqmXXOuRWkMNfF7qB4jhXviWsLbAKs9J2vzrnyVsotu7/VmV8EvGlmHxQ4HudcM5DCXJc72UmqBAYBQ8xsadOH5JwrdZUpzHY5k52Z1YThnGpzbeucc0AKbzzJb4inYZJaNmUwzrnmoUKKPSUWU0MrJR0ePp4C/BZYIOkzSZ9mpiaP0DlXcpTHlLMs6VZJs8JYmJlla0h6QtIH4WenXOXkOo0dTjTm3JExYnLOOaDgHRR/Jxpl6Y6sZb8HnjSzyyT9Psz/rqFCciU7AZjZfxsfp3Ou3BRyPDsze1ZSjzqL9wcGhs+3A8+wismuUtKuNNDaNLOncpThnCsz+fTGShoCDMlaNMLMRuTYrauZzQifvwC65qonV7JrBdxC/cnOgLojojjnylw+7bqQ2HIlt4b2X/Y+6obkSnaLzMyTmXMuLwkMyz5TUjczmxFGTp+Va4ckX5LdaN9W+y1+DWnfuiT+N7oyksCgJ2OBY4DLws8Hcu0Qq4PCOefyUciWnaRRRJ0RnSV9TjS83GXAaEnHA1OBQ3OV02CyM7P2qx6qc67cFHJYdjM7vJ5Vu+VTjp//OOcKrmRHKnbOuXykcBwAT3bOucKTt+ycc+XAW3bOubLg1+ycc2Whorm8Xcw55xri1+ycc2WhkPfZFYonO+dcwXnLzjlXFtLYG5vYZURJh8RZ5pwrfZVS7CkpSfaZnBNzmXOuxCmP/5LS5KexkvYGfgysI+narFUdgOqmrt85l7w0nsYmcc1uOjAe2A94LWv5AuD0BOp3ziUshbmu6ZOdmb0JvCnpHjP7rqnrc84VX5Lvg40ryd7Y7SQNBdYP9Ypo+Hgf9t25ZiZ9qS7ZZHcL0Wnra0BNgvU65xKWwDso8pZksptnZo8kWJ9zrkhSmOsSTXZPS7oCGAMszSw0swkJxuCcS0AKc12iyW778HObrGUGDEowBudcElKY7RJLdma2a1J1OeeKq+yfjZX0E2AToHVmmZldmGQMzrmmV9ajnki6GWgL7Ar8DTgYeCWp+p1zCUphskvy2dgBZnY0MMfMhgH9gd4J1u+cS0hZPhubZUn4uVjS2sBsoFuC9TvnElLut56Mk7Q6cAUwgagn9m8J1u+cS0gKc12ivbEXhY/3SxoHtDazeUnV75xLUAqzXdK9sQOAHpl6JWFmdyQZg3Ou6aVxIIAkRyq+E7gS2BHYNkzbNLhTyowedTdHH34wA7bZnKF/9HFH6zPts6nsv9t2XHHhucUOJXUuueAcDvnJIPYd1J+jD9mXhx64v9ghNQnlMcUqT5oiaaKkNySNb0xMSbbstgH6mpklWGdBde7ShcEnnMhLLzzPN0uX5t6hTN141aX07rNJscNIpZ8fczxnnTeMqqoqPp3yCWecNJheG/8fvfv0LXZohdU0Dbtdzeyrxu6c5K0nbwM/SLC+ghu0+54MHLQ7HVdfvdihpNZ///Moq7VrzxZbb1fsUFKpx4Y9qaqqAqIeS0lM//yzIkdVeOV+60lnYJKkV1hxIID9EozBNaHFixZy5y03ctk1I3l03Jhih5Na1/z5Yh57aCxLl35Dz9592H7ATsUOqeDyuWQnaQgwJGvRCDMbUWczAx6XZMDwlazPKclkNzTBulwR3PG3G/jRPgfSea2uxQ4l1U49+w+cfOY5TJr4Jm9OGE/LqpbFDqng8kl2IXHlSl47mtk0SWsBT0iabGbP5hNTYqexZvbflU31bS9piKTxksbfdkveSdwl7KMPJvPG+Jc54NAjix1KSaisrGSzLfvx5ZczGXv/6GKHU3CFPo01s2nh5yzgX0De10mSfDZ2AVFTNNs8opfxnGlmH2evyM7287+pLdlOjXIx8fXxzPxiOscevBcAS5Yspramlk8HH8Z1t95b5OjSq6a6munTmuE1uwJeipO0GlBhZgvC5z2BvAcQSfI09mrgc+Aeor6aw4CNiJ6muBUYmGAsjVJdXU1NTQ01NbXU1tSwdOlSKisradEi0dsVU2mv/X7KzrvttWx+zL13MHPGdE4+028/yZjz9Wxef+0V+v9wF6patWLCqy/x9BOPcN6Flxc7tIIrcLdDV+BfYaj3FsA9ZvZovoUk+Ve6n5ltkTU/QtIbZvY7SSXxF3HryJsZefMNy+YfeehBTjjx1wz51clFjCodWrduQ+vWbZbPt2lDVVUVHTutUcSo0kUSD44ZzdWXX4zV1tK1WzdOOu1sBuzcDId6LGC2C2d9W+TcMAclddubpBeBq4B/hkUHA2eY2Q4h6W1Z375+GtuwL+f7PX8NadUyyTusStO6nVoVtDH2wcwlsf9me3Vtk8j9J0n+FhwBHAXMAmaGz0dKagN408i5ZqRC8aekJDkQwMfAvvWsfj6pOJxzCUjfo7FNn+wknW1mf5Z0Hd/vjcXMftPUMTjnklWu76B4N/xs1MO7zrnSk8JBT5o+2ZnZg+Hn7U1dl3MuHVKY6xI5jX2QlZy+Zvizsc41P0ph0y6J09grE6jDOZciKcx1iZzG1vv8q3OueUphrkv02dhewKVAX1Z8SfaGScXgnEtGGlt2Sd5UfBtwE1BN9KLsO4C7EqzfOZeYQg/MvuqSTHZtzOxJokfUpprZUOAnCdbvnEtINApzvCkpSQ4EsFRSBfCBpJOBaUC7BOt3ziUkycfA4kqyZXcq0Bb4DbA10bOxxyRYv3MuIWX9DgozezV8XAgcl1S9zrkiSGHLLombisc2tN5vKnau+UlhrkukZdcf+AwYBbxMOr8H51wBpfHWkySS3Q+APYDDgZ8DDwGjzOydBOp2zhVBGh8Xa/IOCjOrMbNHzewYYAfgQ+CZ0CPrnGuG0neXXUIdFJJaEd1TdzjQA7iW6HVozrlmKIUNu0Q6KO4ANgUeBoaZ2dtNXadzrrjSOHhnk79wR1ItsCjMZlcmwMysQ64y/IU7DfMX7jTMX7iTW6FfuDNncU3sv9lObSsTyYxJjHriv2nOuaLztzs75wquIoUX7TzZOecKLoW5zpOdc67wUpjrPNk555pACrOdJzvnXMGl8dYTT3bOuYIr9/HsnHPlosDPi0naS9J7kj6U9PvGhOTJzjlXcIUcvFNSJXADsDfRC7sOl9Q335g82TnnCq7A76DYDvjQzD42s2+Be4H9842pJK7ZdWidrisAkoaY2Yhix5HRoXWbYoewgrR9P2nU3L+j1i3i91BIGgIMyVo0os53sw7RmJgZnwPb5xuTt+waZ0juTcqafz+5+XcUmNkIM9sma2qSfwQ82Tnn0m4a0D1rft2wLC+e7Jxzafcq0EvSBpKqgMOABt9tszIlcc0uhZrttZYC8e8nN/+OYjKz6jCy+WNAJXBrY17r0OTj2TnnXBr4aaxzrix4snPOlYWyS3aSTNJfsubPkjQ04RiekbRNknXmQ1KNpDckvSlpgqQBq1DWhZJ2L2R8SQi/J3dlzbeQ9KWkcTn2G5jZRtJ+jX20qTEkbSnpx0nVV2rKLtkBS4GDJHVuzM6SyqFTZ4mZbWlmWwDnAJc2tiAzO9/M/lO40BKzCNhUUuaO7T3I83YHMxtrZpcVPLL6bQl4sqtHOSa7aqKesNPrrpDUQ9JTkt6S9KSk9cLyv0u6WdLLwJ/D/E2SXpL0cfjX/FZJ70r6e1Z5N0kaL+kdScOSOsAC6wDMycxI+q2kV8N3NCws6xGOfWQ41sczSSJ8VweHzz+WNFnSa5KuzWoBDQ3f3zPh+/xNEY5zZR4megUoRK8BHZVZIWk7SS9Kel3SC5I2rruzpGMlXR8+bxR+XyZKuljSwrB8YDjuf4bv5m6FN0xLOj98129LGpG1/BlJl0t6RdL7knYKt2RcCPwstMp/1qTfTAkqx2QH0UPFR0jqWGf5dcDtZrY5cDfR+20z1gUGmNkZYb4T0J8oaY4FrgI2ATaTtGXY5jwz2wbYHNhF0uZNcTBNoE34g5kM/A24CEDSnkAvomcVtwS2lrRz2KcXcIOZbQLMBX6aXaCk1sBwYG8z2xroUqfOPsCPQtkXSGrZBMeVr3uBw0LsmwMvZ62bDOxkZlsB5wOX5CjrGuAaM9uM6HGnbFsBpxE95L4h8MOw/Hoz29bMNgXaAPtk7dPCzLYL+10Qnhk9H/hHaJX/I68jLQNlmezMbD5wB1C3BdEfuCd8vhPYMWvdfWZWkzX/oEX37UwEZprZRDOrBd4hehE4wKGSJgCvEyXCvEdqKJLMaWwfYC/gjtCq2DNMrwMTiBJUr7DPJ2b2Rvj8Gsu/g4w+wMdm9kmYH1Vn/UNmttTMvgJmAV0LeDyNYmZvER3H4UStvGwdgfskvc3yf+ga0h+4L3y+p866V8zs8/D78wbLv7tdJb0saSIwqE4dY8LPlX3XbiXK4fpTfa4m+oO9Leb2i+rMZ17WWpv1OTPfQtIGwFnAtmY2J5zetm50tEViZi+G65tdiEYfu9TMhmdvI6kHK34HNUQtkXzU3T8tv5tjgSuBgcCaWcsvAp42swPD8T+zCnV879hDa/JGYBsz+yx0orVeyT5p+q5SrSxbdgBm9jUwGjg+a/ELRI+iABwBPLcKVXQgSpDzJHUlGour5EjqQ3TX+myiO9gHS2oX1q0jaa2YRb0HbBgSA0CpXFO6FRhmZhPrLO/I8g6LY2OU8xLLT+0Pa2jDIJPYvgrf98Ex9lkAtI+xXVkq22QX/AXI7pU9BThO0lvAUcCpjS3YzN4kOt2bTHTa8r9ViDNpmWt2bwD/AI4xsxoze5zoWF4Mp1b/JOYfl5ktAU4CHpX0GtEf5rwmib6AwunltStZ9WfgUkmvE69ldRpwRvjd6kmOYzezucBI4G2if2RejVHH00Bf76BYOX9czCVGUjszWxiu/90AfGBmVxU7riRIakt0LdQkHQYcbmZ5D0DpGs/P9V2STpB0DFBF1OodnmP75mRr4PqQ6OcCg4sbTvnxlp1zriyU+zU751yZ8GTnnCsLnuycc2XBk10ZCM+nXhw+7yTpvYTqNUk989xnoKS6j1M5t8o82aWEpCmSlkhaKGlmSFDtCl2PmT1nZt97aH0l8Rwr6flC1+9csXiyS5d9zawd0A/YBvhD3Q1UHkNMOVdwnuxSyMymAY8Am8Ky08FfS/oA+CAs2yfcKT83DDG0bEQVSVspGnRzgaR/kPVMZd3TREndJY1RNDDlbEnXS/o/4Gagf2hpzg3btpJ0paRPQ+vzZi0f7y0z/NMMSdMlNXgfmaQ1JN0Wtp0j6d/1bPd7SR+FY5kk6cCsdT0l/VfSPElfhWNFkaskzZI0X9GwSpnvst5jkNRZ0rjwnX4t6TlJ/jfSTPj/yBSS1J1oEMbXsxYfQPQW9L6StiJ6ZvOXRA+nDwfGhj/kKuDfRKO2rEE00sYKwy1l1VMJjAOmEo2csQ5wr5m9C5wIvGhm7cxs9bDLZUBvouGdeobtzw9l7UU08MEeRCOh5Bqd+E6gLdFIHmsRjRyyMh8BOxE9izoMuEtSt7DuIuBxouG21iUaoguikVl2DrF2BA4lera3wWMAziQafqkL0agr5wJ+I2pzYWY+pWACpgALie6un0o04kWbsM6AQVnb3gRcVGf/94BdiP7IpxNuGA/rXgAuDp8HAp+Hz/2BL4nGRqsbz7HA81nzIhrYYKOsZf2JhnaCKPlelrWud4i750rK7kY0OkynlaxbFl8939MbwP7h8x1EA7GuW2ebQcD7wA5ARR7HcCHwwMpi9qn0J2/ZpcsBZra6ma1vZidZ9PB8xmdZn9cHzgynW3PDaWZ3YO0wTbPw1xtMrae+7sBUM6uOEVsXopbYa1l1PsryQTjXrhNjfXVm6v3azOY0sA0Ako7OOl2fS3Rqnxm84WyiBPaKohGSBwOY2VPA9UTP385SNMpvhxjHcAXwIfC4ohGTE3t/hGt6nuxKR3by+gz4U0iMmamtmY0CZgDrhGcwM9arp8zPgPXq6fSoe/r2FbAE2CSrzo4WdagQ6u0eo85MvWtIWr2BbZC0PtHIHycDa1p0Ov02UYLDzL4wsxPMbG2iU/obM7e6mNm1Fo2I3JeolfnbXMdgZgvM7Ewz2xDYj2iUkt0aitGVDk92pWkkcKKk7cPF+NUk/URSe+BFovds/EZSS0kHEQ11vjKvECWpy0IZrSVlhgSfCawbrgFi0Si6I4GrFMawUzSe3Y/C9qOBYyX1VTTCxwX1BW9mM4g6YG6U1CnEufNKNl2NKOl+Geo7jtBpE+YPkbRumJ0Ttq2VtG34bloSnbZ+A9TmOgZFnT49wz8U84gGxqyt7zhcafFkV4LMbDxwAtGp2hyiU69jw7pvgYPC/NdEg2SOqaecGmBfogv1nxJdnM+Mg/YU0RDzX0j6Kiz7XajrJUnzgf8AG4eyHiEa/fmpsM1TOQ7jKOA7ovH+ZhGN91Y3vklEYw6+SJR8N2PFcQG3BV5W9PKascCpZvYx0cCpI8N3M5Woc+KKXMdA1LHyH6Jrpy8CN5rZ0zmOw5UIH/XEOVcWvGXnnCsLnuycc2XBk51zrix4snPOlQVPds65suDJzjlXFjzZOefKgic751xZ+H+Z//4Tt7i13wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "## visualize confusion matrix \n",
    "\n",
    "plot_confusion_matrix(y_true, y_pred_classes)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2031cd72",
   "metadata": {},
   "source": [
    "## Get evaluation report of model "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "8ccdf139",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2/2 [==============================] - 1s 167ms/step\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "     Class 0       0.97      0.94      0.95        31\n",
      "     Class 1       0.55      0.67      0.60         9\n",
      "     Class 2       0.43      0.38      0.40         8\n",
      "\n",
      "    accuracy                           0.79        48\n",
      "   macro avg       0.65      0.66      0.65        48\n",
      "weighted avg       0.80      0.79      0.79        48\n",
      "\n"
     ]
    }
   ],
   "source": [
    "## evaluate model on test set \n",
    "report = evaluate_model(model, x_test, y_test)\n",
    "print(report)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "940dd258",
   "metadata": {},
   "source": [
    "## Overall statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "0c07a155",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[29  1  1]\n",
      " [ 0  6  3]\n",
      " [ 1  4  3]]\n",
      "Overall Accuracy: 0.792\n",
      "Balanced Accuracy: 0.659\n",
      "Class 0:\n",
      "\tPrecision: 0.967\n",
      "\tRecall: 0.935\n",
      "\tF1-score: 0.951\n",
      "Class 1:\n",
      "\tPrecision: 0.545\n",
      "\tRecall: 0.667\n",
      "\tF1-score: 0.600\n",
      "Class 2:\n",
      "\tPrecision: 0.429\n",
      "\tRecall: 0.375\n",
      "\tF1-score: 0.400\n",
      "Macro Precision, 0.647 \n",
      "Macro Recall, 0.659 \n",
      "Macro F1-score, 0.65 \n"
     ]
    }
   ],
   "source": [
    "conf_mat = cf\n",
    "print(conf_mat)\n",
    "\n",
    "# Calculate total number of predictions\n",
    "total_predictions = np.sum(conf_mat)\n",
    "\n",
    "# Calculate number of correct predictions\n",
    "correct_predictions = np.sum(np.diag(conf_mat))\n",
    "\n",
    "# Calculate overall accuracy\n",
    "accuracy = correct_predictions / total_predictions\n",
    "print(\"Overall Accuracy: {:.3f}\".format(accuracy))\n",
    "\n",
    "# Calculate balanced accuracy\n",
    "class_counts = np.sum(conf_mat, axis=1) ## true instances per class (row marginal - sum over cols)\n",
    "class_correct_predictions = np.diag(conf_mat) ## true positives (sum over diagoal)\n",
    "class_accuracy = class_correct_predictions / class_counts.flatten() ## calculate accuracy per class = tp/tp + fn\n",
    "balanced_accuracy = np.mean(class_accuracy) ## averaging over all class_accuracies = balanced accuracy\n",
    "print(\"Balanced Accuracy: {:.3f}\".format(balanced_accuracy))\n",
    "\n",
    "# Compute metrics for each class\n",
    "metrics_per_class = {} ## dict to store metrices\n",
    "for i in range(len(conf_mat)):\n",
    "    tp = conf_mat[i, i] # true positive\n",
    "    fp = np.sum(conf_mat[:, i]) - tp # false positive (predicted class i but not class i)\n",
    "    fn = np.sum(conf_mat[i, :]) - tp # false negatives (not predicted class i but class i)\n",
    "    \n",
    "    precision = tp / (tp + fp) ## precision of class i = tp / (tp + fp) -> correctly classified out of all predicted\n",
    "    recall = tp / (tp + fn) ## recall of class i = tp / (tp + fn)  -> correctly classified out of all instances of class i\n",
    "    f1_score = 2 * (precision * recall) / (precision + recall) ## f1-score = harmonic mean of precision and recall \n",
    "    ## store metrices per class\n",
    "    metrics_per_class[f'Class {i}'] = {\n",
    "        'Precision': precision,\n",
    "        'Recall': recall,\n",
    "        'F1-score': f1_score\n",
    "    }\n",
    "\n",
    "## define dict to store macro averages\n",
    "macro_metrics = {}\n",
    "\n",
    "# loop over classes\n",
    "for class_name, metrics in metrics_per_class.items():\n",
    "    print(f\"{class_name}:\")\n",
    "    for metric_name, metric_value in metrics.items(): ## loop over metrices \n",
    "        print(f\"\\t{metric_name}: {metric_value:.3f}\") ## print metrics for each class\n",
    "        macro_metrics[metric_name] = macro_metrics.get(metric_name, 0) + metric_value ## add class-wise metrics to total count\n",
    "\n",
    "## loop over metrices and print macro average\n",
    "for k, v in macro_metrics.items():\n",
    "    macro_metrics[k] = v / len(conf_mat[0])\n",
    "    print( f\"Macro {k}, {round(macro_metrics[k],3)} \")\n",
    "    \n",
    "macro_metrics[\"Overall Accuracy\"] = accuracy\n",
    "macro_metrics[\"Balanced Accuracy\"] = balanced_accuracy\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "1eba3a59",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_per_class_avg = round(pd.DataFrame(metrics_per_class),2)\n",
    "df_per_class_avg\n",
    "\n",
    "with open('RESULTS/class_results_firstmod.tex','w') as tf:\n",
    "    tf.write(df_per_class_avg.to_latex())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "7a63d5db",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Class 0</th>\n",
       "      <th>Class 1</th>\n",
       "      <th>Class 2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Precision</th>\n",
       "      <td>0.97</td>\n",
       "      <td>0.55</td>\n",
       "      <td>0.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Recall</th>\n",
       "      <td>0.94</td>\n",
       "      <td>0.67</td>\n",
       "      <td>0.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F1-score</th>\n",
       "      <td>0.95</td>\n",
       "      <td>0.60</td>\n",
       "      <td>0.40</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Class 0  Class 1  Class 2\n",
       "Precision     0.97     0.55     0.43\n",
       "Recall        0.94     0.67     0.38\n",
       "F1-score      0.95     0.60     0.40"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_per_class_avg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "bbfa2234",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create an empty DataFrame with two columns and three rows\n",
    "df_macro_avg = round(pd.DataFrame.from_dict(macro_metrics, orient='index', columns=['CNN Model (averaged over 10 runs)'])\n",
    ",2).transpose()\n",
    "df_macro_avg\n",
    "## combining dataframes\n",
    "df3 = pd.concat([df_baseline, df_macro_avg])\n",
    "df3.index = ['Baseline Model', 'CNN model']\n",
    "df3\n",
    "\n",
    "with open('RESULTS/results_firstmod.tex','w') as tf:\n",
    "    tf.write(df3.to_latex())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "3de3d59c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Precision</th>\n",
       "      <th>Recall</th>\n",
       "      <th>F1-score</th>\n",
       "      <th>Overall Accuracy</th>\n",
       "      <th>Balanced Accuracy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Baseline Model</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.33</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.65</td>\n",
       "      <td>0.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CNN model</th>\n",
       "      <td>0.65</td>\n",
       "      <td>0.66</td>\n",
       "      <td>0.65</td>\n",
       "      <td>0.79</td>\n",
       "      <td>0.66</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Precision  Recall  F1-score  Overall Accuracy  \\\n",
       "Baseline Model        NaN    0.33       NaN              0.65   \n",
       "CNN model            0.65    0.66      0.65              0.79   \n",
       "\n",
       "                Balanced Accuracy  \n",
       "Baseline Model               0.33  \n",
       "CNN model                    0.66  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "22ee0f83",
   "metadata": {},
   "source": [
    "## Baseline classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "c0255609",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(array([0., 1.], dtype=float32), array([96, 48], dtype=int64))\n",
      "Overall Accuracy: 0.646\n",
      "Balanced Accuracy: 0.333\n",
      "Class 0:\n",
      "\t Baseline Precision: 0.646\n",
      "\t Baseline Recall: 1.000\n",
      "\t Baseline F1-score: 0.785\n",
      "Class 1:\n",
      "\t Baseline Precision: nan\n",
      "\t Baseline Recall: 0.000\n",
      "\t Baseline F1-score: nan\n",
      "Class 2:\n",
      "\t Baseline Precision: nan\n",
      "\t Baseline Recall: 0.000\n",
      "\t Baseline F1-score: nan\n",
      "Baseline Macro Precision, nan \n",
      "Baseline Macro Recall, 0.333 \n",
      "Baseline Macro F1-score, nan \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\user\\AppData\\Local\\Temp/ipykernel_12588/1219750040.py:35: RuntimeWarning: invalid value encountered in long_scalars\n",
      "  precision = tp / (tp + fp) ## precision of class i = tp / (tp + fp) -> correctly classified out of all predicted\n"
     ]
    }
   ],
   "source": [
    "## baseline confusion matrix\n",
    "print(np.unique(y_test, return_counts = True))\n",
    "\n",
    "## baseline classifier predicts only majority class which is \"normal\" = 0\n",
    "baseline_cm = np.array([(31, 0, 0),\n",
    "                         (9, 0, 0),\n",
    "                         (8, 0, 0)])\n",
    "conf_mat = baseline_cm\n",
    "\n",
    "\n",
    "# Calculate total number of predictions\n",
    "total_predictions = np.sum(conf_mat)\n",
    "\n",
    "# Calculate number of correct predictions\n",
    "correct_predictions = np.sum(np.diag(conf_mat))\n",
    "\n",
    "# Calculate overall accuracy\n",
    "accuracy = correct_predictions / total_predictions\n",
    "print(\"Overall Accuracy: {:.3f}\".format(accuracy))\n",
    "\n",
    "# Calculate balanced accuracy\n",
    "class_counts = np.sum(conf_mat, axis=1) ## true instances per class (row marginal - sum over cols)\n",
    "class_correct_predictions = np.diag(conf_mat) ## true positives (sum over diagoal)\n",
    "class_accuracy = class_correct_predictions / class_counts.flatten() ## calculate accuracy per class = tp/tp + fn\n",
    "balanced_accuracy = np.mean(class_accuracy) ## averaging over all class_accuracies = balanced accuracy\n",
    "print(\"Balanced Accuracy: {:.3f}\".format(balanced_accuracy))\n",
    "\n",
    "# Compute metrics for each class\n",
    "metrics_per_class = {} ## dict to store metrices\n",
    "for i in range(len(conf_mat)):\n",
    "    tp = conf_mat[i, i] # true positive\n",
    "    fp = np.sum(conf_mat[:, i]) - tp # false positive (predicted class i but not class i)\n",
    "    fn = np.sum(conf_mat[i, :]) - tp # false negatives (not predicted class i but class i)\n",
    "    \n",
    "    precision = tp / (tp + fp) ## precision of class i = tp / (tp + fp) -> correctly classified out of all predicted\n",
    "    recall = tp / (tp + fn) ## recall of class i = tp / (tp + fn)  -> correctly classified out of all instances of class i\n",
    "    f1_score = 2 * (precision * recall) / (precision + recall) ## f1-score = harmonic mean of precision and recall \n",
    "    ## store metrices per class\n",
    "    metrics_per_class[f'Class {i}'] = {\n",
    "        'Precision': precision,\n",
    "        'Recall': recall,\n",
    "        'F1-score': f1_score\n",
    "    }\n",
    "\n",
    "## define dict to store macro averages\n",
    "macro_metrics = {}\n",
    "\n",
    "# loop over classes\n",
    "for class_name, metrics in metrics_per_class.items():\n",
    "    print(f\"{class_name}:\")\n",
    "    for metric_name, metric_value in metrics.items(): ## loop over metrices \n",
    "        print(f\"\\t Baseline {metric_name}: {metric_value:.3f}\") ## print metrics for each class\n",
    "        macro_metrics[metric_name] = macro_metrics.get(metric_name, 0) + metric_value ## add class-wise metrics to total count\n",
    "\n",
    "## loop over metrices and print macro average\n",
    "for k, v in macro_metrics.items():\n",
    "    macro_metrics[k] = v / len(conf_mat[0])\n",
    "    print( f\"Baseline Macro {k}, {round(macro_metrics[k],3)} \")\n",
    "    \n",
    "        \n",
    "macro_metrics[\"Overall Accuracy\"] = accuracy\n",
    "macro_metrics[\"Balanced Accuracy\"] = balanced_accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "bdc809cd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'Precision': nan, 'Recall': 0.3333333333333333, 'F1-score': nan, 'Overall Accuracy': 0.6458333333333334, 'Balanced Accuracy': 0.3333333333333333}\n"
     ]
    }
   ],
   "source": [
    "print(macro_metrics)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "a08b148e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Precision</th>\n",
       "      <th>Recall</th>\n",
       "      <th>F1-score</th>\n",
       "      <th>Overall Accuracy</th>\n",
       "      <th>Balanced Accuracy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Baseline Model (majority predictor)</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.33</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.65</td>\n",
       "      <td>0.33</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                     Precision  Recall  F1-score  \\\n",
       "Baseline Model (majority predictor)        NaN    0.33       NaN   \n",
       "\n",
       "                                     Overall Accuracy  Balanced Accuracy  \n",
       "Baseline Model (majority predictor)              0.65               0.33  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create an empty DataFrame with two columns and three rows\n",
    "df_baseline = round(pd.DataFrame.from_dict(macro_metrics, orient='index', columns=['Baseline Model (majority predictor)'])\n",
    ",2).transpose()\n",
    "df_baseline\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6b66bb30",
   "metadata": {},
   "source": [
    "## Plot AUC curve on test set "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "4a86c182",
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_roc_curve(model, x_test, y_test):\n",
    "    \"\"\"\n",
    "    Plots the ROC curve for a multi-class classification model.\n",
    "    \"\"\"\n",
    "    y_pred = model.predict(x_test)\n",
    "    n_classes = y_test.shape[1]\n",
    "    \n",
    "    fpr = dict()\n",
    "    tpr = dict()\n",
    "    roc_auc = dict()\n",
    "    \n",
    "    for i in range(n_classes):\n",
    "        fpr[i], tpr[i], _ = roc_curve(y_test[:, i], y_pred[:, i])\n",
    "        roc_auc[i] = auc(fpr[i], tpr[i])\n",
    "    \n",
    "    plt.figure(figsize=(8, 6))\n",
    "    \n",
    "    for i in range(n_classes):\n",
    "        plt.plot(fpr[i], tpr[i], label='ROC curve (area = %0.2f) for class %d' % (roc_auc[i], i))\n",
    "    \n",
    "    plt.plot([0, 1], [0, 1], 'k--')\n",
    "    plt.xlim([-0.05, 1.0])\n",
    "    plt.ylim([0.0, 1.05])\n",
    "    plt.xlabel('False Positive Rate', fontsize = 12)\n",
    "    plt.ylabel('True Positive Rate', fontsize = 12)\n",
    "    plt.title('ROC Curve', fontsize = 12)\n",
    "    plt.legend(loc=\"lower right\")\n",
    "    plt.tight_layout()\n",
    "    plt.savefig('RESULTS/roc_curve_model1.png')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "1c8a9177",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2/2 [==============================] - 1s 169ms/step\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_roc_curve(model, x_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fc11c47e",
   "metadata": {},
   "source": [
    "## Get averaged confusion matrix over 10 runs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "67dbc8e8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[28.   1.2  1.8]\n",
      " [ 0.   5.4  3.6]\n",
      " [ 1.2  3.   3.8]]\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Load the matrix from the file\n",
    "avg_cm = np.load('avg_cm.npy')\n",
    "print(avg_cm)\n",
    "\n",
    "## define class names\n",
    "class_names = ['Normal', 'Benign', 'Malignant']\n",
    "\n",
    "# Plot confusion matrix\n",
    "ax = plt.gca()\n",
    "\n",
    "sns.heatmap(avg_cm, annot=True, fmt='g', cmap='Blues', square = True, xticklabels=class_names, yticklabels=class_names)\n",
    "plt.xlabel('Predicted classes', fontsize = 12)\n",
    "plt.ylabel('True classes', fontsize = 12)\n",
    "plt.title('Averaged confusion matrix', fontsize = 12)\n",
    "plt.tight_layout()\n",
    "plt.savefig('RESULTS/confusion_matrix_avg.png')\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ddfee466",
   "metadata": {},
   "source": [
    "## Get averaged evaluation metrics over 10 runs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "f3ee3581",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Overall Accuracy: 0.775\n",
      "Balanced Accuracy: 0.659\n",
      "Class 0:\n",
      "\tPrecision: 0.959\n",
      "\tRecall: 0.903\n",
      "\tF1-score: 0.930\n",
      "Class 1:\n",
      "\tPrecision: 0.562\n",
      "\tRecall: 0.600\n",
      "\tF1-score: 0.581\n",
      "Class 2:\n",
      "\tPrecision: 0.413\n",
      "\tRecall: 0.475\n",
      "\tF1-score: 0.442\n",
      "Macro Precision, 0.645 \n",
      "Macro Recall, 0.659 \n",
      "Macro F1-score, 0.651 \n"
     ]
    }
   ],
   "source": [
    "# Calculate total number of predictions\n",
    "conf_mat = avg_cm\n",
    "total_predictions = np.sum(conf_mat)\n",
    "\n",
    "# Calculate number of correct predictions\n",
    "correct_predictions = np.sum(np.diag(conf_mat))\n",
    "\n",
    "# Calculate overall accuracy\n",
    "accuracy = correct_predictions / total_predictions\n",
    "print(\"Overall Accuracy: {:.3f}\".format(accuracy))\n",
    "\n",
    "# Calculate balanced accuracy\n",
    "class_counts = np.sum(conf_mat, axis=1) ## true instances per class (row marginal - sum over cols)\n",
    "class_correct_predictions = np.diag(conf_mat) ## true positives (sum over diagoal)\n",
    "class_accuracy = class_correct_predictions / class_counts.flatten() ## calculate accuracy per class = tp/tp + fn\n",
    "balanced_accuracy = np.mean(class_accuracy) ## averaging over all class_accuracies = balanced accuracy\n",
    "print(\"Balanced Accuracy: {:.3f}\".format(balanced_accuracy))\n",
    "\n",
    "# Compute metrics for each class\n",
    "metrics_per_class = {} ## dict to store metrices\n",
    "for i in range(len(conf_mat)):\n",
    "    tp = conf_mat[i, i] # true positive\n",
    "    fp = np.sum(conf_mat[:, i]) - tp # false positive (predicted class i but not class i)\n",
    "    fn = np.sum(conf_mat[i, :]) - tp # false negatives (not predicted class i but class i)\n",
    "    \n",
    "    precision = tp / (tp + fp) ## precision of class i = tp / (tp + fp) -> correctly classified out of all predicted\n",
    "    recall = tp / (tp + fn) ## recall of class i = tp / (tp + fn)  -> correctly classified out of all instances of class i\n",
    "    f1_score = 2 * (precision * recall) / (precision + recall) ## f1-score = harmonic mean of precision and recall \n",
    "    ## store metrices per class\n",
    "    metrics_per_class[f'Class {i}'] = {\n",
    "        'Precision': precision,\n",
    "        'Recall': recall,\n",
    "        'F1-score': f1_score\n",
    "    }\n",
    "\n",
    "## define dict to store macro averages\n",
    "macro_metrics = {}\n",
    "\n",
    "# loop over classes\n",
    "for class_name, metrics in metrics_per_class.items():\n",
    "    print(f\"{class_name}:\")\n",
    "    for metric_name, metric_value in metrics.items(): ## loop over metrices \n",
    "        print(f\"\\t{metric_name}: {metric_value:.3f}\") ## print metrics for each class\n",
    "        macro_metrics[metric_name] = macro_metrics.get(metric_name, 0) + metric_value ## add class-wise metrics to total count\n",
    "\n",
    "## loop over metrices and print macro average\n",
    "for k, v in macro_metrics.items():\n",
    "    macro_metrics[k] = v / len(conf_mat[0])\n",
    "    print( f\"Macro {k}, {round(macro_metrics[k],3)} \")\n",
    "    \n",
    "macro_metrics[\"Overall Accuracy\"] = accuracy\n",
    "macro_metrics[\"Balanced Accuracy\"] = balanced_accuracy\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "07bae570",
   "metadata": {},
   "outputs": [],
   "source": [
    "print(metrics_per_class)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "0d79d984",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Class 0</th>\n",
       "      <th>Class 1</th>\n",
       "      <th>Class 2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Precision</th>\n",
       "      <td>0.96</td>\n",
       "      <td>0.56</td>\n",
       "      <td>0.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Recall</th>\n",
       "      <td>0.90</td>\n",
       "      <td>0.60</td>\n",
       "      <td>0.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F1-score</th>\n",
       "      <td>0.93</td>\n",
       "      <td>0.58</td>\n",
       "      <td>0.44</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Class 0  Class 1  Class 2\n",
       "Precision     0.96     0.56     0.41\n",
       "Recall        0.90     0.60     0.48\n",
       "F1-score      0.93     0.58     0.44"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_per_class_avg = round(pd.DataFrame(metrics_per_class),2)\n",
    "df_per_class_avg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "fc73e5a9",
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('RESULTS/class_results.tex','w') as tf:\n",
    "    tf.write(df_per_class_avg.to_latex())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "62bd3c09",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'Precision': 0.6448158626166369, 'Recall': 0.6594086021505378, 'F1-score': 0.6509127281820456, 'Overall Accuracy': 0.7749999999999999, 'Balanced Accuracy': 0.6594086021505378}\n"
     ]
    }
   ],
   "source": [
    "print(macro_metrics)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "62862e02",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Precision</th>\n",
       "      <th>Recall</th>\n",
       "      <th>F1-score</th>\n",
       "      <th>Overall Accuracy</th>\n",
       "      <th>Balanced Accuracy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>CNN Model (averaged over 10 runs)</th>\n",
       "      <td>0.64</td>\n",
       "      <td>0.66</td>\n",
       "      <td>0.65</td>\n",
       "      <td>0.77</td>\n",
       "      <td>0.66</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                   Precision  Recall  F1-score  \\\n",
       "CNN Model (averaged over 10 runs)       0.64    0.66      0.65   \n",
       "\n",
       "                                   Overall Accuracy  Balanced Accuracy  \n",
       "CNN Model (averaged over 10 runs)              0.77               0.66  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create an empty DataFrame with two columns and three rows\n",
    "df_macro_avg = round(pd.DataFrame.from_dict(macro_metrics, orient='index', columns=['CNN Model (averaged over 10 runs)'])\n",
    ",2).transpose()\n",
    "df_macro_avg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "283a297d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Precision</th>\n",
       "      <th>Recall</th>\n",
       "      <th>F1-score</th>\n",
       "      <th>Overall Accuracy</th>\n",
       "      <th>Balanced Accuracy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Baseline Model</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.33</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.65</td>\n",
       "      <td>0.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CNN model</th>\n",
       "      <td>0.64</td>\n",
       "      <td>0.66</td>\n",
       "      <td>0.65</td>\n",
       "      <td>0.77</td>\n",
       "      <td>0.66</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Precision  Recall  F1-score  Overall Accuracy  \\\n",
       "Baseline Model        NaN    0.33       NaN              0.65   \n",
       "CNN model            0.64    0.66      0.65              0.77   \n",
       "\n",
       "                Balanced Accuracy  \n",
       "Baseline Model               0.33  \n",
       "CNN model                    0.66  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## combining dataframes\n",
    "df3 = pd.concat([df_baseline, df_macro_avg])\n",
    "df3.index = ['Baseline Model', 'CNN model']\n",
    "df3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "b8b15cf7",
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('RESULTS/avg_results.tex','w') as tf:\n",
    "    tf.write(df3.to_latex())"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}