project_brain_tumour.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data preprocessing completed successfully.\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import cv2\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.model_selection import train_test_split\n",
    "import os\n",
    "\n",
    "def has_gaussian_noise(image, threshold=2):\n",
    "    \n",
    "    # Convert the image to grayscale if it's not already in grayscale\n",
    "    if len(image.shape) == 3:\n",
    "        gray_image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)\n",
    "    else:\n",
    "        gray_image = image\n",
    "\n",
    "    # Calculate the standard deviation of pixel values\n",
    "    std_deviation = np.std(gray_image)\n",
    "\n",
    "    # Check if the standard deviation is above the threshold\n",
    "    if std_deviation > threshold:\n",
    "        return True\n",
    "    else:\n",
    "        return False\n",
    "\n",
    "def has_salt_and_pepper_noise(image, threshold=0.4):\n",
    "    \n",
    "    # Convert the image to grayscale if it's not already in grayscale\n",
    "    if len(image.shape) == 3:\n",
    "        gray_image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)\n",
    "    else:\n",
    "        gray_image = image\n",
    "\n",
    "    # Calculate the percentage of pixels with extreme values\n",
    "    num_pixels = gray_image.size\n",
    "    num_extreme_pixels = np.sum((gray_image == 0) | (gray_image == 255))\n",
    "    percentage_extreme_pixels = num_extreme_pixels / num_pixels\n",
    "\n",
    "    # Check if the percentage of extreme pixels is above the threshold\n",
    "    if percentage_extreme_pixels > threshold:\n",
    "        return True\n",
    "    else:\n",
    "        return False\n",
    "\n",
    "def get_image_paths_and_labels(folder_path):\n",
    "    image_paths = []\n",
    "    labels = []\n",
    "\n",
    "    # Check if the folder exists\n",
    "    if not os.path.exists(folder_path):\n",
    "        print(f\"The folder '{folder_path}' does not exist.\")\n",
    "        return image_paths, labels\n",
    "\n",
    "    # Iterate through the files in the folder\n",
    "    for filename in os.listdir(folder_path):\n",
    "        file_path = os.path.join(folder_path, filename)\n",
    "\n",
    "        # Check if the file is a valid image\n",
    "        if os.path.isfile(file_path) and filename.lower().endswith(('.jpg', '.jpeg')):\n",
    "            image_paths.append(file_path)\n",
    "            labels.append(filename)  # Use the filename as the label\n",
    "            \n",
    "    return image_paths, labels\n",
    "\n",
    "# Example functions for data preprocessing\n",
    "def load_and_preprocess_data(image_paths, labels, target_size=(256, 256)):\n",
    "    # Load and preprocess MRI images\n",
    "    images = []\n",
    "    for path in image_paths:\n",
    "        try:\n",
    "            image = cv2.imread(path, cv2.IMREAD_GRAYSCALE)  # Read as grayscale\n",
    "\n",
    "            # Ensure the image has a valid size and aspect ratio\n",
    "            if image is not None and image.shape[0] > 0 and image.shape[1] > 0:\n",
    "                image = cv2.resize(image, target_size)  # Resize to a common size\n",
    "                image = image / 255.0  # Normalize pixel values to [0, 1]\n",
    "                images.append(image)\n",
    "                \n",
    "        except Exception as e:\n",
    "            print(f\"Error processing image at path {path}: {str(e)}\")\n",
    "\n",
    "    # Convert images and labels to NumPy arrays\n",
    "    images = np.array(images)\n",
    "    labels = np.array(labels)\n",
    "\n",
    "    return images, labels\n",
    "\n",
    "def data_augmentation(images):\n",
    "    # Example data augmentation (rotate, flip, and add noise)\n",
    "    augmented_images = []\n",
    "    for image in images:\n",
    "        augmented_image = image.copy()  # Create a copy of the original image\n",
    "\n",
    "        if has_gaussian_noise(augmented_image) == True:\n",
    "            augmented_image = cv2.GaussianBlur(augmented_image, (5, 5), 0)\n",
    "\n",
    "        if has_salt_and_pepper_noise(augmented_image) == True:\n",
    "            augmented_image = cv2.medianBlur(np.float32(augmented_image), 5)\n",
    "\n",
    "        augmented_images.append(augmented_image)\n",
    "\n",
    "    return np.array(augmented_images)\n",
    "           \n",
    "def split_data(images, labels):\n",
    "    try:\n",
    "        # Split data into training, validation, and test sets\n",
    "        X_train, X_temp, y_train, y_temp = train_test_split(images, labels, test_size=0.3)\n",
    "        X_val, X_test, y_val, y_test = train_test_split(X_temp, y_temp, test_size=0.5)\n",
    "\n",
    "        return X_train, y_train, X_val, y_val, X_test, y_test\n",
    "    except ValueError as e:\n",
    "        print(f\"ValueError occurred during data splitting: {str(e)}\")\n",
    "        return None, None, None, None, None, None\n",
    "\n",
    "folder_path_tumor = \"Brain Tumor Data Set\\Brain Tumor Data Set\\Brain Tumor\"\n",
    "image_paths_tumor, labels_tumor = get_image_paths_and_labels(folder_path_tumor)\n",
    "\n",
    "folder_path_healthy = \"Brain Tumor Data Set\\Brain Tumor Data Set\\Healthy\"\n",
    "image_paths_healthy, labels_healthy = get_image_paths_and_labels(folder_path_healthy)\n",
    "\n",
    "image_paths = image_paths_healthy + image_paths_tumor\n",
    "labels = labels_healthy + labels_tumor\n",
    "\n",
    "for i in range(len(labels)) :\n",
    "    if \"Not Cancer  (\" in labels[i] :\n",
    "        labels[i] = 0\n",
    "    elif \"Cancer (\" in labels[i]:\n",
    "        labels[i] = 1\n",
    "\n",
    "images, labels = load_and_preprocess_data(image_paths, labels)\n",
    "\n",
    "# Data augmentation\n",
    "augmented_images = data_augmentation(images)\n",
    "\n",
    "# Split data (you had an issue here, so I fixed it)\n",
    "X_train, y_train, X_val, y_val, X_test, y_test = split_data(augmented_images, labels)\n",
    "\n",
    "# Check for errors and handle accordingly\n",
    "if X_train is None:\n",
    "    # Handle the error gracefully, e.g., print an error message or log it\n",
    "    print(\"Error occurred during data splitting.\")\n",
    "else:\n",
    "    # Continue with model training or further processing\n",
    "    print(\"Data preprocessing completed successfully.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras import Sequential\n",
    "from keras.layers import InputLayer, Conv2D, MaxPool2D, Flatten, Dense, BatchNormalization, Dropout, PReLU, ELU\n",
    "from keras.callbacks import EarlyStopping , ReduceLROnPlateau, ModelCheckpoint, LearningRateScheduler\n",
    "\n",
    "reduce_lr = ReduceLROnPlateau(\n",
    "    monitor='val_loss',\n",
    "    factor=0.5,\n",
    "    patience=2,\n",
    "    min_lr=1e-5\n",
    ")\n",
    "\n",
    "def custom_lr_schedule(epoch, current_lr):\n",
    "    if epoch < 5:\n",
    "        return 0.01  # Higher learning rate\n",
    "    elif epoch < 12:\n",
    "        return 0.001  # Medium learning rate\n",
    "    else:\n",
    "        return 0.0001  # Slower learning rate\n",
    "\n",
    "lr_scheduler = LearningRateScheduler(custom_lr_schedule)\n",
    "    \n",
    "model = Sequential()\n",
    "model.add(InputLayer(input_shape=(256,256,1)))\n",
    "model.add(Conv2D(filters=32, kernel_size=3, activation='relu', padding=\"same\"))\n",
    "model.add(MaxPool2D())\n",
    "model.add(Conv2D(filters=64, kernel_size=3, activation='relu', padding=\"same\"))\n",
    "model.add(MaxPool2D())\n",
    "model.add(Conv2D(filters=128, kernel_size=3, activation=PReLU(), padding=\"same\"))\n",
    "model.add(MaxPool2D())\n",
    "model.add(Conv2D(filters=256, kernel_size=3, activation=PReLU(), padding=\"same\"))\n",
    "model.add(MaxPool2D())\n",
    "model.add(Flatten())\n",
    "model.add(Dense(128, activation=ELU()))\n",
    "model.add(BatchNormalization())\n",
    "model.add(Dense(64, activation=ELU()))\n",
    "model.add(BatchNormalization())\n",
    "model.add(Dense(32, activation='relu'))\n",
    "model.add(BatchNormalization())\n",
    "model.add(Dropout(rate=0.5))\n",
    "model.add(Dense(1, activation='sigmoid'))\n",
    "\n",
    "early_stopping = EarlyStopping(monitor='val_loss', patience=5, restore_best_weights=True)\n",
    "reduce_lr = ReduceLROnPlateau(monitor='val_loss', factor=0.5, patience=2, min_lr=1e-5)\n",
    "model_checkpoint = ModelCheckpoint('latest_check.h5', save_best_only=True)\n",
    "\n",
    "model.compile(optimizer='nadam', loss='binary_crossentropy', metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_1\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " conv2d_4 (Conv2D)           (None, 256, 256, 32)      320       \n",
      "                                                                 \n",
      " max_pooling2d_4 (MaxPoolin  (None, 128, 128, 32)      0         \n",
      " g2D)                                                            \n",
      "                                                                 \n",
      " conv2d_5 (Conv2D)           (None, 128, 128, 64)      18496     \n",
      "                                                                 \n",
      " max_pooling2d_5 (MaxPoolin  (None, 64, 64, 64)        0         \n",
      " g2D)                                                            \n",
      "                                                                 \n",
      " conv2d_6 (Conv2D)           (None, 64, 64, 128)       598144    \n",
      "                                                                 \n",
      " max_pooling2d_6 (MaxPoolin  (None, 32, 32, 128)       0         \n",
      " g2D)                                                            \n",
      "                                                                 \n",
      " conv2d_7 (Conv2D)           (None, 32, 32, 256)       557312    \n",
      "                                                                 \n",
      " max_pooling2d_7 (MaxPoolin  (None, 16, 16, 256)       0         \n",
      " g2D)                                                            \n",
      "                                                                 \n",
      " flatten_1 (Flatten)         (None, 65536)             0         \n",
      "                                                                 \n",
      " dense_4 (Dense)             (None, 128)               8388736   \n",
      "                                                                 \n",
      " batch_normalization_3 (Bat  (None, 128)               512       \n",
      " chNormalization)                                                \n",
      "                                                                 \n",
      " dense_5 (Dense)             (None, 64)                8256      \n",
      "                                                                 \n",
      " batch_normalization_4 (Bat  (None, 64)                256       \n",
      " chNormalization)                                                \n",
      "                                                                 \n",
      " dense_6 (Dense)             (None, 32)                2080      \n",
      "                                                                 \n",
      " batch_normalization_5 (Bat  (None, 32)                128       \n",
      " chNormalization)                                                \n",
      "                                                                 \n",
      " dropout_1 (Dropout)         (None, 32)                0         \n",
      "                                                                 \n",
      " dense_7 (Dense)             (None, 1)                 33        \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 9574273 (36.52 MB)\n",
      "Trainable params: 9573825 (36.52 MB)\n",
      "Non-trainable params: 448 (1.75 KB)\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/20\n",
      "99/99 [==============================] - ETA: 0s - loss: 0.6098 - accuracy: 0.6868"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\ariha\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\keras\\src\\engine\\training.py:3000: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.\n",
      "  saving_api.save_model(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "99/99 [==============================] - 236s 2s/step - loss: 0.6098 - accuracy: 0.6868 - val_loss: 2.5013 - val_accuracy: 0.5968 - lr: 0.0100\n",
      "Epoch 2/20\n",
      "99/99 [==============================] - 236s 2s/step - loss: 0.4720 - accuracy: 0.7736 - val_loss: 0.6960 - val_accuracy: 0.6839 - lr: 0.0100\n",
      "Epoch 3/20\n",
      "99/99 [==============================] - 234s 2s/step - loss: 0.3592 - accuracy: 0.8486 - val_loss: 0.9593 - val_accuracy: 0.7016 - lr: 0.0100\n",
      "Epoch 4/20\n",
      "99/99 [==============================] - 231s 2s/step - loss: 0.2631 - accuracy: 0.8955 - val_loss: 1.9503 - val_accuracy: 0.5746 - lr: 0.0100\n",
      "Epoch 5/20\n",
      "99/99 [==============================] - 233s 2s/step - loss: 0.1863 - accuracy: 0.9351 - val_loss: 0.8173 - val_accuracy: 0.7976 - lr: 0.0100\n",
      "Epoch 6/20\n",
      "99/99 [==============================] - 226s 2s/step - loss: 0.1164 - accuracy: 0.9617 - val_loss: 0.3830 - val_accuracy: 0.8700 - lr: 0.0010\n",
      "Epoch 7/20\n",
      "99/99 [==============================] - 234s 2s/step - loss: 0.0781 - accuracy: 0.9807 - val_loss: 0.1831 - val_accuracy: 0.9394 - lr: 0.0010\n",
      "Epoch 8/20\n",
      "99/99 [==============================] - 237s 2s/step - loss: 0.0583 - accuracy: 0.9826 - val_loss: 0.1466 - val_accuracy: 0.9483 - lr: 0.0010\n",
      "Epoch 9/20\n",
      "99/99 [==============================] - 240s 2s/step - loss: 0.0514 - accuracy: 0.9851 - val_loss: 0.1282 - val_accuracy: 0.9586 - lr: 0.0010\n",
      "Epoch 10/20\n",
      "99/99 [==============================] - 239s 2s/step - loss: 0.0506 - accuracy: 0.9870 - val_loss: 0.1195 - val_accuracy: 0.9616 - lr: 0.0010\n",
      "Epoch 11/20\n",
      "99/99 [==============================] - 241s 2s/step - loss: 0.0444 - accuracy: 0.9867 - val_loss: 0.1407 - val_accuracy: 0.9572 - lr: 0.0010\n",
      "Epoch 12/20\n",
      "99/99 [==============================] - 239s 2s/step - loss: 0.0376 - accuracy: 0.9873 - val_loss: 0.0969 - val_accuracy: 0.9705 - lr: 0.0010\n",
      "Epoch 13/20\n",
      "99/99 [==============================] - 239s 2s/step - loss: 0.0395 - accuracy: 0.9899 - val_loss: 0.0938 - val_accuracy: 0.9705 - lr: 1.0000e-04\n",
      "Epoch 14/20\n",
      "99/99 [==============================] - 238s 2s/step - loss: 0.0275 - accuracy: 0.9930 - val_loss: 0.0939 - val_accuracy: 0.9690 - lr: 1.0000e-04\n",
      "Epoch 15/20\n",
      "99/99 [==============================] - 238s 2s/step - loss: 0.0254 - accuracy: 0.9937 - val_loss: 0.0962 - val_accuracy: 0.9690 - lr: 1.0000e-04\n",
      "Epoch 16/20\n",
      "99/99 [==============================] - 239s 2s/step - loss: 0.0281 - accuracy: 0.9921 - val_loss: 0.0966 - val_accuracy: 0.9690 - lr: 1.0000e-04\n",
      "Epoch 17/20\n",
      "99/99 [==============================] - 241s 2s/step - loss: 0.0261 - accuracy: 0.9934 - val_loss: 0.0953 - val_accuracy: 0.9705 - lr: 1.0000e-04\n",
      "Epoch 18/20\n",
      "99/99 [==============================] - 239s 2s/step - loss: 0.0285 - accuracy: 0.9946 - val_loss: 0.0946 - val_accuracy: 0.9690 - lr: 1.0000e-04\n"
     ]
    }
   ],
   "source": [
    "history = model.fit(X_train,y_train,batch_size=32,epochs=20,verbose=1,validation_data=(X_val,y_val),callbacks=[lr_scheduler,early_stopping, reduce_lr, model_checkpoint])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12, 6))\n",
    "plt.plot(history.history['loss'], label='Training Loss')\n",
    "plt.plot(history.history['val_loss'], label='Validation Loss')\n",
    "plt.xlabel('Epochs')\n",
    "plt.ylabel('Loss')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "22/22 [==============================] - 11s 509ms/step\n"
     ]
    }
   ],
   "source": [
    "y_pred = model.predict(X_test)\n",
    "y_pred_binary = (y_pred > 0.5).astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.9601181683899557\n",
      "Precision: 0.9551451187335093\n",
      "Recall: 0.9731182795698925\n",
      "F1 Score: 0.9640479360852198\n",
      "Confusion Matrix:\n",
      " [[288  17]\n",
      " [ 10 362]]\n",
      "AUC: 0.9586902873259299\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, confusion_matrix, roc_curve, roc_auc_score, mean_squared_error\n",
    "\n",
    "# Accuracy\n",
    "accuracy = accuracy_score(y_test,y_pred_binary)\n",
    "print(\"Accuracy:\", accuracy)\n",
    "\n",
    "# Precision\n",
    "precision = precision_score(y_test,y_pred_binary)\n",
    "print(\"Precision:\", precision)\n",
    "\n",
    "# Recall\n",
    "recall = recall_score(y_test,y_pred_binary)\n",
    "print(\"Recall:\", recall)\n",
    "\n",
    "# F1 Score\n",
    "f1 = f1_score(y_test,y_pred_binary)\n",
    "print(\"F1 Score:\", f1)\n",
    "\n",
    "# Confusion Matrix\n",
    "conf_matrix = confusion_matrix(y_test,y_pred_binary)\n",
    "print(\"Confusion Matrix:\\n\", conf_matrix)\n",
    "\n",
    "# ROC Curve and AUC\n",
    "fpr, tpr, thresholds = roc_curve(y_test,y_pred_binary)\n",
    "auc = roc_auc_score(y_test,y_pred_binary)\n",
    "print(\"AUC:\", auc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "class_distribution = np.bincount(labels)\n",
    "\n",
    "class_names = [\"Non-Tumor\", \"Tumor\"]\n",
    "\n",
    "plt.bar(class_names, class_distribution)\n",
    "plt.xlabel(\"Class\")\n",
    "plt.ylabel(\"Number of Images\")\n",
    "plt.title(\"Class Distribution of Images\")\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "\n",
    "cm = confusion_matrix(y_test, y_pred_binary)\n",
    "\n",
    "plt.figure(figsize=(6, 4))\n",
    "sns.heatmap(cm, annot=True, fmt=\"d\", cmap=\"Blues\")\n",
    "plt.xlabel('Predicted')\n",
    "plt.ylabel('True')\n",
    "plt.title('Confusion Matrix')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import precision_recall_curve\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "precision, recall, thresholds = precision_recall_curve(y_test, y_pred_binary)\n",
    "\n",
    "plt.plot(recall, precision, marker='.')\n",
    "plt.xlabel('Recall')\n",
    "plt.ylabel('Precision')\n",
    "plt.title('Precision-Recall Curve')\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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N7ywrSI8++qi2b9+uNWvWZNt24cIFpaenS7pWf3p6umbPnm3bnpGRcUuvQffu3eXk5KRJkyZlOxvx99ejdOnSOd4mXhg1OkJeX+PTp0/bzatKSkrSwoUL1bBhQwUGBkq69nN7/c/s0qVL9fvvv+dYw8KFC3Xp0iXb8hdffKH4+HiHBIfcXp/rNW7cWFWrVtVbb72l5OTkbNvPnj0r6dprc/3lY39/fwUHB5e4T//GNZzZgWnVrl1brVq1UuPGjeXn56ddu3bZbiXN0rhxY0nXPpm2Q4cOcnZ21mOPPaauXbuqdevWevXVV3Xy5Ek1aNBAa9eu1VdffaUXX3zR9hdi48aN1bNnT82aNUvnz5+33Xqe9VfwrfzF6e3trRYtWmj69OlKS0vTHXfcobVr19o+O6WgNWjQQBEREfrwww914cIFtWzZUj/88IMWLFig7t27q3Xr1oVSx/VGjRql//73v3rggQfUr18/NW7cWCkpKdq/f7+++OILnTx5UuXKlVPXrl3VrFkzvfzyyzp58qTtM2Fymut0vbCwML366quaPHmymjdvrh49eshqterHH39UcHCwoqKiJF17nWfPnq3XX39dYWFh8vf3V5s2bQqlRkfI62tcvXp1DRgwQD/++KMCAgL00UcfKTExUfPmzbO1eeCBBzRp0iT1799f9957r/bv369FixblOqfFz89P9913n/r376/ExETNmjVLYWFhGjhw4G0fX26vz/WcnJz0n//8R506dVKdOnXUv39/3XHHHfr999/13XffydvbW19//bUuXbqkChUq6OGHH1aDBg3k6empdevW6ccff7zlz65CMVN0N4IBucu6vTTrltrrtWzZ8qa3nr/++uvG3Xffbfj6+hru7u5GzZo1jSlTphhXr161tUlPTzeee+45o3z58obFYrG7LfnSpUvGsGHDjODgYKNUqVJGtWrVjDfffNPullXDMIyUlBQjMjLS8PPzMzw9PY3u3bsbsbGxhiS7W8Gzbgs+e/ZstuP57bffjIceesjw9fU1fHx8jEceecQ4ffp0rrevX99HbreE5zROOUlLSzMmTpxoVK5c2ShVqpQREhJijBkzxrhy5cot7Scnt9o2NDQ0148IuHTpkjFmzBgjLCzMcHV1NcqVK2fce++9xltvvWX3Op4/f97o06eP4e3tbfj4+Bh9+vQx9uzZc9Nbz7N89NFHxp133mlYrVajTJkyRsuWLY2YmBjb9oSEBKNLly6Gl5eXIcnuNmdH15iTrFuoly5desN2N/oZu9XXOOv1WLNmjVG/fn3DarUaNWvWzLbvK1euGCNGjDCCgoIMd3d3o1mzZsb27duNli1b2o1PVu2ffvqpMWbMGMPf399wd3c3unTpYvz66692feb31vPcXp/rbz3PsmfPHqNHjx5G2bJlDavVaoSGhhqPPvqosX79esMwDCM1NdUYNWqU0aBBA8PLy8soXbq00aBBA+P999+/weijOLMYxi3OXgRwy/bu3as777xTn3zyiZ544omiLgcA/tGYswPcpr/++ivbulmzZsnJyemmn1wMACh4zNkBbtP06dO1e/dutW7dWi4uLvr222/17bffatCgQQoJCSnq8gDgH4/LWMBtiomJ0cSJE3Xw4EElJyerYsWK6tOnj1599dUS/Q3bAGAWhB0AAGBqzNkBAACmRtgBAACmxoQCXfu+lNOnT8vLy6tAvh8IAAA4nmEYunTpkoKDg+XklPv5G8KOrn00OnfNAABQMp06dUoVKlTIdTthR7J90eOpU6fk7e1dxNUAAIBbkZSUpJCQELsvbM4JYUf///1F3t7ehB0AAEqYm01BYYIyAAAwNcIOAAAwNcIOAAAwNcIOAAAwNcIOAAAwNcIOAAAwNcIOAAAwNcIOAAAwNcIOAAAwNcIOAAAwNcIOAAAwNcIOAAAwNcIOAAAwNcIOAAAwNZeiLgAAABQPcXFxOnfunMP7LVeunCpWrOjwfm8VYQcAACguLk41atbSlb8uO7xvN3cPxf5yqMgCD2EHAADo3LlzuvLXZZV9YIRKlQ1xWL9p50/p/MoZOnfuHGEHAAAUvVJlQ2QNDCvqMhyKCcoAAMDUCDsAAMDUCDsAAMDUCDsAAMDUCDsAAMDUCDsAAMDUCDsAAMDUCDsAAMDUCDsAAMDUCDsAAMDUCDsAAMDUCDsAAMDUCDsAAMDUCDsAAMDUCDsAAMDUCDsAAMDUCDsAAMDUCDsAAMDUCDsAAMDUijTszJ49W/Xr15e3t7e8vb0VHh6ub7/91ra9VatWslgsdo/Bgwfb9REXF6cuXbrIw8ND/v7+GjVqlNLT0wv7UAAAQDHlUpQ7r1ChgqZNm6Zq1arJMAwtWLBA3bp10549e1SnTh1J0sCBAzVp0iTbczw8PGz/zsjIUJcuXRQYGKht27YpPj5effv2ValSpTR16tRCPx4AAFD8FGnY6dq1q93ylClTNHv2bO3YscMWdjw8PBQYGJjj89euXauDBw9q3bp1CggIUMOGDTV58mSNHj1aEyZMkKura4EfAwAAKN6KzZydjIwMLVmyRCkpKQoPD7etX7RokcqVK6e6detqzJgxunz5sm3b9u3bVa9ePQUEBNjWdejQQUlJSTpw4ECh1g8AAIqnIj2zI0n79+9XeHi4rly5Ik9PTy1fvly1a9eWJPXu3VuhoaEKDg7Wvn37NHr0aMXGxmrZsmWSpISEBLugI8m2nJCQkOs+U1NTlZqaaltOSkpy9GEBAIBiosjDTo0aNbR3715dvHhRX3zxhSIiIrRp0ybVrl1bgwYNsrWrV6+egoKC1LZtWx07dkxVq1bN9z6joqI0ceJER5QPAACKuSK/jOXq6qqwsDA1btxYUVFRatCggd55550c2zZt2lSSdPToUUlSYGCgEhMT7dpkLec2z0eSxowZo4sXL9oep06dcsShAACAYqjIw871MjMz7S4x/d3evXslSUFBQZKk8PBw7d+/X2fOnLG1iYmJkbe3t+1SWE6sVqvtdvesBwAAMKcivYw1ZswYderUSRUrVtSlS5e0ePFibdy4UWvWrNGxY8e0ePFide7cWWXLltW+ffs0bNgwtWjRQvXr15ck3X///apdu7b69Omj6dOnKyEhQWPHjlVkZKSsVmtRHhoAACgmijTsnDlzRn379lV8fLx8fHxUv359rVmzRu3bt9epU6e0bt06zZo1SykpKQoJCVHPnj01duxY2/OdnZ21cuVKPfvsswoPD1fp0qUVERFh97k8AADgn61Iw87cuXNz3RYSEqJNmzbdtI/Q0FCtWrXKkWUBAAATKXZzdgAAAByJsAMAAEyNsAMAAEyNsAMAAEyNsAMAAEyNsAMAAEyNsAMAAEyNsAMAAEyNsAMAAEyNsAMAAEyNsAMAAEyNsAMAAEyNsAMAAEyNsAMAAEyNsAMAAEyNsAMAAEyNsAMAAEyNsAMAAEyNsAMAAEyNsAMAAEyNsAMAAEyNsAMAAEyNsAMAAEyNsAMAAEyNsAMAAEyNsAMAAEyNsAMAAEyNsAMAAEyNsAMAAEyNsAMAAEyNsAMAAEyNsAMAAEyNsAMAAEyNsAMAAEyNsAMAAEyNsAMAAEytSMPO7NmzVb9+fXl7e8vb21vh4eH69ttvbduvXLmiyMhIlS1bVp6enurZs6cSExPt+oiLi1OXLl3k4eEhf39/jRo1Sunp6YV9KAAAoJgq0rBToUIFTZs2Tbt379auXbvUpk0bdevWTQcOHJAkDRs2TF9//bWWLl2qTZs26fTp0+rRo4ft+RkZGerSpYuuXr2qbdu2acGCBZo/f77GjRtXVIcEAACKGZei3HnXrl3tlqdMmaLZs2drx44dqlChgubOnavFixerTZs2kqR58+apVq1a2rFjh+655x6tXbtWBw8e1Lp16xQQEKCGDRtq8uTJGj16tCZMmCBXV9eiOCwAAFCMFJs5OxkZGVqyZIlSUlIUHh6u3bt3Ky0tTe3atbO1qVmzpipWrKjt27dLkrZv36569eopICDA1qZDhw5KSkqynR3KSWpqqpKSkuweAADAnIo87Ozfv1+enp6yWq0aPHiwli9frtq1ayshIUGurq7y9fW1ax8QEKCEhARJUkJCgl3QydqetS03UVFR8vHxsT1CQkIce1AAAKDYKNLLWJJUo0YN7d27VxcvXtQXX3yhiIgIbdq0qUD3OWbMGA0fPty2nJSUVGCBJy4uTufOnSuQvsuVK6eKFSsWSN8AAJhFkYcdV1dXhYWFSZIaN26sH3/8Ue+884569eqlq1ev6sKFC3ZndxITExUYGChJCgwM1A8//GDXX9bdWlltcmK1WmW1Wh18JNnFxcWpRs1auvLX5QLp383dQ7G/HCLwAABwA0Uedq6XmZmp1NRUNW7cWKVKldL69evVs2dPSVJsbKzi4uIUHh4uSQoPD9eUKVN05swZ+fv7S5JiYmLk7e2t2rVrF9kxZDl37pyu/HVZZR8YoVJlHXvmKO38KZ1fOUPnzp0j7AAAcANFGnbGjBmjTp06qWLFirp06ZIWL16sjRs3as2aNfLx8dGAAQM0fPhw+fn5ydvbW88995zCw8N1zz33SJLuv/9+1a5dW3369NH06dOVkJCgsWPHKjIyslDO3NyqUmVDZA0MK+oyAAD4RyrSsHPmzBn17dtX8fHx8vHxUf369bVmzRq1b99ekvT222/LyclJPXv2VGpqqjp06KD333/f9nxnZ2etXLlSzz77rMLDw1W6dGlFRERo0qRJRXVIAACgmCnSsDN37twbbndzc1N0dLSio6NzbRMaGqpVq1Y5ujQAAGASRX7rOQAAQEEi7AAAAFMj7AAAAFMj7AAAAFMj7AAAAFMj7AAAAFMj7AAAAFMj7AAAAFMj7AAAAFMj7AAAAFMj7AAAAFMj7AAAAFMj7AAAAFMj7AAAAFMj7AAAAFMj7AAAAFMj7AAAAFMj7AAAAFMj7AAAAFMj7AAAAFMj7AAAAFMj7AAAAFMj7AAAAFMj7AAAAFMj7AAAAFMj7AAAAFMj7AAAAFMj7AAAAFMj7AAAAFMj7AAAAFMj7AAAAFMj7AAAAFMj7AAAAFMj7AAAAFMj7AAAAFMr0rATFRWlu+66S15eXvL391f37t0VGxtr16ZVq1ayWCx2j8GDB9u1iYuLU5cuXeTh4SF/f3+NGjVK6enphXkoAACgmHIpyp1v2rRJkZGRuuuuu5Senq5XXnlF999/vw4ePKjSpUvb2g0cOFCTJk2yLXt4eNj+nZGRoS5duigwMFDbtm1TfHy8+vbtq1KlSmnq1KmFejwAAKD4KdKws3r1arvl+fPny9/fX7t371aLFi1s6z08PBQYGJhjH2vXrtXBgwe1bt06BQQEqGHDhpo8ebJGjx6tCRMmyNXVtUCPAQAAFG/Fas7OxYsXJUl+fn526xctWqRy5cqpbt26GjNmjC5fvmzbtn37dtWrV08BAQG2dR06dFBSUpIOHDhQOIUDAIBiq0jP7PxdZmamXnzxRTVr1kx169a1re/du7dCQ0MVHBysffv2afTo0YqNjdWyZcskSQkJCXZBR5JtOSEhIcd9paamKjU11baclJTk6MMBAADFRLEJO5GRkfrf//6nLVu22K0fNGiQ7d/16tVTUFCQ2rZtq2PHjqlq1ar52ldUVJQmTpx4W/UCAICSoVhcxho6dKhWrlyp7777ThUqVLhh26ZNm0qSjh49KkkKDAxUYmKiXZus5dzm+YwZM0YXL160PU6dOnW7hwAAAIqpIg07hmFo6NChWr58uTZs2KDKlSvf9Dl79+6VJAUFBUmSwsPDtX//fp05c8bWJiYmRt7e3qpdu3aOfVitVnl7e9s9AACAORXpZazIyEgtXrxYX331lby8vGxzbHx8fOTu7q5jx45p8eLF6ty5s8qWLat9+/Zp2LBhatGiherXry9Juv/++1W7dm316dNH06dPV0JCgsaOHavIyEhZrdaiPDwAAFAMFOmZndmzZ+vixYtq1aqVgoKCbI/PPvtMkuTq6qp169bp/vvvV82aNTVixAj17NlTX3/9ta0PZ2dnrVy5Us7OzgoPD9eTTz6pvn372n0uDwAA+Ocq0jM7hmHccHtISIg2bdp0035CQ0O1atUqR5UFAABMpFhMUAYAACgohB0AAGBqhB0AAGBqhB0AAGBqhB0AAGBqhB0AAGBqhB0AAGBqhB0AAGBq+Qo7x48fd3QdAAAABSJfYScsLEytW7fWJ598oitXrji6JgAAAIfJV9j56aefVL9+fQ0fPlyBgYF65pln9MMPPzi6NgAAgNuWr7DTsGFDvfPOOzp9+rQ++ugjxcfH67777lPdunU1c+ZMnT171tF1AgAA5MttTVB2cXFRjx49tHTpUr3xxhs6evSoRo4cqZCQEPXt21fx8fGOqhMAACBfbivs7Nq1S0OGDFFQUJBmzpypkSNH6tixY4qJidHp06fVrVs3R9UJAACQLy75edLMmTM1b948xcbGqnPnzlq4cKE6d+4sJ6dr2aly5cqaP3++KlWq5MhaAQAA8ixfYWf27Nl66qmn1K9fPwUFBeXYxt/fX3Pnzr2t4gAAAG5XvsLOkSNHbtrG1dVVERER+ekeAADAYfI1Z2fevHlaunRptvVLly7VggULbrsoAAAAR8lX2ImKilK5cuWyrff399fUqVNvuygAAABHyVfYiYuLU+XKlbOtDw0NVVxc3G0XBQAA4Cj5Cjv+/v7at29ftvU///yzypYte9tFAQAAOEq+ws7jjz+u559/Xt99950yMjKUkZGhDRs26IUXXtBjjz3m6BoBAADyLV93Y02ePFknT55U27Zt5eJyrYvMzEz17duXOTsAAKBYyVfYcXV11WeffabJkyfr559/lru7u+rVq6fQ0FBH1wcAAHBb8hV2slSvXl3Vq1d3VC0AAAAOl6+wk5GRofnz52v9+vU6c+aMMjMz7bZv2LDBIcUBAADcrnyFnRdeeEHz589Xly5dVLduXVksFkfXBQAA4BD5CjtLlizR559/rs6dOzu6HgAAAIfK163nrq6uCgsLc3QtAAAADpevsDNixAi98847MgzD0fUAAAA4VL4uY23ZskXfffedvv32W9WpU0elSpWy275s2TKHFAcAAHC78hV2fH199dBDDzm6FgAAAIfLV9iZN2+eo+sAAAAoEPmasyNJ6enpWrdunT744ANdunRJknT69GklJyc7rDgAAIDbla8zO7/++qs6duyouLg4paamqn379vLy8tIbb7yh1NRUzZkzx9F1AgAA5Eu+zuy88MILatKkif7880+5u7vb1j/00ENav369w4oDAAC4XfkKO99//73Gjh0rV1dXu/WVKlXS77//fsv9REVF6a677pKXl5f8/f3VvXt3xcbG2rW5cuWKIiMjVbZsWXl6eqpnz55KTEy0axMXF6cuXbrIw8ND/v7+GjVqlNLT0/NzaAAAwGTyFXYyMzOVkZGRbf1vv/0mLy+vW+5n06ZNioyM1I4dOxQTE6O0tDTdf//9SklJsbUZNmyYvv76ay1dulSbNm3S6dOn1aNHD9v2jIwMdenSRVevXtW2bdu0YMECzZ8/X+PGjcvPoQEAAJPJV9i5//77NWvWLNuyxWJRcnKyxo8fn6evkFi9erX69eunOnXqqEGDBpo/f77i4uK0e/duSdLFixc1d+5czZw5U23atFHjxo01b948bdu2TTt27JAkrV27VgcPHtQnn3yihg0bqlOnTpo8ebKio6N19erV/BweAAAwkXyFnRkzZmjr1q2qXbu2rly5ot69e9suYb3xxhv5LubixYuSJD8/P0nS7t27lZaWpnbt2tna1KxZUxUrVtT27dslSdu3b1e9evUUEBBga9OhQwclJSXpwIEDOe4nNTVVSUlJdg8AAGBO+bobq0KFCvr555+1ZMkS7du3T8nJyRowYICeeOIJuwnLeZGZmakXX3xRzZo1U926dSVJCQkJcnV1la+vr13bgIAAJSQk2Nr8Pehkbc/alpOoqChNnDgxX3UCAICSJV9hR5JcXFz05JNPOqyQyMhI/e9//9OWLVsc1mduxowZo+HDh9uWk5KSFBISUuD7BQAAhS9fYWfhwoU33N63b9889Td06FCtXLlSmzdvVoUKFWzrAwMDdfXqVV24cMHu7E5iYqICAwNtbX744Qe7/rLu1spqcz2r1Sqr1ZqnGgEAQMmUr7Dzwgsv2C2npaXp8uXLcnV1lYeHxy2HHcMw9Nxzz2n58uXauHGjKleubLe9cePGKlWqlNavX6+ePXtKkmJjYxUXF6fw8HBJUnh4uKZMmaIzZ87I399fkhQTEyNvb2/Vrl07P4cHAABMJF9h588//8y27siRI3r22Wc1atSoW+4nMjJSixcv1ldffSUvLy/bHBsfHx+5u7vLx8dHAwYM0PDhw+Xn5ydvb28999xzCg8P1z333CPp2p1htWvXVp8+fTR9+nQlJCRo7NixioyM5OwNAADI/3djXa9atWqaNm1atrM+NzJ79mxdvHhRrVq1UlBQkO3x2Wef2dq8/fbbeuCBB9SzZ0+1aNFCgYGBWrZsmW27s7OzVq5cKWdnZ4WHh+vJJ59U3759NWnSJEcdGgAAKMHyPUE5x85cXHT69Olbbm8Yxk3buLm5KTo6WtHR0bm2CQ0N1apVq255vwAA4J8jX2Hnv//9r92yYRiKj4/Xv/71LzVr1swhhQEAADhCvsJO9+7d7ZYtFovKly+vNm3aaMaMGY6oCwAAwCHyFXYyMzMdXQcAAECBcNgEZQAAgOIoX2d2/v7pwzczc+bM/OwCAADAIfIVdvbs2aM9e/YoLS1NNWrUkCQdPnxYzs7OatSoka2dxWJxTJUAAAD5lK+w07VrV3l5eWnBggUqU6aMpGsfNNi/f381b95cI0aMcGiRAAAA+ZWvOTszZsxQVFSULehIUpkyZfT6669zNxYAAChW8hV2kpKSdPbs2Wzrz549q0uXLt12UQAAAI6Sr7Dz0EMPqX///lq2bJl+++03/fbbb/ryyy81YMAA9ejRw9E1AgAA5Fu+5uzMmTNHI0eOVO/evZWWlnatIxcXDRgwQG+++aZDCwQAALgd+Qo7Hh4eev/99/Xmm2/q2LFjkqSqVauqdOnSDi0OAADgdt3WhwrGx8crPj5e1apVU+nSpW/piz0BAAAKU77Czvnz59W2bVtVr15dnTt3Vnx8vCRpwIAB3HYOAACKlXyFnWHDhqlUqVKKi4uTh4eHbX2vXr20evVqhxUHAABwu/I1Z2ft2rVas2aNKlSoYLe+WrVq+vXXXx1SGAAAgCPk68xOSkqK3RmdLH/88YesVuttFwUAAOAo+Qo7zZs318KFC23LFotFmZmZmj59ulq3bu2w4gAAAG5Xvi5jTZ8+XW3bttWuXbt09epVvfTSSzpw4ID++OMPbd261dE1AgAA5Fu+zuzUrVtXhw8f1n333adu3bopJSVFPXr00J49e1S1alVH1wgAAJBveT6zk5aWpo4dO2rOnDl69dVXC6ImAAAAh8nzmZ1SpUpp3759BVELAACAw+XrMtaTTz6puXPnOroWAAAAh8vXBOX09HR99NFHWrdunRo3bpztO7FmzpzpkOIAAABuV57CzvHjx1WpUiX973//U6NGjSRJhw8ftmtjsVgcVx0AAMBtylPYqVatmuLj4/Xdd99Juvb1EO+++64CAgIKpDgAAIDblac5O9d/q/m3336rlJQUhxYEAADgSPmaoJzl+vADAABQ3OQp7FgslmxzcpijAwAAirM8zdkxDEP9+vWzfdnnlStXNHjw4Gx3Yy1btsxxFQIAANyGPIWdiIgIu+Unn3zSocUAAAA4Wp7Czrx58wqqDgAAgAJxWxOUAQAAijvCDgAAMDXCDgAAMLUiDTubN29W165dFRwcLIvFohUrVtht79evn+1296xHx44d7dr88ccfeuKJJ+Tt7S1fX18NGDBAycnJhXgUAACgOCvSsJOSkqIGDRooOjo61zYdO3ZUfHy87fHpp5/abX/iiSd04MABxcTEaOXKldq8ebMGDRpU0KUDAIASIl/feu4onTp1UqdOnW7Yxmq1KjAwMMdthw4d0urVq/Xjjz+qSZMmkqT33ntPnTt31ltvvaXg4GCH1wwAAEqWYj9nZ+PGjfL391eNGjX07LPP6vz587Zt27dvl6+vry3oSFK7du3k5OSknTt35tpnamqqkpKS7B4AAMCcinXY6dixoxYuXKj169frjTfe0KZNm9SpUydlZGRIkhISEuTv72/3HBcXF/n5+SkhISHXfqOiouTj42N7hISEFOhxAACAolOkl7Fu5rHHHrP9u169eqpfv76qVq2qjRs3qm3btvnud8yYMRo+fLhtOSkpicADAIBJFeszO9erUqWKypUrp6NHj0qSAgMDdebMGbs26enp+uOPP3Kd5yNdmwfk7e1t9wAAAOZUosLOb7/9pvPnzysoKEiSFB4ergsXLmj37t22Nhs2bFBmZqaaNm1aVGUCAIBipEgvYyUnJ9vO0kjSiRMntHfvXvn5+cnPz08TJ05Uz549FRgYqGPHjumll15SWFiYOnToIEmqVauWOnbsqIEDB2rOnDlKS0vT0KFD9dhjj3EnFgAAkFTEZ3Z27dqlO++8U3feeackafjw4brzzjs1btw4OTs7a9++fXrwwQdVvXp1DRgwQI0bN9b3338vq9Vq62PRokWqWbOm2rZtq86dO+u+++7Thx9+WFSHBAAAipkiPbPTqlUrGYaR6/Y1a9bctA8/Pz8tXrzYkWUBAAATKVFzdgAAAPKKsAMAAEyNsAMAAEyNsAMAAEyNsAMAAEyNsAMAAEyNsAMAAEyNsAMAAEyNsAMAAEyNsAMAAEyNsAMAAEyNsAMAAEyNsAMAAEyNsAMAAEyNsAMAAEyNsAMAAEyNsAMAAEyNsAMAAEyNsAMAAEyNsAMAAEyNsAMAAEyNsAMAAEyNsAMAAEyNsAMAAEyNsAMAAEyNsAMAAEyNsAMAAEyNsAMAAEyNsAMAAEyNsAMAAEyNsAMAAEyNsAMAAEyNsAMAAEyNsAMAAEyNsAMAAEyNsAMAAEytSMPO5s2b1bVrVwUHB8tisWjFihV22w3D0Lhx4xQUFCR3d3e1a9dOR44csWvzxx9/6IknnpC3t7d8fX01YMAAJScnF+JRAACA4qxIw05KSooaNGig6OjoHLdPnz5d7777rubMmaOdO3eqdOnS6tChg65cuWJr88QTT+jAgQOKiYnRypUrtXnzZg0aNKiwDgEAABRzLkW5806dOqlTp045bjMMQ7NmzdLYsWPVrVs3SdLChQsVEBCgFStW6LHHHtOhQ4e0evVq/fjjj2rSpIkk6b333lPnzp311ltvKTg4uNCOBQAAFE/Fds7OiRMnlJCQoHbt2tnW+fj4qGnTptq+fbskafv27fL19bUFHUlq166dnJyctHPnzlz7Tk1NVVJSkt0DAACYU7ENOwkJCZKkgIAAu/UBAQG2bQkJCfL397fb7uLiIj8/P1ubnERFRcnHx8f2CAkJcXD1AACguCi2YacgjRkzRhcvXrQ9Tp06VdQlAQCAAlJsw05gYKAkKTEx0W59YmKibVtgYKDOnDljtz09PV1//PGHrU1OrFarvL297R4AAMCcim3YqVy5sgIDA7V+/XrbuqSkJO3cuVPh4eGSpPDwcF24cEG7d++2tdmwYYMyMzPVtGnTQq8ZAAAUP0V6N1ZycrKOHj1qWz5x4oT27t0rPz8/VaxYUS+++KJef/11VatWTZUrV9Zrr72m4OBgde/eXZJUq1YtdezYUQMHDtScOXOUlpamoUOH6rHHHuNOLAAAIKmIw86uXbvUunVr2/Lw4cMlSREREZo/f75eeuklpaSkaNCgQbpw4YLuu+8+rV69Wm5ubrbnLFq0SEOHDlXbtm3l5OSknj176t133y30YwEAAMVTkYadVq1ayTCMXLdbLBZNmjRJkyZNyrWNn5+fFi9eXBDlAQAAEyi2c3YAAAAcgbADAABMjbADAABMjbADAABMjbADAABMjbADAABMjbADAABMjbADAABMjbADAABMjbADAABMjbADAABMjbADAABMjbADAABMjbADAABMjbADAABMjbADAABMjbADAABMjbADAABMjbADAABMjbADAABMjbADAABMjbADAABMjbADAABMjbADAABMjbADAABMjbADAABMjbADAABMjbADAABMjbADAABMjbADAABMjbADAABMjbADAABMjbADAABMjbADAABMjbADAABMrViHnQkTJshisdg9atasadt+5coVRUZGqmzZsvL09FTPnj2VmJhYhBUDAIDipliHHUmqU6eO4uPjbY8tW7bYtg0bNkxff/21li5dqk2bNun06dPq0aNHEVYLAACKG5eiLuBmXFxcFBgYmG39xYsXNXfuXC1evFht2rSRJM2bN0+1atXSjh07dM899xR2qQAAoBgq9md2jhw5ouDgYFWpUkVPPPGE4uLiJEm7d+9WWlqa2rVrZ2tbs2ZNVaxYUdu3b79hn6mpqUpKSrJ7AAAAcyrWYadp06aaP3++Vq9erdmzZ+vEiRNq3ry5Ll26pISEBLm6usrX19fuOQEBAUpISLhhv1FRUfLx8bE9QkJCCvAoAABAUSrWl7E6depk+3f9+vXVtGlThYaG6vPPP5e7u3u++x0zZoyGDx9uW05KSiLwAABgUsX6zM71fH19Vb16dR09elSBgYG6evWqLly4YNcmMTExxzk+f2e1WuXt7W33AAAA5lSiwk5ycrKOHTumoKAgNW7cWKVKldL69ett22NjYxUXF6fw8PAirBIAABQnxfoy1siRI9W1a1eFhobq9OnTGj9+vJydnfX444/Lx8dHAwYM0PDhw+Xn5ydvb28999xzCg8P504sAABgU6zDzm+//abHH39c58+fV/ny5XXfffdpx44dKl++vCTp7bfflpOTk3r27KnU1FR16NBB77//fhFXDQAAipNiHXaWLFlyw+1ubm6Kjo5WdHR0IVUEAABKmhI1ZwcAACCvCDsAAMDUCDsAAMDUCDsAAMDUCDsAAMDUCDsAAMDUCDsAAMDUCDsAAMDUCDsAAMDUCDsAAMDUCDsAAMDUCDsAAMDUCDsAAMDUCDsAAMDUCDsAAMDUCDsAAMDUCDsAAMDUCDsAAMDUCDsAAMDUCDsAAMDUCDsAAMDUCDsAAMDUCDsAAMDUCDsAAMDUCDsAAMDUCDsAAMDUCDsAAMDUCDsAAMDUCDsAAMDUCDsAAMDUCDsAAMDUCDsAAMDUCDsAAMDUCDsAAMDUCDsAAMDUTBN2oqOjValSJbm5ualp06b64YcfirokAABQDJgi7Hz22WcaPny4xo8fr59++kkNGjRQhw4ddObMmaIuDQAAFDFThJ2ZM2dq4MCB6t+/v2rXrq05c+bIw8NDH330UVGXBgAAiliJDztXr17V7t271a5dO9s6JycntWvXTtu3by/CygAAQHHgUtQF3K5z584pIyNDAQEBdusDAgL0yy+/5Pic1NRUpaam2pYvXrwoSUpKSnJobcnJydf2l3BUmVevOLTvtD9+kyTt3r3bth9HcXJyUmZmpkP7LOi+qbnk903NhdM3NRdO3yWx5tjYWEmOf8/Ker9KTk52+PtsVn+GYdywXYkPO/kRFRWliRMnZlsfEhJSIPv7c82/CqRfSRo0aFCB9Q0A+OcpqPesli1bFki/knTp0iX5+Pjkur3Eh51y5crJ2dlZiYmJdusTExMVGBiY43PGjBmj4cOH25YzMzP1xx9/qGzZsrJYLA6rLSkpSSEhITp16pS8vb0d1i/sMc6Fh7EuHIxz4WCcC0dBjrNhGLp06ZKCg4Nv2K7Ehx1XV1c1btxY69evV/fu3SVdCy/r16/X0KFDc3yO1WqV1Wq1W+fr61tgNXp7e/M/UiFgnAsPY104GOfCwTgXjoIa5xud0clS4sOOJA0fPlwRERFq0qSJ7r77bs2aNUspKSnq379/UZcGAACKmCnCTq9evXT27FmNGzdOCQkJatiwoVavXp1t0jIAAPjnMUXYkaShQ4fmetmqqFitVo0fPz7bJTM4FuNceBjrwsE4Fw7GuXAUh3G2GDe7XwsAAKAEK/EfKggAAHAjhB0AAGBqhB0AAGBqhB0AAGBqhJ3bFB0drUqVKsnNzU1NmzbVDz/8cMP2S5cuVc2aNeXm5qZ69epp1apVhVRpyZaXcf73v/+t5s2bq0yZMipTpozatWt309cF1+T15znLkiVLZLFYbB/siZvL61hfuHBBkZGRCgoKktVqVfXq1fn9cQvyOs6zZs1SjRo15O7urpCQEA0bNkxXrjj2uw3NZvPmzeratauCg4NlsVi0YsWKmz5n48aNatSokaxWq8LCwjR//vyCLdJAvi1ZssRwdXU1PvroI+PAgQPGwIEDDV9fXyMxMTHH9lu3bjWcnZ2N6dOnGwcPHjTGjh1rlCpVyti/f38hV16y5HWce/fubURHRxt79uwxDh06ZPTr18/w8fExfvvtt0KuvGTJ6zhnOXHihHHHHXcYzZs3N7p161Y4xZZweR3r1NRUo0mTJkbnzp2NLVu2GCdOnDA2btxo7N27t5ArL1nyOs6LFi0yrFarsWjRIuPEiRPGmjVrjKCgIGPYsGGFXHnJsmrVKuPVV181li1bZkgyli9ffsP2x48fNzw8PIzhw4cbBw8eNN577z3D2dnZWL16dYHVSNi5DXfffbcRGRlpW87IyDCCg4ONqKioHNs/+uijRpcuXezWNW3a1HjmmWcKtM6SLq/jfL309HTDy8vLWLBgQUGVaAr5Gef09HTj3nvvNf7zn/8YERERhJ1blNexnj17tlGlShXj6tWrhVWiKeR1nCMjI402bdrYrRs+fLjRrFmzAq3TTG4l7Lz00ktGnTp17Nb16tXL6NChQ4HVxWWsfLp69ap2796tdu3a2dY5OTmpXbt22r59e47P2b59u117SerQoUOu7ZG/cb7e5cuXlZaWJj8/v4Iqs8TL7zhPmjRJ/v7+GjBgQGGUaQr5Gev//ve/Cg8PV2RkpAICAlS3bl1NnTpVGRkZhVV2iZOfcb733nu1e/du26Wu48ePa9WqVercuXOh1PxPURTvhab5BOXCdu7cOWVkZGT7SoqAgAD98ssvOT4nISEhx/YJCQkFVmdJl59xvt7o0aMVHByc7X8u/L/8jPOWLVs0d+5c7d27txAqNI/8jPXx48e1YcMGPfHEE1q1apWOHj2qIUOGKC0tTePHjy+Mskuc/Ixz7969de7cOd13330yDEPp6ekaPHiwXnnllcIo+R8jt/fCpKQk/fXXX3J3d3f4PjmzA1ObNm2alixZouXLl8vNza2oyzGNS5cuqU+fPvr3v/+tcuXKFXU5ppeZmSl/f399+OGHaty4sXr16qVXX31Vc+bMKerSTGXjxo2aOnWq3n//ff30009atmyZvvnmG02ePLmoS8Nt4sxOPpUrV07Ozs5KTEy0W5+YmKjAwMAcnxMYGJin9sjfOGd56623NG3aNK1bt07169cvyDJLvLyO87Fjx3Ty5El17drVti4zM1OS5OLiotjYWFWtWrVgiy6h8vMzHRQUpFKlSsnZ2dm2rlatWkpISNDVq1fl6upaoDWXRPkZ59dee019+vTR008/LUmqV6+eUlJSNGjQIL366qtycuL8gCPk9l7o7e1dIGd1JM7s5Jurq6saN26s9evX29ZlZmZq/fr1Cg8Pz/E54eHhdu0lKSYmJtf2yN84S9L06dM1efJkrV69Wk2aNCmMUku0vI5zzZo1tX//fu3du9f2ePDBB9W6dWvt3btXISEhhVl+iZKfn+lmzZrp6NGjtkApSYcPH1ZQUBBBJxf5GefLly9nCzRZAdPgayQdpkjeCwts6vM/wJIlSwyr1WrMnz/fOHjwoDFo0CDD19fXSEhIMAzDMPr06WO8/PLLtvZbt241XFxcjLfeess4dOiQMX78eG49vwV5Hedp06YZrq6uxhdffGHEx8fbHpcuXSqqQygR8jrO1+NurFuX17GOi4szvLy8jKFDhxqxsbHGypUrDX9/f+P1118vqkMoEfI6zuPHjze8vLyMTz/91Dh+/Lixdu1ao2rVqsajjz5aVIdQIly6dMnYs2ePsWfPHkOSMXPmTGPPnj3Gr7/+ahiGYbz88stGnz59bO2zbj0fNWqUcejQISM6Oppbz4u79957z6hYsaLh6upq3H333caOHTts21q2bGlERETYtf/888+N6tWrG66urkadOnWMb775ppArLpnyMs6hoaGGpGyP8ePHF37hJUxef57/jrCTN3kd623bthlNmzY1rFarUaVKFWPKlClGenp6IVdd8uRlnNPS0owJEyYYVatWNdzc3IyQkBBjyJAhxp9//ln4hZcg3333XY6/c7PGNiIiwmjZsmW25zRs2NBwdXU1qlSpYsybN69Aa7QYBufmAACAeTFnBwAAmBphBwAAmBphBwAAmBphBwAAmBphBwAAmBphBwAAmBphBwAAmBphB0Cu+vXrp+7du9uWW7VqpRdffLHQ69i4caMsFosuXLhQ6PuuVKmSZs2adVt9zJ8/X76+vjdsM2HCBDVs2NC2nJ+xv5X9AP9EhB2ghOnXr58sFossFotcXV0VFhamSZMmKT09vcD3vWzZslv+BujCDiiVKlWyjUvp0qXVqFEjLV26tFD27QgjR47M9n1Bf3f92OcUwnr16qXDhw8XVIlAiUXYAUqgjh07Kj4+XkeOHNGIESM0YcIEvfnmmzm2vXr1qsP26+fnJy8vL4f152iTJk1SfHy89uzZo7vuuku9evXStm3bcmzryHFxBE9PT5UtWzbX7bcy9u7u7vL393d0aUCJR9gBSiCr1arAwECFhobq2WefVbt27fTf//5X0v9f/pgyZYqCg4NVo0YNSdKpU6f06KOPytfXV35+furWrZtOnjxp6zMjI0PDhw+Xr6+vypYtq5deeinbNz1ffyklNTVVo0ePVkhIiKxWq8LCwjR37lydPHlSrVu3liSVKVNGFotF/fr1k3Ttm6ejoqJUuXJlubu7q0GDBvriiy/s9rNq1SpVr15d7u7uat26tV2dN+Ll5aXAwEBVr15d0dHRcnd319dffy3p2pmQyZMnq2/fvvL29tagQYMkSV9++aXq1Kkjq9WqSpUqacaMGdn6vXTpkh5//HGVLl1ad9xxh6Kjo+22z5w5U/Xq1VPp0qUVEhKiIUOGKDk5OVs/K1asULVq1eTm5qYOHTro1KlTtm3XX8a63t/HvlWrVvr11181bNgw29ksKefLWF999ZUaNWokNzc3ValSRRMnTrSdBTQMQxMmTFDFihVltVoVHBys559//saDDJRAhB3ABNzd3e3OVKxfv16xsbGKiYnRypUrlZaWpg4dOsjLy0vff/+9tm7dKk9PT3Xs2NH2vBkzZmj+/Pn66KOPtGXLFv3xxx9avnz5Dffbt29fffrpp3r33Xd16NAhffDBB/L09FRISIi+/PJLSVJsbKzi4+P1zjvvSJKioqK0cOFCzZkzRwcOHNCwYcP05JNPatOmTZKuhbIePXqoa9eu2rt3r55++mm9/PLLeR4TFxcXlSpVym5c3nrrLTVo0EB79uzRa6+9pt27d+vRRx/VY489pv3792vChAl67bXXNH/+fLu+3nzzTdvzXn75Zb3wwguKiYmxbXdyctK7776rAwcOaMGCBdqwYYNeeukluz4uX76sKVOmaOHChdq6dasuXLigxx57LM/HJV27pFWhQgXbmaz4+Pgc233//ffq27evXnjhBR08eFAffPCB5s+frylTpki6FvTefvttffDBBzpy5IhWrFihevXq5asmoFgr0K8ZBeBwf/928czMTCMmJsawWq3GyJEjbdsDAgKM1NRU23M+/vhjo0aNGkZmZqZtXWpqquHu7m6sWbPGMAzDCAoKMqZPn27bnpaWZlSoUMHum8xbtmxpvPDCC4ZhGEZsbKwhyYiJicmxzqxvQv77N0ZfuXLF8PDwMLZt22bXdsCAAcbjjz9uGIZhjBkzxqhdu7bd9tGjR2fr63qhoaHG22+/bTu2qVOnGpKMlStX2rZ3797d7jm9e/c22rdvb7du1KhRdvsPDQ01OnbsaNemV69eRqdOnXKtZenSpUbZsmVty/PmzTMk2X3j9qFDhwxJxs6dOw3DMIzx48cbDRo0sG2//lvk/z721x/v3/fj4+NjW27btq0xdepUuzYff/yxERQUZBiGYcyYMcOoXr26cfXq1VyPBTADzuwAJdDKlSvl6ekpNzc3derUSb169dKECRNs2+vVqydXV1fb8s8//6yjR4/Ky8tLnp6e8vT0lJ+fn65cuaJjx47p4sWLio+PV9OmTW3PcXFxUZMmTXKtYe/evXJ2dlbLli1vue6jR4/q8uXLat++va0OT09PLVy4UMeOHZMkHTp0yK4OSQoPD7+l/kePHi1PT095eHjojTfe0LRp09SlSxfb9uuP59ChQ2rWrJndumbNmunIkSPKyMjIdf/h4eE6dOiQbXndunVq27at7rjjDnl5ealPnz46f/68Ll++bGvj4uKiu+66y7Zcs2ZN+fr62vXjaD///LMmTZpkN9YDBw5UfHy8Ll++rEceeUR//fWXqlSpooEDB2r58uWFMtEdKGwuRV0AgLxr3bq1Zs+eLVdXVwUHB8vFxf5/5dKlS9stJycnq3Hjxlq0aFG2vsqXL5+vGtzd3fP8nKx5LN98843uuOMOu21WqzVfdfzdqFGj1K9fP3l6eiogIMA2lyXL9ePiCCdPntQDDzygZ599VlOmTJGfn5+2bNmiAQMG6OrVq/Lw8HD4Pm9VcnKyJk6cqB49emTb5ubmppCQEMXGxmrdunWKiYnRkCFD9Oabb2rTpk0qVapUEVQMFAzCDlAClS5dWmFhYbfcvlGjRvrss8/k7+8vb2/vHNsEBQVp586datGihSQpPT1du3fvVqNGjXJsX69ePWVmZmrTpk1q165dtu1ZZ5b+foakdu3aslqtiouLy/WMUK1atWyTrbPs2LHj5gcpqVy5cnkal1q1amnr1q1267Zu3arq1avL2dk51/3v2LFDtWrVkiTt3r1bmZmZmjFjhpycrp0s//zzz7PtKz09Xbt27dLdd98t6dpcpgsXLtj6yStXV1e7sc1Jo0aNFBsbe8MxcXd3V9euXdW1a1dFRkaqZs2a2r9/f66vO1AScRkL+Ad44oknVK5cOXXr1k3ff/+9Tpw4oY0bN+r555/Xb7/9Jkl64YUXNG3aNK1YsUK//PKLhgwZcsPPyKlUqZIiIiL01FNPacWKFbY+s97oQ0NDZbFYtHLlSp09e1bJycny8vLSyJEjNWzYMC1YsEDHjh3TTz/9pPfee08LFiyQJA0ePFhHjhzRqFGjFBsbq8WLF2ebMOwoI0aM0Pr16zV58mQdPnxYCxYs0L/+9S+NHDnSrt3WrVs1ffp0HT58WNHR0Vq6dKleeOEFSVJYWJjS0tL03nvv6fjx4/r44481Z86cbPsqVaqUnnvuOe3cuVO7d+9Wv379dM8999jCT15VqlRJmzdv1u+//65z587l2GbcuHFauHChJk6cqAMHDujQoUNasmSJxo4dK+na3Vtz587V//73Px0/flyffPKJ3N3dFRoamq+agOKKsAP8A3h4eGjz5s2qWLGievTooVq1amnAgAG6cuWK7UzPiBEj1KdPH0VERCg8PFxeXl566KGHbtjv7Nmz9fDDD2vIkCGqWbOmBg4cqJSUFEnSHXfcoYkTJ+rll19WQECAhg4dKkmaPHmyXnvtNUVFRalWrVrq2LGjvvnmG1WuXFmSVLFiRX355ZdasWKFGjRooDlz5mjq1KkFMi6NGjXS559/riVLlqhu3boaN26cJk2aZLtNPsuIESO0a9cu3XnnnXr99dc1c+ZMdejQQZLUoEEDzZw5U2+88Ybq1q2rRYsWKSoqKtu+PDw8NHr0aPXu3VvNmjWTp6enPvvss3zXPmnSJJ08eVJVq1bN9VJkhw4dtHLlSq1du1Z33XWX7rnnHr399tu2MOPr66t///vfatasmerXr69169bp66+/vuHn/QAlkcUwrvsgDQAAABPhzA4AADA1wg4AADA1wg4AADA1wg4AADA1wg4AADA1wg4AADA1wg4AADA1wg4AADA1wg4AADA1wg4AADA1wg4AADA1wg4AADC1/wMqfURvKH3aswAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(y_pred_binary, bins=20, edgecolor='k')\n",
    "plt.xlabel('Predicted Probabilities')\n",
    "plt.ylabel('Frequency')\n",
    "plt.title('Histogram of Predicted Probabilities')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import roc_curve, auc\n",
    "\n",
    "fpr, tpr, thresholds = roc_curve(y_test, y_pred)\n",
    "roc_auc = auc(fpr, tpr)\n",
    "\n",
    "plt.figure(figsize=(8, 6))\n",
    "plt.plot(fpr, tpr, color='darkorange', lw=2, label='ROC curve (area = {:.2f})'.format(roc_auc))\n",
    "plt.plot([0, 1], [0, 1], color='navy', lw=2, linestyle='--')\n",
    "plt.xlim([0.0, 1.0])\n",
    "plt.ylim([0.0, 1.05])\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.title('Receiver Operating Characteristic')\n",
    "plt.legend(loc='lower right')\n",
    "\n",
    "optimal_threshold_index = np.argmax(tpr - fpr)\n",
    "optimal_threshold = thresholds[optimal_threshold_index]\n",
    "\n",
    "plt.scatter(fpr[optimal_threshold_index], tpr[optimal_threshold_index], c='red', marker='o', label='Optimal Threshold')\n",
    "plt.legend()\n",
    "\n",
    "plt.show()\n",
    "\n",
    "y_pred_binary = (y_pred >= optimal_threshold).astype(int)\n"
   ]
  }
 ],
 "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": "3.11.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}