cnn_model.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-03-20 21:37:27.453694: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  SSE4.1 SSE4.2 AVX AVX2 AVX512F AVX512_VNNI FMA\n",
      "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
      "2023-03-20 21:37:27.496240: I tensorflow/core/util/port.cc:104] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.\n"
     ]
    }
   ],
   "source": [
    "# Import necessary packages\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.model_selection import train_test_split, cross_val_score\n",
    "from sklearn.metrics import roc_curve, auc, roc_auc_score, precision_recall_curve, accuracy_score, precision_score, f1_score, ConfusionMatrixDisplay, confusion_matrix\n",
    "from scipy.stats import ttest_rel, ttest_ind\n",
    "from scipy import stats\n",
    "from keras.layers import Input, GRU, Conv1D, MaxPooling1D, AveragePooling1D, Lambda, concatenate, Dense, Dropout\n",
    "from keras.models import Model, load_model\n",
    "from keras import backend as K\n",
    "import tensorflow as tf\n",
    "import cms_preprocess"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/shared/orx/cms_preprocess.py:45: DtypeWarning: Columns (1,3,21) have mixed types. Specify dtype option on import or set low_memory=False.\n",
      "  ben = pd.read_csv(ben_path, usecols=ben_cols)\n",
      "/shared/orx/cms_preprocess.py:46: DtypeWarning: Columns (1,3,30) have mixed types. Specify dtype option on import or set low_memory=False.\n",
      "  ip = pd.read_csv(ip_path, usecols=ip_cols)\n",
      "/shared/orx/cms_preprocess.py:47: DtypeWarning: Columns (1) have mixed types. Specify dtype option on import or set low_memory=False.\n",
      "  pde = pd.read_csv(pde_path, usecols=pde_cols)\n"
     ]
    }
   ],
   "source": [
    "# Split the data into train and test sets\n",
    "x_train, x_val, y_train, y_val, dv_train, dv_val, test_input, test_dv, test_target = cms_preprocess.get_mhe(cms_preprocess.ben_path,\n",
    "  cms_preprocess.ip_path, cms_preprocess.pde_path, cms_preprocess.dx_path, cms_preprocess.pcs_path, cms_preprocess.ben_cols,\n",
    "  cms_preprocess.ip_cols, cms_preprocess.pde_cols, rows=10000, start_year=2008, end_year=2010, random_state = 42, col_num=6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Using MirroredStrategy with devices ('/job:localhost/replica:0/task:0/device:GPU:0',)\n",
      "INFO:tensorflow:Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).\n",
      "INFO:tensorflow:Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).\n",
      "INFO:tensorflow:Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).\n",
      "INFO:tensorflow:Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-03-20 21:38:14.638867: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
      "2023-03-20 21:38:14.654222: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
      "2023-03-20 21:38:14.654354: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
      "2023-03-20 21:38:14.655427: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  SSE4.1 SSE4.2 AVX AVX2 AVX512F AVX512_VNNI FMA\n",
      "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
      "2023-03-20 21:38:14.655814: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
      "2023-03-20 21:38:14.655917: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
      "2023-03-20 21:38:14.655994: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
      "2023-03-20 21:38:14.975873: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
      "2023-03-20 21:38:14.976033: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
      "2023-03-20 21:38:14.976125: I tensorflow/compiler/xla/stream_executor/cuda/cuda_gpu_executor.cc:981] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero\n",
      "2023-03-20 21:38:14.976193: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1613] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 10205 MB memory:  -> device: 0, name: NVIDIA GeForce RTX 3060, pci bus id: 0000:01:00.0, compute capability: 8.6\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n",
      "INFO:tensorflow:Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).\n",
      "INFO:tensorflow:Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).\n",
      "INFO:tensorflow:Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).\n",
      "INFO:tensorflow:Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).\n",
      "INFO:tensorflow:Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).\n",
      "INFO:tensorflow:Reduce to /job:localhost/replica:0/task:0/device:CPU:0 then broadcast to ('/job:localhost/replica:0/task:0/device:CPU:0',).\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-03-20 21:38:20.053535: E tensorflow/core/grappler/optimizers/meta_optimizer.cc:954] layout failed: INVALID_ARGUMENT: Size of values 0 does not match size of permutation 4 @ fanin shape inmodel/dropout/dropout/SelectV2-2-TransposeNHWCToNCHW-LayoutOptimizer\n",
      "2023-03-20 21:38:20.709894: I tensorflow/compiler/xla/stream_executor/cuda/cuda_dnn.cc:428] Loaded cuDNN version 8401\n",
      "2023-03-20 21:38:21.481241: I tensorflow/tsl/platform/default/subprocess.cc:304] Start cannot spawn child process: No such file or directory\n",
      "2023-03-20 21:38:21.956032: I tensorflow/compiler/xla/stream_executor/cuda/cuda_blas.cc:630] TensorFloat-32 will be used for the matrix multiplication. This will only be logged once.\n",
      "2023-03-20 21:38:21.956415: I tensorflow/compiler/xla/service/service.cc:173] XLA service 0x7f10cd6d2910 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n",
      "2023-03-20 21:38:21.956426: I tensorflow/compiler/xla/service/service.cc:181]   StreamExecutor device (0): NVIDIA GeForce RTX 3060, Compute Capability 8.6\n",
      "2023-03-20 21:38:21.959135: I tensorflow/compiler/mlir/tensorflow/utils/dump_mlir_util.cc:268] disabling MLIR crash reproducer, set env var `MLIR_CRASH_REPRODUCER_DIRECTORY` to enable.\n",
      "2023-03-20 21:38:21.996840: I tensorflow/tsl/platform/default/subprocess.cc:304] Start cannot spawn child process: No such file or directory\n",
      "2023-03-20 21:38:22.020807: I tensorflow/compiler/jit/xla_compilation_cache.cc:477] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "200/200 [==============================] - 10s 31ms/step - loss: 0.0670 - accuracy: 0.9936 - val_loss: 0.0462 - val_accuracy: 0.9937\n",
      "Epoch 2/10\n",
      "200/200 [==============================] - 5s 27ms/step - loss: 0.0423 - accuracy: 0.9936 - val_loss: 0.0380 - val_accuracy: 0.9937\n",
      "Epoch 3/10\n",
      "200/200 [==============================] - 5s 26ms/step - loss: 0.0329 - accuracy: 0.9936 - val_loss: 0.0310 - val_accuracy: 0.9937\n",
      "Epoch 4/10\n",
      "200/200 [==============================] - 5s 26ms/step - loss: 0.0184 - accuracy: 0.9944 - val_loss: 0.0246 - val_accuracy: 0.9937\n",
      "Epoch 5/10\n",
      "200/200 [==============================] - 5s 26ms/step - loss: 0.0097 - accuracy: 0.9962 - val_loss: 0.0210 - val_accuracy: 0.9956\n",
      "Epoch 6/10\n",
      "200/200 [==============================] - 5s 27ms/step - loss: 0.0051 - accuracy: 0.9986 - val_loss: 0.0205 - val_accuracy: 0.9969\n",
      "Epoch 7/10\n",
      "200/200 [==============================] - 5s 26ms/step - loss: 0.0027 - accuracy: 0.9994 - val_loss: 0.0206 - val_accuracy: 0.9969\n",
      "Epoch 8/10\n",
      "200/200 [==============================] - 5s 26ms/step - loss: 0.0024 - accuracy: 0.9998 - val_loss: 0.0212 - val_accuracy: 0.9975\n",
      "Epoch 9/10\n",
      "200/200 [==============================] - 5s 26ms/step - loss: 0.0013 - accuracy: 0.9997 - val_loss: 0.0219 - val_accuracy: 0.9962\n",
      "Epoch 10/10\n",
      "200/200 [==============================] - 5s 26ms/step - loss: 0.0012 - accuracy: 0.9997 - val_loss: 0.0222 - val_accuracy: 0.9962\n"
     ]
    }
   ],
   "source": [
    "batch_size = 32\n",
    "epochs=10\n",
    "\n",
    "strategy = tf.distribute.MirroredStrategy()\n",
    "with strategy.scope():\n",
    "    # Define the CNN model\n",
    "    data_input = tf.keras.layers.Input(shape=(366, 516))  # input shape for non-demographic variables data\n",
    "    dv_input = tf.keras.layers.Input(shape=(2,))  # input shape for demographic variables\n",
    "    x = tf.keras.layers.Reshape((366, 516, 1))(data_input)  # reshape the input to a 4D tensor\n",
    "    x = tf.keras.layers.Conv2D(100, (366, 516), activation='relu')(x)  # convolutional layer with 100 filters\n",
    "    x = tf.keras.layers.MaxPooling2D((1, 1))(x)  # max pooling layer\n",
    "    x = tf.keras.layers.Dropout(0.5)(x)  # dropout layer to prevent overfitting\n",
    "    x = tf.keras.layers.Flatten()(x)  # flatten the output from the convolutional layer\n",
    "    concatenated = tf.keras.layers.concatenate([x, dv_input])  # concatenate the demographic variables with the flattened output\n",
    "    output = tf.keras.layers.Dense(1, activation='sigmoid')(concatenated)  # dense, fully connected layer with a sigmoid activation\n",
    "    model = tf.keras.Model(inputs=[data_input, dv_input], outputs=output)  # define the model with inputs and outputs\n",
    "\n",
    "    # Compile the model with both the loss and accuracy metrics\n",
    "    model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])\n",
    "    \n",
    "    # Train the model\n",
    "    history = model.fit((x_train, dv_train), y_train, epochs=epochs, batch_size=batch_size, validation_data=((x_val, dv_val), y_val))\n",
    "    \n",
    "    # Plot the loss and accuracy\n",
    "    # fig, ax = plt.subplots(figsize=(8, 6))\n",
    "    # ax.plot(history.history['loss'], label='Training Loss')\n",
    "    # ax.plot(history.history['accuracy'], label='Training Accuracy')\n",
    "    # ax.plot(history.history['val_loss'], label='Validation Loss')\n",
    "    # ax.plot(history.history['val_accuracy'], label='Validation Accuracy')\n",
    "    # ax.set_title('Model Loss and Accuracy', fontsize=16)\n",
    "    # ax.set_xlabel('Epoch', fontsize=14)\n",
    "    # ax.set_ylabel('Loss / Accuracy', fontsize=14)\n",
    "    # ax.tick_params(axis='both', which='major', labelsize=12)\n",
    "    # ax.legend(fontsize=12)\n",
    "    # plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the loss and accuracy\n",
    "fig, ax = plt.subplots(figsize=(8, 6))\n",
    "ax.plot(history.history['loss'], label='Training Loss')\n",
    "ax.plot(history.history['val_loss'], label='Validation Loss')\n",
    "ax.set_title('Model Loss', fontsize=16)\n",
    "ax.set_xlabel('Epoch', fontsize=14)\n",
    "ax.set_ylabel('Loss', fontsize=14)\n",
    "ax.tick_params(axis='both', which='major', labelsize=12)\n",
    "ax.legend(fontsize=12)\n",
    "plt.show()\n",
    "\n",
    "# Plot the loss and accuracy\n",
    "fig, ax = plt.subplots(figsize=(8, 6))\n",
    "ax.plot(history.history['accuracy'], label='Training Accuracy')\n",
    "ax.plot(history.history['val_accuracy'], label='Validation Accuracy')\n",
    "ax.set_title('Model Accuracy', fontsize=16)\n",
    "ax.set_xlabel('Epoch', fontsize=14)\n",
    "ax.set_ylabel('Loss Accuracy', fontsize=14)\n",
    "ax.tick_params(axis='both', which='major', labelsize=12)\n",
    "ax.legend(fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "27/27 [==============================] - 2s 30ms/step\n",
      "27/27 [==============================] - 0s 15ms/step\n",
      "27/27 [==============================] - 0s 15ms/step\n",
      "27/27 [==============================] - 0s 14ms/step\n",
      "27/27 [==============================] - 0s 15ms/step\n",
      "27/27 [==============================] - 0s 14ms/step\n",
      "27/27 [==============================] - 0s 15ms/step\n",
      "27/27 [==============================] - 0s 15ms/step\n",
      "27/27 [==============================] - 0s 15ms/step\n",
      "27/27 [==============================] - 0s 14ms/step\n",
      "27/27 [==============================] - 0s 15ms/step\n",
      "27/27 [==============================] - 0s 15ms/step\n",
      "27/27 [==============================] - 0s 15ms/step\n",
      "27/27 [==============================] - 0s 14ms/step\n",
      "27/27 [==============================] - 0s 15ms/step\n",
      "27/27 [==============================] - 0s 14ms/step\n",
      "27/27 [==============================] - 0s 15ms/step\n",
      "27/27 [==============================] - 0s 15ms/step\n",
      "27/27 [==============================] - 0s 15ms/step\n",
      "27/27 [==============================] - 0s 15ms/step\n",
      "27/27 [==============================] - 0s 15ms/step\n",
      "27/27 [==============================] - 0s 14ms/step\n",
      "27/27 [==============================] - 0s 14ms/step\n",
      "27/27 [==============================] - 0s 15ms/step\n",
      "27/27 [==============================] - 0s 15ms/step\n",
      "27/27 [==============================] - 0s 15ms/step\n",
      "27/27 [==============================] - 0s 15ms/step\n",
      "27/27 [==============================] - 0s 14ms/step\n",
      "27/27 [==============================] - 0s 15ms/step\n",
      "27/27 [==============================] - 0s 14ms/step\n",
      "2/2 [==============================] - 0s 423ms/step\n",
      "Best threshold: 0.25\n"
     ]
    }
   ],
   "source": [
    "# Set batch size and number of chunks\n",
    "batch_size = 8\n",
    "num_chunks = 30\n",
    "\n",
    "# Split train data and demographic variables into chunks\n",
    "chunk_size = len(x_train) // num_chunks\n",
    "train_chunks = [x_train[i:i+chunk_size] for i in range(0, len(x_train), chunk_size)]\n",
    "dv_chunks = [dv_train[i:i+chunk_size] for i in range(0, len(dv_train), chunk_size)]\n",
    "\n",
    "# Get predictions for each chunk of data\n",
    "predictions = []\n",
    "for i, chunk in enumerate(train_chunks):\n",
    "    dv_chunk = dv_chunks[i]\n",
    "    chunk_predictions = model.predict([chunk, dv_chunk], batch_size=batch_size)\n",
    "    predictions.append(chunk_predictions)\n",
    "\n",
    "# Concatenate predictions from all chunks\n",
    "predictions = np.concatenate(predictions, axis=0)\n",
    "\n",
    "preds = np.copy(predictions)\n",
    "preds = np.squeeze(preds)\n",
    "\n",
    "# Find the F1 score for different threshold values\n",
    "thresholds = np.arange(0, 1, 0.01)\n",
    "f1_scores = [f1_score(y_train, (preds > t).astype(int), average='weighted') for t in thresholds]\n",
    "\n",
    "# Find the threshold value that maximizes the F1 score\n",
    "best_idx = np.argmax(f1_scores)\n",
    "best_threshold = thresholds[best_idx]\n",
    "\n",
    "print(\"Best threshold:\" ,best_threshold)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s 482ms/step\n",
      "Prediction: [[8.4267956e-01]\n",
      " [9.6426678e-01]\n",
      " [9.0767962e-01]\n",
      " [9.9208611e-01]\n",
      " [9.8921740e-01]\n",
      " [8.9103156e-01]\n",
      " [6.8990997e-04]\n",
      " [9.6824694e-01]\n",
      " [9.8921740e-01]\n",
      " [8.9103156e-01]\n",
      " [3.3621134e-03]\n",
      " [4.4138069e-06]\n",
      " [9.8384756e-01]]\n"
     ]
    }
   ],
   "source": [
    "# Get the prediction for some samples in the test set\n",
    "sample = test_input[np.where(test_target==1)[0]]\n",
    "dv_sample = test_dv[np.where(test_target==1)[0]]\n",
    "\n",
    "prediction = model.predict([np.array(sample), dv_sample])\n",
    "\n",
    "# Print the prediction\n",
    "print('Prediction:', prediction)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9/9 [==============================] - 0s 15ms/step\n",
      "9/9 [==============================] - 0s 14ms/step\n",
      "9/9 [==============================] - 0s 14ms/step\n",
      "9/9 [==============================] - 0s 15ms/step\n",
      "9/9 [==============================] - 0s 14ms/step\n",
      "9/9 [==============================] - 0s 14ms/step\n",
      "9/9 [==============================] - 0s 14ms/step\n",
      "9/9 [==============================] - 0s 15ms/step\n",
      "9/9 [==============================] - 0s 14ms/step\n",
      "9/9 [==============================] - 0s 15ms/step\n",
      "9/9 [==============================] - 0s 15ms/step\n",
      "9/9 [==============================] - 0s 15ms/step\n",
      "9/9 [==============================] - 0s 14ms/step\n",
      "9/9 [==============================] - 0s 14ms/step\n",
      "9/9 [==============================] - 0s 15ms/step\n",
      "9/9 [==============================] - 0s 15ms/step\n",
      "9/9 [==============================] - 0s 14ms/step\n",
      "9/9 [==============================] - 0s 14ms/step\n",
      "9/9 [==============================] - 0s 14ms/step\n",
      "9/9 [==============================] - 0s 14ms/step\n",
      "9/9 [==============================] - 0s 14ms/step\n",
      "9/9 [==============================] - 0s 14ms/step\n",
      "9/9 [==============================] - 0s 15ms/step\n",
      "9/9 [==============================] - 0s 14ms/step\n",
      "9/9 [==============================] - 0s 14ms/step\n",
      "9/9 [==============================] - 0s 14ms/step\n",
      "9/9 [==============================] - 0s 15ms/step\n",
      "9/9 [==============================] - 0s 14ms/step\n",
      "9/9 [==============================] - 0s 14ms/step\n",
      "9/9 [==============================] - 0s 15ms/step\n",
      "3/3 [==============================] - 0s 220ms/step\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AUC: 0.8831\n",
      "Precision with recall set to 0.9: 0.2745\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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UqVNo3bo15s+fj9q1a8POzg7jx49HTEyMynFGRkYiNTU1T/uZcayMjIyyfd3Y2BjXr1/H9evXcfDgQTRr1gzbt2/Hr7/+qrBcxnBrxrB5dl68eAEAKFmypNLrZMfIyAhCCCQkJOS4zLt37wAgzz/7nGT3882vz72ijDU3RUClSpXkBXzNmzdHamoq1q1bh127dqFHjx7y+oQZM2agW7du2W4jo/7Azs4Or1+/zvX9bG1tYWxsLK/XyO713AwePBgLFiyQ1/wcOHAAEydOVPg2b2tri+rVq+f47c/Z2VnhubLfvGxsbJCSkoJ3794pJDhCCISGhuKLL75QWD67WoiMtoyTtKrHI7tYd+zYAUNDQxw6dEjhJLJv3z6l9utzZRyXiIgIhQQnu/3PjjK/N8qKiorCoUOHMGvWLEyfPl3enpiYiIiIiGzXye6Y/vXXXxgwYAB++eUXhfawsDBYWVmpJfZixYpBX18f/fv3x9ixY7NdpnTp0p+MNTsuLi5Yv349AODx48fw9fXF7NmzkZSUhFWrVqkUp7W1NfT19fO0nxm/vzkdez09PfnnDwC0bNkS7u7umDNnDvr27StPVFq2bIk1a9Zg3759Cj/XzPbt2wcDAwN5AW/r1q0/uU52IiIiIJPJYGZmluMyGX//nzomRkZGWYrvgZwTjZx+vvnxuVeUseemCJo/fz6KFSuGmTNnIi0tDRUqVED58uVx+/ZteHh4ZPswNzcHkN59eubMGfkwVXY6dOiAZ8+ewcbGJtttZb6CIDuVKlVC3bp14e3tjW3btiExMRGDBw/O8h737t1D2bJls32PvP6Rf/XVVwDST3yZ7d69G7GxsfLXM9y/fx+3b99WaNu2bRvMzc1Ru3ZteayfczwAyCdhzPxBFx8fjy1btmRZViaTfXZPzseaNm0KAPDx8VFoV/aKk7Zt2+Lx48fyYb/PIZFIIITIUuC6bt06pKamqrSdj7dx+PBhvHnzRqFNmdiz610CABMTEzRv3hz+/v6oXr16tj//j3uq8sLNzQ3/+9//UK1aNYVJ65T9XTA2NkbTpk2xc+dOlb/9u7i4wNjYGM+ePVNqeZlMhuXLlyMhIQE//fSTvL1r166oXLkyfv31Vzx+/DjLej4+Pjhx4gSGDRsm7x3t3LkzqlWrhnnz5uVY5Hv8+HHExcUptD1//hyVK1fONc4GDRrA0tISq1atUrgi8WOurq54/PixwhVl4eHhuHz5cq7b/5gmP/d0EXtuiqBixYphxowZmDZtGrZt24Z+/fph9erVaNu2LVq3bo1BgwahePHiiIiIwMOHD3Hr1i3s3LkTADB37lwcPXoUTZo0wXfffYdq1arh/fv3OHbsGCZPnoyKFSti4sSJ2L17N5o0aYJJkyahevXqSEtLQ1BQEE6cOIEpU6agbt26ucY4ZMgQjBw5EsHBwWjQoIG85yjD3Llz4efnhwYNGmD8+PGoUKECEhIS8OLFCxw5cgSrVq3KU3dyy5Yt0bp1a3z77beIjo5Gw4YN5VdL1apVC/3791dY3tnZGZ06dcLs2bPh5OSEv/76C35+fvjtt9/kl5+q43i0b98eixcvRp8+fTBixAiEh4dj4cKF2V7BUq1aNezYsQM+Pj4oU6YMjIyMUK1aNZWPRWZt2rRBw4YNMWXKFERHR8Pd3R1XrlzB5s2bAeCTV8lMnDgRPj4+6Ny5M6ZPn446deogPj4e586dQ4cOHdC8eXOlY7GwsECTJk2wYMEC2NrawtXVFefOncP69esVelw+pUOHDti4cSMqVqyI6tWr4+bNm1iwYEGW3xtlYjc3N4eLiwv279+Pr776CtbW1vLYfv/9dzRq1AiNGzfG6NGj4erqipiYGDx9+hQHDx7MU8J3584djBs3Dj179kT58uUhlUpx+vRp3LlzR6EHQ5XfhcWLF6NRo0aoW7cupk+fjnLlyuHff//FgQMHsHr1avkXnI9JpdJsL9fPTdOmTdGuXTt4e3tj+vTpKF26NPT19bF79260bNkS9evXx5QpU1C/fn0kJibi4MGDWLNmDZo2bYpFixbJt6Ovr4+9e/eiVatWqF+/PkaPHo3mzZvD1NQUL1++xK5du3Dw4EFERkbK10lLS8O1a9cwdOjQXGM0MzPDokWLMGzYMLRo0QLDhw+Hg4MDnj59itu3b2PZsmUAgP79+2P16tXo168fhg8fjvDwcMyfPx8WFhZKH48Mmvrc00kaLGamfJbTJH5CCBEfHy9KlSolypcvL78y5fbt28LT01PY29sLQ0ND4ejoKL788kuxatUqhXVfvXolhgwZIhwdHYWhoaFwdnYWnp6e4t9//5Uv8+HDB/G///1PVKhQQUilUmFpaSmqVasmJk2aJEJDQ+XLfXy1VIaoqChhbGwsAIi1a9dmu3/v3r0T48ePF6VLlxaGhobC2tpauLu7i++//158+PBBCJG3ieHi4+PFt99+K1xcXIShoaFwcnISo0ePFpGRkQrLubi4iPbt24tdu3aJKlWqCKlUKlxdXcXixYuzbFPZ4wFAjB07Ntu4NmzYICpUqCBkMpkoU6aMmDdvnli/fr0AIAIDA+XLvXjxQrRq1UqYm5sLAPIrNnK7Wurdu3cK75Xxu5N5uxEREWLw4MHCyspKmJiYiJYtW4qrV68KAApXKOUkMjJSTJgwQZQqVUoYGhoKe3t70b59e/HPP/8oxJfdzwofXYn0+vVr0b17d1GsWDFhbm4u2rRpI+7du5fl9ym3v4HIyEgxdOhQYW9vL0xMTESjRo3EhQsXsr365VOxCyHEyZMnRa1atYRMJhMAFOIIDAwUQ4YMEcWLFxeGhobCzs5ONGjQQPz000/yZTKuiNq5c2eWWD++Wurff/8VgwYNEhUrVhSmpqbCzMxMVK9eXSxZskThSjNVfheESJ+Mr2fPnsLGxkZIpVJRqlQpMWjQoE9ejbR+/Xqhr68vgoODFdpzmsRPCCHu3r0r9PT0xODBgxXaw8LCxPTp00XFihWFkZGRMDMzE3Xq1BHLli3L8YrH9+/fix9//FHUrl1bmJmZCUNDQ1GqVCnRr18/cenSJYVlT506JQCImzdv5rpPGY4cOSKaNm0qTE1NhYmJiahcubL8aq0MmzZtEpUqVRJGRkaicuXKwsfHJ8erpXL7LFLX5x4JIREil/42IsqRq6srqlatikOHDmk6FI3Ztm0b+vbti0uXLqFBgwaaDoc0JCEhAaVKlcKUKVPw7bffajqcXPXv3x/Pnz/HpUuXNB0K5SMOSxGRUrZv3443b96gWrVq0NPTw9WrV7FgwQI0adKEiU0RZ2RkhDlz5mD27NkYN24cTE1NNR1Stp49ewYfHx+11H5R4cbkhoiUYm5ujh07duCnn35CbGwsnJycMGjQIIWiUCq6RowYgffv3+P58+efXeOVX4KCgrBs2TI0atRI06FQPuOwFBEREekUXgpOREREOoXJDREREekUJjdERESkU4pcQXFaWhqCg4Nhbm6u1puhERERUf4RQiAmJgbOzs6fnDi0yCU3wcHB8nuZEBERkXZ59erVJ2diLnLJTcYU4q9evcrT9NhERERU8KKjo1GyZMkcbwWSWZFLbjKGoiwsLJjcEBERaRllSkpYUExEREQ6hckNERER6RQmN0RERKRTmNwQERGRTmFyQ0RERDqFyQ0RERHpFCY3REREpFOY3BAREZFOYXJDREREOoXJDREREekUjSY358+fR8eOHeHs7AyJRIJ9+/Z9cp1z587B3d0dRkZGKFOmDFatWpX/gRIREZHW0GhyExsbixo1amDZsmVKLR8YGIh27dqhcePG8Pf3x3fffYfx48dj9+7d+RwpERERaQuN3jizbdu2aNu2rdLLr1q1CqVKlcLSpUsBAJUqVcKNGzewcOFCdO/ePZ+iJCIiok+Ke4uk4DuQhN+HYdR9oPnvgKGxRkLRqruCX7lyBa1atVJoa926NdavX4/k5GQYGhpmWScxMRGJiYny59HR0fkeJxERkc5KjAbC7wNh99If4en/vnidhF5/9UDj0i+xoKMfUGM04FBLIyFqVXITGhoKBwcHhTYHBwekpKQgLCwMTk5OWdaZN28e5syZU1AhEhER6YaUBCDinyxJDKJfZln0bYwpai0Zh/fxxvg7qASalX2B9uH3mNwoSyKRKDwXQmTbnmHGjBmYPHmy/Hl0dDRKliyZfwESERFpk7QU4P0zxSTm3V3g/RNApCm1CXsHMwxoFoE/jhZHmRL6cOjxJ1C+UT4HnjOtSm4cHR0RGhqq0Pb27VsYGBjAxsYm23VkMhlkMllBhEdERFR4CQHEvPoviQm7m/5vxEMgNfHT6wOA1AKwrZr+sKn63/9N7DB/SApM55zDt982hKWlUf7uyydoVXJTv359HDx4UKHtxIkT8PDwyLbehoiIqEiKe5c1iQm/ByTFKLe+vgywqZw1iTEvCUgk8PW9j6R/U9GvX3X5KjKZAX755at82iHVaDS5+fDhA54+fSp/HhgYiICAAFhbW6NUqVKYMWMG3rx5g82bNwMARo0ahWXLlmHy5MkYPnw4rly5gvXr12P79u2a2gUiIiLNSYoBwu7/l8Rk1MXEvVVufYkeUMwtaxJjVRbQy5oiJCSkYNKkY1i16iaMjQ1Qq5YjqlSxV/NOfT6NJjc3btxA8+bN5c8zamMGDhyIjRs3IiQkBEFBQfLXS5cujSNHjmDSpElYvnw5nJ2d8ccff/AycCIi0m0pienFvRn1MLkU9+bIwiVrEmNdETBQbgjp8eNweHruxO3b/wIA4uNTsH37Pfz005d52aN8JREZFblFRHR0NCwtLREVFQULCwtNh0NERPSftNT/L+69q3iFUuQTQKQqtw0T+6xJjE0VQJb3c962bXcxcuQhfPiQBAAwMjLAsmVtMWRIrRwv6FE3Vc7fWlVzQ0REpBOEAGJeZ01iIh6mX4KtDKm5YgJjWw2wrZKe3KhJXFwyJkw4inXr/OVtFSvaYufOnqhatfANR2VgckNERJSfMhf3ZiQxYfeAJCUnldWXAdaVMiUwisW9+eXhw3fw9NyFe/f+q98ZOLAGli9vB1NTab69rzowuSEiIlKHzMW9mZOYuH+VW1+iB1iVz5rE5FDcm59SU9PQtasPHj0KBwCYmBhixYp2GDiwZoHGkVdMboiIiFSRubg3LNMj+oXy2zAvlTWJUaG4N7/p6+th7dqOaNZsEypXtoOPTw9Urmyn6bCUxuSGiIgoOxnFvR8nMZGPlS/uNbb9/wQmUxJjUxmQWeZv7HkghFAoDm7c2AUHD/ZGs2auMDHRrrnkmNwQEVHRllHcq5DE3P3M4t6MmXsLb9FtBiEE1q/3x+HDT7B7tyf09P5LcNq1K6/ByPKOyQ0RERUdcWFZk5g8F/dmnrm3VL4W9+aXmJhEjBp1GNu23QUA/PbbRcyY0VjDUX0+JjdERKR7kmKA8AdZk5g8FfdmupdSsXIFXtybXwICQuHpuRNPnkTI20JCPmQZntJGuvETIiKioiklEYh8pJjA5Lm4N1MSY1Op0BT3qpsQAqtW3cCkSceRmJheO2RhIcPatR3h6VlFw9GpB5MbIiIq/NJSgajnWZOYPBX3Zr6rdZVCWdybX6KiEjB8+EHs3PlA3ubu7gQfnx4oW9Zag5GpF5MbIiIqPIQAPrxRTGDC7gERD5Qv7jU0+6gmpprWFPfmpxs3guHltQvPn0fK28aPr4P581tCJtOtdEC39oaIiLRHfHjWJCb8HpAYpdz6+tIcZu7VzuLe/LZ69Q15YmNlZYQNGzqha9dKGo4qfzC5ISKi/JX0AQi/nzWJiQ1Vbn2JHmBVTjGJ0bHi3oKwdGkbXLr0CubmMvj49ICrq5WmQ8o3/K0gIiL1UCjuzZTERAUqvw3zklmTGOuKgKFx/sWto2JiEmFuLpM/NzWV4vjxfnBwMINUqq/ByPIfkxsiIlKNQnFvpiQm8jGQlqLcNoxsALtqiklMESvuzS9CCCxefAW//noJV68OVSgULlmyaBxfJjdERJQ9eXHvR3e0Dn8ApMQrt42Pi3szZvE1sWddTD4ID4/DoEH7cejQYwCAl9cuXLo0ROcKhj+laO0tERFlLz5cMYl5dzfvxb2Zb0NgUSq9Zoby3aVLQejVazdev/5vtuWWLcso3E6hqGByQ0RUlCR9+G/m3sxJTF6KezMnMcXKs7hXQ9LSBObPv4T//e80UlMFAMDW1gRbtnRFmzblNBydZvA3kYhIF6UmARGPsiYxeSnuzZzEWFdicW8h8vZtLAYM2Ivjx5/J25o2dcG2bd3h7Gyuwcg0i8kNEZE2S0tNT1gyz9yb1+LezEmMTRXAyCpfQ6fPc+HCS3h57UJIyAcA6SVM//tfE8yc2RQGBkV7KJDJDRGRNhAC+BCs2AujcnGv6Uc9MdVY3KvFYmOT5YmNg4Mp/vqrG1q0KKPhqAoHJjdERIXNx8W9Gf9PfK/c+nqG6Td+/DiJYXGvTmnTphy+/bYhbtwIxl9/dYOjo5mmQyo0mNwQEWlKciwQdj9rEhMbouQGJOmz9Np8dPsBq3KAvmG+hk4FLyAgFDVqOECSqZftp5++hEQC6Oszac2MyQ0RUX77uLg3I4mJeq78NsxKZL2HknVFwNAk/+KmQiE1NQ1z557Djz+exx9/tMW4cXXkrxX12pqcMLkhIlIXkQa8f541iYl8pEJxr/X/JzDVWNxLCA6OQZ8+u3Hu3EsAwJQpJ9CiRRlUrGir4cgKNyY3RESqylzcq3ALgvufUdybMXOvA4t7CQBw/PhT9Ou3F2FhcQAAfX0J5sxpBjc3G80GpgWY3BAR5SY+Ipsk5h6QEKnc+lmKezNm7nVhcS9lKyUlDT/8cBq//npJ3laihAW2b++ORo1KaTAy7cHkhogISC/uzZi5N/OcMXkq7s30sCrP4l5S2qtXUejdezcuXXolb2vfvjw2beoCGxvWVymLyQ0RFS2pSekT3H2cxEQFAhDKbUNe3PvxzL08+VDeXbv2Bm3bbkVERPrQpoGBHn799StMmlS/SN4f6nMwuSEi3STSMs3cmymJyVNx70d3tGZxL+UDNzcbWFrKEBERDxcXS+zY0QP16pXQdFhaickNEWk3IdKHjj5OYsIfAClxym3D0DT9iqTMSYxdNRb3UoGysjKCj08PLFx4BatWtUexYryHV15JhBBK9sPqhujoaFhaWiIqKgoWFhaaDoeIVBEfkX5F0sf3UVKluNe6YqbhpGos7iWNOXDgEdzdnVC8OM9FylDl/M2eGyIqfJJjgfCH/yUwGUnMh2AlNyABrMpmTWJY3EuFQGJiCr799iR+//1vNG5cCqdPD+RkfGrG5IaINCc1Ob0G5uPLrN8/h/LFvcWzmbmXxb1UOD1/HglPz524eTP9KrwLF4Lg63sfffpU03BkuoXJDRHlvyzFvf+fxEQ8AtKSlduGUTHFmXttqgK2VdLbibTArl0PMHToAURHJwIAZDJ9LFnSGr17V9VwZLqHyQ0RqU+W4t6M2xDcV76418AkPWlRSGKqAqaOLO4lrZSQkILJk49j5cob8rby5a3h69sTNWs6ajAy3cXkhojyJiEymyTmHpAQodz6Hxf3ZiQxlq4s7iWd8eRJODw9dyEgIFTe1rt3Vaxe3QHm5jINRqbbmNwQUe6S4xRn7s1IYj68UXIDHxX3ZiQxxcoD+tJ8DZ1Ik968iYa7+xrExCQBAIyMDPDnn20xdGgtSNgLma+Y3BBRutTk/2buld9L6W7einsz34LAplL6PDJERUzx4hbo3786Vqy4gYoVbeHr2wPVqjloOqwigckNUVEj0oCoF/8lMe/u5r249+P7KLG4l0jBokWtYWtrgm++aQgzM/ZUFhQmN0S6SgggNjRrEpOX4l6FJKYai3uJsrF5823o60vQt291eZuRkQHmzGmuwaiKJiY3RLogITI9aVFIYlQp7jVIL+79OIlhcS/RJ8XGJmHcuKPYuDEAJiaGqF3bCZUq2Wk6rCKNyQ2RNkmOAyIepicumZMYlYp7y2RKYqqxuJfoM9y79xaenjvx8GEYACAuLhm7dz/E//7H5EaTmNwQFUapycD7J4r3UAq7B7x/BuWLe52zJjEs7iVSCyEENmzwx9dfH0V8fPpd5k1NDbF6dQeFYSnSDCY3RJok0oDol4q9MGH3gIh/VCvutfno9gM2VQBj6/yNnaiIiolJxOjRh7F16115W40aDvD17Qk3NxsNRkYZmNwQFYSPi3vlc8bcT79JpDIMjNOTlsxJjG1VwNSJxb1EBeT27VB4eu7C48fh8rZRo9yxeHFrGBvzpqyFBZMbInVLeJ81iQm7BySEf3JVADkU91YFLEuzuJdIg1JS0tCtmy+eP48EAJibS7FuXSd4elbRcGT0MSY3RHmVubg38+PDayU38HFxb8bMvW4s7iUqhAwM9LBhQyd8+eVm1KzpCF/fHihblsO/hRGTG6JPUSjuzXQLgsinyFtxb0ZdTGUW9xIVckIIhVslNG3qiiNH+qBZM1fIZDyFFlb8yRBlyCju/TiJifgHSE1Sbhsyq6w1MTZVAGMWGRJpEyEEli27htOnX2D3bk/o6f2X4LRuXU6DkZEymNxQ0SMEEPfvR8NJd/NY3PvxzL0s7iXSdu/fJ2Do0APYs+chAGDhwsuYNq2hhqMiVTC5Id2W8D49acmcxKha3FusguIdre2qARaugJ5+fkZORBpw7dobeHntwosX7+Vt4eFK3q6ECg0mN6QbkuMzFffezUNxLwDLMoq9MCzuJSoyhBBYsuQqvv32JFJS0gAAxYoZYdOmLujYsYKGoyNVMbkh7ZKaDLx/qpjEhP//zL0iTbltmDplTWKsKwFSs/yNnYgKpYiIeAwatA8HDz6WtzVoUBLbt3dHqVKWGoyM8orJDRVOIg2IDsqaxKhc3Pvx7QdY3EtE/7l8+RV69dqFV6+i5W3fftsQP/7YHIaGHHrWVkxuSLOEAOLeKg4lhd9Lv8N18gfltmFgnH5ZtcLtB6qmX37N4l4iysXatbfkiY2trQk2b+6Ctm3Lazgq+lwaT25WrFiBBQsWICQkBFWqVMHSpUvRuHHjHJffunUr5s+fjydPnsDS0hJt2rTBwoULYWPDb+OFXmJU1susw+4B8WHKrZ9dcW/GzL0s7iWiPPjzz7a4cuUV7O1NsX17dxQvbqHpkEgNJEIIJWchUz8fHx/0798fK1asQMOGDbF69WqsW7cODx48QKlSpbIsf/HiRTRt2hRLlixBx44d8ebNG4waNQrly5fH3r17lXrP6OhoWFpaIioqChYW/CXOFwrFvZmSmJhXym8jc3FvRhJTzA0wkOVf3ESk86KiEmBpaaTQ9uZNNBwczGBgwNubFGaqnL81mtzUrVsXtWvXxsqVK+VtlSpVQpcuXTBv3rwsyy9cuBArV67Es2fP5G1//vkn5s+fj1evlDtxMrlRo7QUIPJJ1iTm/dO8FfdmJDE2lVncS0RqlZqahnnzLmLp0qu4fn04SpcupumQSEWqnL81NiyVlJSEmzdvYvr06QrtrVq1wuXLl7Ndp0GDBvj+++9x5MgRtG3bFm/fvsWuXbvQvn37HN8nMTERiYmJ8ufR0dE5Lks5UCjuzZTERDxUobjXMlNRb6aZe01s8zd2Iiry/v33A/r124uTJ58DALy8duHixSGQSjmcras0ltyEhYUhNTUVDg4OCu0ODg4IDQ3Ndp0GDRpg69at8PLyQkJCAlJSUtCpUyf8+eefOb7PvHnzMGfOHLXGrrPkxb3Z3NFa6eJeo/9m7s18LyWz4izuJaICd/p0IPr23YPQ0PTPMD09CTp0cIO+Pj+PdJnGC4olH53wPr5JWWYPHjzA+PHjMXPmTLRu3RohISH45ptvMGrUKKxfvz7bdWbMmIHJkyfLn0dHR6NkyZLq2wFtlRiVfkWSPIm5q1pxr0QfsK7w0c0gq7G4l4gKhdTUNMydew4//ngeGcUXTk5m2LatO5o1c9VobJT/NJbc2NraQl9fP0svzdu3b7P05mSYN28eGjZsiG+++QYAUL16dZiamqJx48b46aef4OTklGUdmUwGmawIF6Emx6fPDfNxEqNScW/pTElMNRb3ElGhFhwcg7599+Ds2RfytlatymLLlq6wtzfVXGBUYDSW3EilUri7u8PPzw9du3aVt/v5+aFz587ZrhMXFwcDA8WQ9fXTewk0WBddOKSlAJFPsyYxKhX3OmZNYljcS0Ra5OTJ5+jTZzfevUu/H5S+vgQ//tgc337bSOHO3qTbNDosNXnyZPTv3x8eHh6oX78+1qxZg6CgIIwaNQpA+pDSmzdvsHnzZgBAx44dMXz4cKxcuVI+LDVx4kTUqVMHzs7OmtyVgiMEEPP/xb3v7ua9uDdLEsPiXiLSfomJKfLEpnhxc+zY0QONGmWdWoR0m0aTGy8vL4SHh2Pu3LkICQlB1apVceTIEbi4uAAAQkJCEBQUJF9+0KBBiImJwbJlyzBlyhRYWVnhyy+/xG+//aapXchfGcW9CrP33geSYpRb38AIsK6smMSwuJeIdFj79m6YOrU+HjwIw6ZNXWBra6LpkEgDNDrPjSYUynluEqOzv0Ip/p1y60v002tgMicwtlXTJ8JjcS8R6bAbN4Lh7u6kcCFKSkoa9PQkHIbSMVoxz02RlJIAhD/MmsTEBH163QwKxb0ZM/dWYHEvERUpycmp+O67U1i48ApWrmyPUaM85K9xpmFicpMfshT3Zszc+ySPxb2ZZ+41z9/YiYgKuZcv36NXr924evU1AGDixGNo0aIMypWz1nBkVFgwuVG3CzOAm0uA1MRPLwt8VNybeeZeu/yNk4hIC+3f/w8GDdqP9+8TAACGhnr47bcWKFuWt1Og/zC5UafEaODabwCyKWNSKO7NdC8l8xIs7iUi+oSkpFRMm+aH33//W95WurQVfHx64IsvimswMiqMmNyoU2oC5ImNeSmg2tD/khirsizuJSLKg+fPI+HltQs3bgTL27p3r4R16zrBysoolzWpqGJyo06Z62nsawH1Z2ouFiIiHXDxYhDat9+G6Oj0oX6pVB9LlrTG6NEeOd6qh4jJjTplvqpewmp9IqLPVaWKHYoVM0J0dCLKlbOGr28P1KqV9VY7RJnxDKxWmZMbfqMgIvpcxYoZw8enB/r1q46bN0cwsSGlsOdGnRQu82ZyQ0SkKl/f+2jcuBScnP6b9qJu3RKoW7eEBqMibcOeG3XisBQRUZ7ExydjxIiD8PLahb599yA1Vck5wYiywTOwWmW+BJw9N0REyvjnnzDUrbsOa9feAgCcOfMC+/c/0nBUpM2Y3KhT5mEp1twQEX3Sli234eGxBnfvvgUAGBsbwNu7M7p1q6ThyEibseZGrTgsRUSkjNjYJHz99VF4ewfI26pUsYOvb09UrswZ2unzMLlRJ8FhKSKiT7l//y08PXfhwYN38rahQ2vhjz/awsTEUIORka5gcqNOHJYiIsrVy5fv8cUXaxEfnwIAMDU1xOrVHdC3b3UNR0a6hGMnasVhKSKi3Li4WGHAgBoAgOrVHXDz5ggmNqR27LlRJw5LERF90pIlrVG8uDmmTm0AY2MOQ5H6sXtBnRSGpXhoiahoE0Jg9eob2L79rkK7sbEhfvihKRMbyjfsuVEr9twQEQFAdHQiRow4CB+f+zA1NYS7uzPc3Gw0HRYVEexeUCcWFBMR4datENSuvRo+PvcBALGxyTh4kJPyUcFhz41asaCYiIouIQSWL7+OKVNOICkpFQBgaSnDhg2clI8KFpMbdWJBMREVUe/fJ2Do0APYs+ehvO2LL5zh49MDpUsX02BkVBQxuVEnDksRURF07dobeHntwosX7+VtkybVw6+/toBUqq+5wKjIYnKjVhyWIqKiJSkpFT16+OLVq2gAQLFiRti4sQs6daqg4cioKOMZWJ04LEVERYxUqg9v786QSID69UsgIGAUExvSOPbcqFWmYSkmN0Sko4QQkGQaev/qqzI4frwfmjVzhaEhh6FI89hzo06Cw1JEpLvS0gTmz7+E7t19IRR6qoGWLcsysaFCgz03apU5uWHPDRHpjnfvYjFw4D4cPfoUALBkyVVMnlxfw1ERZY/JjToJDksRke65cOElevXajeDgGADp391iYhI1HBVRzpjcqBOHpYhIh6SlCcybdwEzZ55FWlr655u9vSn++qsrWrYsq+HoiHLG5EatOCxFRLrh338/oH//vfDzey5va97cFVu3doOTk7kGIyP6NCY36sRhKSLSAadPB6Jv3z0IDf0AIP272qxZTfG//zWBvj57panwY3KjThyWIiIdsGGDvzyxcXQ0w7Zt3dC8eWkNR0WkvDydgVNSUnDy5EmsXr0aMTHpBWbBwcH48OGDWoPTPpzEj4i034oV7VGunDVatiyD27dHMbEhraNyz83Lly/Rpk0bBAUFITExES1btoS5uTnmz5+PhIQErFq1Kj/i1A68txQRaaHIyHgUK2Ysf25hIcO5c4Pg6GgGPT1+lpH2UbnnZsKECfDw8EBkZCSMjf/7Y+jatStOnTql1uC0D4eliEh7pKSk4X//O43y5f/Ey5fvFV5zdjZnYkNaS+Wem4sXL+LSpUuQSqUK7S4uLnjz5o3aAtNKvLcUEWmJ16+j0afPbly4EAQA6NVrN86fH8RZhkknqJzcpKWlITU1NUv769evYW5exC8PVBiWYs8NERVOR448wYABexEeHg8A0NeXoFu3irwSinSGyr/JLVu2xNKlS+XPJRIJPnz4gFmzZqFdu3bqjE0LcZ4bIiq8kpNTMW2aH9q33yZPbEqVssSFC4PxzTcNOQxFOkPlnpslS5agefPmqFy5MhISEtCnTx88efIEtra22L59e37EqD04zw0RFVJBQVHo1WsXrlx5LW/r1KkCvL07w9raOJc1ibSPysmNs7MzAgICsGPHDty8eRNpaWkYOnQo+vbtq1BgXDSxoJiICp/Dhx+jf/+9iIxMAAAYGuph/vyWmDChLiTsZSYdpHJyc/78eTRo0ACDBw/G4MGD5e0pKSk4f/48mjRpotYAtQoLiomoEEpJSZMnNqVLW8HHpwe++KK4hqMiyj8qJzfNmzdHSEgI7O3tFdqjoqLQvHnzbIuNiwzOc0NEhVDnzhUxcWJdvHoVjXXrOsHKykjTIRHlK5WTGyFEtt2Y4eHhMDU1VUtQ2ovDUkSkeX///Rp16hRX+KxesKAV9PUlHIaiIkHp5KZbt24A0q+OGjRoEGQymfy11NRU3LlzBw0aNFB/hNqEw1JEpEEJCSn45psTWLbsOtas6YDhw93lrxkY8AsXFR1KJzeWlpYA0ntuzM3NFYqHpVIp6tWrh+HDh6s/Qm3CYSki0pCnTyPg6bkT/v6hAIDx44+hZcuycHW10mxgRBqgdHLj7e0NAHB1dcXUqVM5BJWtzD03/JZERAXDx+cehg8/iJiYJACATKaP339vAxcXSw1HRqQZKtfczJo1Kz/i0A2Ck/gRUcGJj0/GxInHsGbNLXlbhQo28PXtierVHTQYGZFmqZzcAMCuXbvg6+uLoKAgJCUlKbx269atHNYqCjiJHxEVjEePwuDpuQt37vwrb+vXrzpWrmwPMzNpLmsS6T6Vx07++OMPDB48GPb29vD390edOnVgY2OD58+fo23btvkRo/YQvFqKiPLf6dOBcHdfI09sjI0NsGFDJ2ze3IWJDRHykNysWLECa9aswbJlyyCVSjFt2jT4+flh/PjxiIqKyo8YtQiHpYgo/9Wo4SC/ZULlyna4fn04Bg+uxcu8if6fyslNUFCQ/JJvY2NjxMTEAAD69+/Pe0vx3lJEVABsbEywY0cPDBtWC9euDUOVKvafXomoCFE5uXF0dER4eDgAwMXFBVevXgUABAYGQijM81IEcViKiNRMCIEtW24jNPSDQnuDBiWxdm0nmJpyGIroYyqfgb/88kscPHgQADB06FBMmjQJLVu2hJeXF7p27ar2ALULh6WISH0+fEjCwIH7MGDAPvTrtwepqWmfXomIVL9aas2aNUhLS/8DGzVqFKytrXHx4kV07NgRo0aNUnuAWoXDUkSkJnfu/AtPz5149Ci9p/zUqUAcPfoUHTq4aTgyosJP5eRGT08Penr/dfh4enrC09MTAPDmzRsUL16U7zTLYSki+jxCCKxdewsTJhxDQkIKAMDMTIq1azsysSFSklrOwKGhofj6669Rrlw5ldddsWIFSpcuDSMjI7i7u+PChQu5Lp+YmIjvv/8eLi4ukMlkKFu2LDZs2JDX0NWL95Yios8QHZ2IPn32YOTIQ/LEplYtR9y6NQK9elXVcHRE2kPp5Ob9+/fo27cv7Ozs4OzsjD/++ANpaWmYOXMmypQpg6tXr6qcZPj4+GDixIn4/vvv4e/vj8aNG6Nt27YICgrKcR1PT0+cOnUK69evx6NHj7B9+3ZUrFhRpffNN7y3FBHlkb9/CNzd12DHjnvytrFjv8Dly0NRvryNBiMj0j5KD0t99913OH/+PAYOHIhjx45h0qRJOHbsGBISEnD06FE0bdpU5TdfvHgxhg4dimHDhgEAli5diuPHj2PlypWYN29eluWPHTuGc+fO4fnz57C2tgaQfq+rwoPDUkSkuqdPI1Cv3nokJaUCACwtZVi/vhO6d6+s4ciItJPSZ+DDhw/D29sbCxcuxIEDByCEgJubG06fPp2nxCYpKQk3b95Eq1atFNpbtWqFy5cvZ7vOgQMH4OHhgfnz56N48eJwc3PD1KlTER8fn+P7JCYmIjo6WuGRb1hQTER5UK6cNfr3rw4A+OILZ9y6NZKJDdFnULrnJjg4GJUrp/+xlSlTBkZGRvIel7wICwtDamoqHBwUb+7m4OCA0NDQbNd5/vw5Ll68CCMjI+zduxdhYWEYM2YMIiIichwSmzdvHubMmZPnOFXCeW6IKI/++KMtypWzxuTJ9SGV6ms6HCKtpvQZOC0tDYaGhvLn+vr6MDU1/ewAPp4uXAiR4xTiaWlpkEgk2Lp1K+rUqYN27dph8eLF2LhxY469NzNmzEBUVJT88erVq8+OOWcsKCai3Akh8PvvV+Hjc0+h3cTEENOnN2JiQ6QGSvfcCCEwaNAgyGQyAEBCQgJGjRqVJcHZs2ePUtuztbWFvr5+ll6at2/fZunNyeDk5ITixYvD0tJS3lapUiUIIfD69WuUL18+yzoymUwec75jQTER5SIiIh5DhuzH/v2PYGYmRe3aTiwWJsoHSvfcDBw4EPb29rC0tISlpSX69esHZ2dn+fOMh7KkUinc3d3h5+en0O7n5ye/d9XHGjZsiODgYHz48N805I8fP4aenh5KlCih9HvnHw5LEVH2rl59jVq1VmP//kcA0mcfPn78mYajItJNSvfceHt7q/3NJ0+ejP79+8PDwwP169fHmjVrEBQUJJ/peMaMGXjz5g02b94MAOjTpw9+/PFHDB48GHPmzEFYWBi++eYbDBkyBMbGxmqPT2Wc54aIPpKWJrBo0WV8991ppKSk9+7a2Bhj06YuaN+ek/IR5QeVZyhWJy8vL4SHh2Pu3LkICQlB1apVceTIEbi4uAAAQkJCFOa8MTMzg5+fH77++mt4eHjAxsYGnp6e+OmnnzS1C4o4LEVEmYSFxWHgwH04cuSJvK1Ro1LYvr07SpSw0GBkRLpNIorYrbyjo6NhaWmJqKgoWFio+cMlYAVwamz6/9tuBir3V+/2iUhrXLjwEr1778abNzEA0r/vzJjRCHPmNIeBAYetiVSlyvlboz03OofDUkQEICEhBb167UZwcHpiY2dngr/+6oZWrcpqODKiooFfH9SJw1JEBMDIyAAbN3aGRAI0a+aKgIBRTGyIChB7btQqc88N80aioiQtTUBP778vNS1blsXJkwPQtKkL9PX5eUBUkPL0F7dlyxY0bNgQzs7OePnyJYD0+0Lt379frcFpn8yXgrPnhqgoSE1Nw5w5Z9Gz5058XML45ZelmdgQaYDKf3UrV67E5MmT0a5dO7x//x6pqek3erOyssLSpUvVHZ924b2liIqU0NAPaNXqL8yefQ579jzEn39e03RIRIQ8JDd//vkn1q5di++//x76+v9NE+7h4YG7d++qNTitw3tLERUZJ08+R40aq3D6dCAAQE9PgoSEFA1HRURAHmpuAgMDUatWrSztMpkMsbGxaglKe3FYikjXpaSkYfbss/jllwvy7zPOzubYvr07mjRx0WxwRAQgD8lN6dKlERAQIJ9oL8PRo0fldw0vsjgsRaTT3ryJRp8+e3D+/Et5W9u25bBpUxfY2X3+jYSJSD1UTm6++eYbjB07FgkJCRBC4Nq1a9i+fTvmzZuHdevW5UeM2oPDUkQ66+jRJxgwYB/CwuIAAPr6Evzyy1eYOrWBwlVSRKR5Kic3gwcPRkpKCqZNm4a4uDj06dMHxYsXx++//45evXrlR4xahJP4EemqTZtuyxObkiUtsGNHDzRoUFLDURFRdvI0z83w4cMxfPhwhIWFIS0tDfb29uqOSztxEj8inbV6dQdcvx6MKlXs4O3dGTY2JpoOiYhyoPLYyZw5c/Ds2TMAgK2tLRMbBRyWItIV4eFxCs8tLY1w6dIQ7N/fi4kNUSGn8hl49+7dcHNzQ7169bBs2TK8e/cuP+LSTiwoJtJ6SUmpmDz5OCpWXI7Xr6MVXnN0NIOEvbJEhZ7Kyc2dO3dw584dfPnll1i8eDGKFy+Odu3aYdu2bYiLi/v0BnQZC4qJtFpgYCQaN/bGkiVXERYWh169diElJe3TKxJRoZKnM3CVKlXwyy+/4Pnz5zhz5gxKly6NiRMnwtHRUd3xaRnOc0OkrfbseYhatVbj2rU3AACpVB+9elWFvj7/lom0zWffONPU1BTGxsaQSqWIiYlRR0zai8NSRFonMTEFU6eewLJl1+VtZcsWg49PD7i7O2swMiLKqzz13AQGBuLnn39G5cqV4eHhgVu3bmH27NkIDQ1Vd3zahcNSRFrl6dMINGiwQSGx8fSsglu3RjKxIdJiKvfc1K9fH9euXUO1atUwePBg+Tw3BHCeGyLtsWfPQwwatA8xMUkAAJlMH7//3gYjRrizaJhIy6mc3DRv3hzr1q1DlSpV8iMe7cZ5boi0hkQCeWLj5mYDX98eqFGjqNcNEukGlZObX375JT/i0BEcliLSFl27VsLXX9dBREQ8Vq5sD3NzmaZDIiI1USq5mTx5Mn788UeYmppi8uTJuS67ePFitQSmlQSHpYgKq0uXgtCgQUmFIaclS1pDT0/CYSgiHaNUcuPv74/k5GT5/ykHHJYiKnTi4pLx9ddHsGFDADZs6ITBg2vJX9PXZw8rkS5SKrk5c+ZMtv+nj3FYiqgwefDgHTw9d+L+/fSZ1MeOPYJWrcqieHELDUdGRPlJ5TPwkCFDsp3PJjY2FkOGDFFLUFqLw1JEhcbGjQHw8FgjT2xMTAyxenUHJjZERYDKyc2mTZsQHx+fpT0+Ph6bN29WS1Bai5P4EWnchw9JGDhwHwYP3o/4+BQAQLVq9rh5cwT696+h4eiIqCAofbVUdHQ0hBAQQiAmJgZGRkby11JTU3HkyBHeIZzDUkQadefOv/Dy2oV//gmTt40YURtLl7aBsbGhBiMjooKkdHJjZWUFiST9qgI3N7csr0skEsyZM0etwWkf3luKSFOOHn2Cbt18kZCQ3ltjZibF2rUd0atXVQ1HRkQFTenk5syZMxBC4Msvv8Tu3bthbW0tf00qlcLFxQXOzkV8unIOSxFpjIeHM6ytjREcHIOaNR3h69sD5cvbaDosItIApZObpk2bAki/r1SpUqU4L0R2eG8pIo2xszPFjh3d4eNzHwsXtoKR0WffF5iItJRSf/137txB1apVoaenh6ioKNy9ezfHZatXr6624LQO57khKhBCCKxf749OnSrA3t5U3t64sQsaN3bRYGREVBgoldzUrFkToaGhsLe3R82aNSGRSCAULntOJ5FIkJqaqvYgtQcvBSfKb1FRCRg27CB27XqAnTsf4OjRvtDT498bEf1HqeQmMDAQdnZ28v9TDjgsRZSvrl9/Ay+vXQgMfA8AOHHiGU6fDkSLFmU0GxgRFSpKJTcuLi7Z/p8+xoJiovwghMAff/yNb77xQ3Jy+t+ZlZURNm7szMSGiLLI0yR+hw8flj+fNm0arKys0KBBA7x8+VKtwWkd9twQqV1ERDy6dvXBxInH5YlNvXolEBAwEp07V9RwdERUGKl8Bv7ll19gbGwMALhy5QqWLVuG+fPnw9bWFpMmTVJ7gNqF89wQqdPVq69Rq9Zq7N//SN42dWp9nD8/CC4uVpoLjIgKNZWvlXz16hXKlSsHANi3bx969OiBESNGoGHDhmjWrJm649MunOeGSG0ePnyHxo29kZKS/ndlY2OMTZu6oH37rJOIEhFlpnLPjZmZGcLDwwEAJ06cQIsWLQAARkZG2d5zqkjhsBSR2lSqZIe+fasBABo1KoWAgFFMbIhIKSr33LRs2RLDhg1DrVq18PjxY7Rv3x4AcP/+fbi6uqo7Pi3DYSkidVq+vB2qVbPHhAn1YGDALwxEpByVPy2WL1+O+vXr4927d9i9ezdsbNKnN7958yZ69+6t9gC1CoeliPIkLU3gl18uYNeuBwrtpqZSTJnSgIkNEalE5Z4bKysrLFu2LEs7b5oJ8K7gRKp7+zYW/fvvxYkTz2BhIUOtWo4oW9b60ysSEeUgTzdfef/+PdavX4+HDx9CIpGgUqVKGDp0KCwtLdUdn3YRnKGYSBVnz75Anz67ERLyAQAQE5OIM2deMLkhos+icvfCjRs3ULZsWSxZsgQREREICwvDkiVLULZsWdy6dSs/YtQevLcUkVJSU9Mwd+45fPXVZnli4+BgCj+//hg2rLaGoyMibadyz82kSZPQqVMnrF27FgYG6aunpKRg2LBhmDhxIs6fP6/2ILUHh6WIPiU09AP69t2D06f/u5VLixZl8NdfXeHgYKbByIhIV6ic3Ny4cUMhsQEAAwMDTJs2DR4eHmoNTutwWIooVydPPkffvnvw9m0sAEBPT4I5c5phxoxG0NfnFwIiUg+VkxsLCwsEBQWhYkXFac9fvXoFc3NztQWmlTgsRZSj2NgkhcTG2dkc27Z1Q9OmrpoNjIh0jspflby8vDB06FD4+Pjg1atXeP36NXbs2IFhw4bxUnAOSxHlyNRUik2bugAA2rQph4CAkUxsiChfqNxzs3DhQkgkEgwYMAApKSkAAENDQ4wePRq//vqr2gPUKhyWIlKQliagp/ff30KbNuVw5sxANGniotBORKROEiEUzshKi4uLw7NnzyCEQLly5WBiYqLu2PJFdHQ0LC0tERUVBQsLC/Vu/GBP4PGu9P8PDwIsSqp3+0RaIjk5Ff/732k8f/4evr49IOEwLRF9JlXO30qPncTFxWHs2LEoXrw47O3tMWzYMDg5OaF69epak9jkO95bighBQVFo1mwT5s+/jF27HmDFiuuaDomIihilz8CzZs3Cxo0b0b59e/Tq1Qt+fn4YPXp0fsamfVhQTEXcwYOPUKvWaly+/AoAYGCgh9TUPHUOExHlmdI1N3v27MH69evRq1cvAEC/fv3QsGFDpKamQl9fP98C1C6suaGiKSkpFTNmnMTixVflbS4ulvDx6YG6dUtoMDIiKoqUTm5evXqFxo0by5/XqVMHBgYGCA4ORsmSrC0BwGEpKpICAyPRq9duXLv2Rt7WpUtFbNjQCcWKGWswMiIqqpROblJTUyGVShVXNjCQXzFF4LAUFTl79z7E4MH7ERWVCACQSvWxcGFLjBtXh0XERKQxSic3QggMGjQIMplM3paQkIBRo0bB1NRU3rZnzx71RqhVOCxFRcvWrXfliU2ZMsXg69sD7u7OGo6KiIo6pZObgQMHZmnr16+fWoPRfhyWoqJl3bpOuHkzBHXqFMeaNR1gaWmk6ZCIiJRPbry9vfMzDt2QeViKPTekg96+jYW9/X89tVZWRrh6dSjs7U05DEVEhYbGuxdWrFiB0qVLw8jICO7u7rhw4YJS6126dAkGBgaoWbNm/gaoChYUk46Kj0/G6NGHUKXKCrx5E63wmoODGRMbIipUNHoG9vHxwcSJE/H999/D398fjRs3Rtu2bREUFJTrelFRURgwYAC++uqrAopUWZmTG37Yk2549CgM9eqtx6pVNxEWFoc+ffYgNTXt0ysSEWmIRpObxYsXY+jQoRg2bBgqVaqEpUuXomTJkli5cmWu640cORJ9+vRB/fr1CyhSJXFYinTM1q134O6+Bnfu/AsAMDIywIAB1XlfKCIq1DSW3CQlJeHmzZto1aqVQnurVq1w+fLlHNfz9vbGs2fPMGvWrPwOUXUcliIdEReXjGHDDqBfv72IjU0GAFSqZIvr14dj6NDaHIYiokJN5buCq0tYWBhSU1Ph4OCg0O7g4IDQ0NBs13ny5AmmT5+OCxcuwMBAudATExORmJgofx4dHZ3L0p+Lw1Kk/R4+fAdPz124d++tvG3gwBpYvrwdTE2luaxJRFQ45Kl7YcuWLWjYsCGcnZ3x8uVLAMDSpUuxf/9+lbf18TdAIUS23wpTU1PRp08fzJkzB25ubkpvf968ebC0tJQ/8nU2ZQ5LkZbbtu0uPDzWyhMbExNDbNzYGRs3dmFiQ0RaQ+XkZuXKlZg8eTLatWuH9+/fIzU1FQBgZWWFpUuXKr0dW1tb6OvrZ+mlefv2bZbeHACIiYnBjRs3MG7cOBgYGMDAwABz587F7du3YWBggNOnT2f7PjNmzEBUVJT88erVK+V3VmUcliLtZmioh7i49GGoqlXtcePGcAwcWFOzQRERqUjlM/Cff/6JtWvX4vvvv1e4YaaHhwfu3r2r9HakUinc3d3h5+en0O7n54cGDRpkWd7CwgJ3795FQECA/DFq1ChUqFABAQEBqFu3brbvI5PJYGFhofDIN4IzFJN269mzCkaP9sCwYbXw99/DUKmSnaZDIiJSmco1N4GBgahVq1aWdplMhtjYWJW2NXnyZPTv3x8eHh6oX78+1qxZg6CgIIwaNQpAeq/LmzdvsHnzZujp6aFq1aoK69vb28PIyChLu8bw3lKkRYQQOHfuJZo1c1VoX7asHa+GIiKtpnJyU7p0aQQEBMDFxUWh/ejRo6hcubJK2/Ly8kJ4eDjmzp2LkJAQVK1aFUeOHJFvOyQk5JNz3hQuHJYi7RATk4iRIw9h+/Z72Lixs8LQExMbItJ2EiEUxlI+ydvbGz/88AMWLVqEoUOHYt26dXj27BnmzZuHdevWoVevXvkVq1pER0fD0tISUVFR6h+i2t4ICL6U/v9JKYCefu7LE2mAv38IPD134enTCADpRcPPn4+Hg4OZhiMjIsqZKudvlXtuBg8ejJSUFEybNg1xcXHo06cPihcvjt9//73QJzb5jsNSVIgJIbBy5Q1MmnQcSUnpFwJYWMiwdm1HJjZEpFPyNM/N8OHDMXz4cISFhSEtLQ329vbqjktLsaCYCqeoqAQMG3YQu3Y9kLe5uzvBx6cHypa11mBkRETq91mT+Nna2qorDt3AnhsqhG7cCIan504EBr6Xt40fXwfz57eETKaxeTyJiPJNngqKc5t6/fnz558VkHbL6LlhYkOFw/79/6Bnz51ITk5PvK2sjODt3RldulTUcGRERPlH5eRm4sSJCs+Tk5Ph7++PY8eO4ZtvvlFXXNopozabV0pRIVG/fknY2pogJOQD6tYtjh07esDV1UrTYRER5SuVk5sJEyZk2758+XLcuHHjswPSahnDUhySokLC3t4U27Z1x6FDj/HLL19BKuUVfESk+9TWxdC2bVvs3r1bXZvTUhyWIs1JSxNYvvwa3r1TnEyzWTNXLFzYiokNERUZaktudu3aBWvrIn7VBYelSEPCw+PQqdN2jBt3FAMH7kNamkrTVxER6RSVh6Vq1aqlUFAshEBoaCjevXuHFStWqDU47cNhKSp4Fy8GoXfv3Xj9OhoAcPToU1y8GIQmTVw+sSYRkW5SObnp0qWLwnM9PT3Y2dmhWbNmqFixiF+BIZ/smT03lP/S0gR+++0ifvjhDFJT03/3bG1N8NdfXZnYEFGRplJyk5KSAldXV7Ru3RqOjo75FZMWyxiWYs8N5a+3b2PRv/9enDjxTN7WtKkLtm3rDmdncw1GRkSkeSp1MRgYGGD06NFITEzMr3i0m3wSPyY3lH/Onn2BmjVXyRMbiQT44YcmOHlyABMbIiLkYViqbt268Pf3z3JXcAILiinf3b4diq++2iwvGHZwMMVff3VDixZlNBwZEVHhoXJyM2bMGEyZMgWvX7+Gu7s7TE1NFV6vXr262oLTPhyWovxVvboD+vSphr/+uoOvviqNv/7qBkdH3vSSiCgziRBCqWtGhwwZgqVLl8LKyirrRiQSCCEgkUiQmpqq7hjVSpVbpqtsQwUg8jEgswLGRap320T/78OHJHh7+2PMmC+gr89eQiIqGlQ5fyud3Ojr6yMkJATx8fG5LlfYh6vyN7lxAyKfAEbWwNhw9W6bipyUlDTMmXMWtWs7oWvXSpoOh4hIo1Q5fys9LJWRAxX25EWjBGcoJvV48yYaffrswfnzL2FlZYRatZx4TygiIiWp1Ked293ACby3FKnFsWNPUbPmapw//xIAEBOTiIsXgzQcFRGR9lCpoNjNze2TCU5ERMRnBaTdeLUU5V1ycip++OEMfvvtkrytRAkL7NjRHQ0bltJgZERE2kWl5GbOnDmwtLTMr1i0H+e5oTx69SoKvXrtxuXLr+Rt7duXx6ZNXWBjY6LByIiItI9KyU2vXr1gb2+fX7FoP8FLwUl1Bw8+wqBB+xERkV6sb2Cgh19//QqTJtWHnh5/l4iIVKV0csN6G2VwWIpUExOTiCFDDsgTGxcXS+zY0QP16pXQcGRERNpL6bOwkleMF20cliIVmZvLsHFjZwBAly4V4e8/kokNEdFnUrrnJi0t7dMLFXm8FJw+LTU1TWHyvfbt3XDhwmA0bFiSPaRERGrA8RN14r2lKBeJiSkYP/4o+vbdk6UntFGjUkxsiIjUROV7S1EuOM8N5eDZswh4ee3CzZshAIBmzVwxapSHhqMiItJNTG7UisNSlNXOnfcxbNhBREcnAgBkMn0YGLB3j4govzC5UScOS1EmCQkpmDz5OFauvCFvK1/eGr6+PVGzpqMGIyMi0m1MbtSJw1L0/x4/Doen507cvv2vvK1Pn2pYtao9zM1lGoyMiEj3MblRK/bcELBt212MHHkIHz4kAQCMjAywbFlbDBlSi0XDREQFgMmNWrHmpqgTQmDnzgfyxKZiRVvs3NkTVatyZm8iooLC5EadOIlfkSeRSLB+fSfcuhWC5s1dsXx5O5iaSjUdFhFRkcLkRp1YUFwkhYTEwMnJXP7c2toYN2+OgK0tb3hJRKQJPAurFW+cWZTExiZh4MB9qFFjFUJCYhReY2JDRKQ5TG7UicNSRcbdu//Cw2MtNm++jXfv4tCnzx6kpfH+a0REhQGTG3XisJTOE0Jg3bpbqFNnHf75JwwAYGYmxfDhtaGnx6SWiKgwYM2NWnFYSpfFxCRi5MhD2L79nrytRg0H+Pr2hJubjQYjIyKizJjcqBOHpXRWQEAoPD134smTCHnb6NEeWLy4NYyM+GdERFSY8FNZrTgspYvWr7+FsWOPIDExFQBgYSHD2rUd4elZRcORERFRdpjcqBN7bnSSqalUnti4uzvBx6cHypa11nBURESUEyY36iRYc6OLevWqijNnAiGTGWDBgpaQyfhnQ0RUmPFTWq04LKXthBA4dSoQLVqUUWhfubIDr4YiItISPAurE4eltFpkZDy6dfNFy5ZbsHXrHYXXmNgQEWkPJjf5gcNSWufvv1+jVq3V2LfvHwDAqFGHER4ep+GoiIgoL5jcqIvIPDstD6u2EEJg0aLLaNTIGy9fRgFIvzfU9u3dYWPDWygQEWkj1tyoi3xICuy50RLh4XEYNGg/Dh16LG9r2LAktm/vjpIlLTUYGRERfQ4mN2qTueeGyU1hd+lSEHr12o3Xr6PlbdOnN8Tcuc1haKivwciIiOhzMblRl8zDUrxaqlDz9b2PPn12IzU1/Wdma2uCLVu6ok2bchqOjIiI1IFnYXXhsJTWaNLEBba2JvL/BwSMZGJDRKRD2HOjNhyW0haOjmbYurUbzp59gVmzmsHAgDk+EZEu4ae6unBYqlBKTU3D4sVXslzW/dVXZfDjj18ysSEi0kH8ZFcbDksVNqGhH9C69V+YMuUEBg/eD6FwuT4REekqJjfqwnluCpVTp56jZs1VOHUqEABw+PAT/P33Gw1HRUREBYFnYbXJPCzFnhtNSU1Nw8yZZ9Cy5Rb8+28sAMDJyQynTw9AvXolNBwdEREVBBYUq0vmq6VYUKwRwcEx6NNnN86deylva926LDZv7gp7e1MNRkZERAWJyY26sKBYo44de4r+/fciLCy9cFhfX4KffvoS06Y15E0viYiKGI2fhVesWIHSpUvDyMgI7u7uuHDhQo7L7tmzBy1btoSdnR0sLCxQv359HD9+vACjzQ2HpTTl+vU3aNt2qzyxKVHCAmfPDsL06Y2Y2BARFUEaTW58fHwwceJEfP/99/D390fjxo3Rtm1bBAUFZbv8+fPn0bJlSxw5cgQ3b95E8+bN0bFjR/j7+xdw5NngsJTGeHg4o3fvqgCADh3cEBAwEo0aldJwVEREpCkSocHrY+vWrYvatWtj5cqV8rZKlSqhS5cumDdvnlLbqFKlCry8vDBz5kyllo+OjoalpSWioqJgYWGRp7izFRcGrLRL/3+ZDkDXg+rbNn1SdHQitm+/ixEj3CFhzxkRkc5R5fytsZ6bpKQk3Lx5E61atVJob9WqFS5fvqzUNtLS0hATEwNra+v8CFFF7LkpCMnJqfjmmxM4cOCRQruFhQwjR3owsSEiIs0VFIeFhSE1NRUODg4K7Q4ODggNDVVqG4sWLUJsbCw8PT1zXCYxMRGJiYny59HR0Tku+1kEa27y24sX79Gr1y78/fcbrF/vD3//kXBxsdJ0WEREVMhovKD442/aQgilvn1v374ds2fPho+PD+zt7XNcbt68ebC0tJQ/SpYs+dkxZ49XS+Wnffv+Qa1aq+UT8X34kIRr1zgpHxERZaWxs7CtrS309fWz9NK8ffs2S2/Ox3x8fDB06FD4+vqiRYsWuS47Y8YMREVFyR+vXr367NizxYLifJGYmIKJE4+ha1cfvH+fAAAoU6YYLl8eip49q2g4OiIiKow0ltxIpVK4u7vDz89Pod3Pzw8NGjTIcb3t27dj0KBB2LZtG9q3b//J95HJZLCwsFB45AsOS6nds2cRaNhwA37//W95W48elXHr1gh4eDhrMDIiIirMNDqJ3+TJk9G/f394eHigfv36WLNmDYKCgjBq1CgA6b0ub968webNmwGkJzYDBgzA77//jnr16sl7fYyNjWFpaamx/UjHYSl12rnzPoYNO4jo6PR6KZlMH0uWtMaoUSwaJiKi3Gk0ufHy8kJ4eDjmzp2LkJAQVK1aFUeOHIGLiwsAICQkRGHOm9WrVyMlJQVjx47F2LFj5e0DBw7Exo0bCzp8RRyWUpvIyHiMHHlIntiUL28NX9+eqFnTUcORERGRNtDoPDeakG/z3ES/BNa6pv/fzRPo6KO+bRdB+/f/gy5dfNC7d1WsXt0B5uYyTYdEREQapMr5m/eWUhfeW+qzpKSkwcDgv+PWuXNFXLkyFHXrFucwFBERqYRnYXXJPCzFk7HS4uOTMWLEQQwYsBcfdyLWq1eCiQ0REamMPTdqk/nEzBOyMh4+fAdPz124d+8tAKB5c1cMH+6u4aiIiEjbMblRFw5LqWTz5tsYPfow4uKSAQAmJoYwMuKvIxERfT6eTdSFw1JKiY1NwrhxR7FxY4C8rUoVO/j69kTlynaaC4yIiHQGkxu1Yc/Np9y79xaenjvx8GGYvG3o0Fr444+2MDEx1GBkRESkS5jcqItgzU1OhBDYsMEfX399FPHxKQAAU1NDrF7dAX37VtdwdEREpGuY3KgNJ/HLzb59j+SJTY0aDvD17Qk3NxsNR0VERLqI4yfqwoLiHEkkEmzc2BklS1pg1Ch3XL06jIkNERHlG/bcqA1vnJlBCIHg4BgUL/7fDJI2NiYICBgFa2tjDUZGRERFAbsY1IX3lgIAREcnolev3XB3X4PQ0A8KrzGxISKigsDkRl04LIWbN4NRu/Zq+Prex7//xqJfvz1ZZh0mIiLKb0XzLJwfivA8N0II/Pnn32jQYAOePYsEAFhayjBmzBe8fQIRERU41tyoTdG8FDwyMh5Dhx7A3r3/yNvq1CmOHTu6o3TpYhqMjIiIiiomN2pT9Ialrl17Ay+vXXjx4r28bfLkepg3rwWkUn3NBUZEREUakxt1KWIFxStWXMeECceQkpK+38WKGWHTpi7o2LGChiMjIqKijsmNuoiidSm4paVMntg0aFAS27d3R6lSlhqOioiIiMmNGhWtYam+favj3LmXsLY2xo8/NoehIYehiIiocGByoy46PCyVlibg5/cMrVuXU2hfvboDr4YiIqJCR/e7GAqKjg5LvXsXiw4dtqFNm63w8bmn8BoTGyIiKoyY3KhN5kvBdeOwnj//EjVrrsbRo08BACNHHsL79wkajoqIiCh3unEWLgx0aBK/1NQ0/PTTeTRvvgnBwTEAAAcHU+za5QkrKyMNR0dERJQ71tyojW5M4vfvvx/Qr99enDz5XN725ZelsXVrNzg6mmkwMiIiIuUwuVEXHbi31OnTgejbd4/8hpd6ehLMmtUU33/fGPr62rlPRERU9DC5URctH5basuU2Bg7cJ8/RnJzMsG1bdzRr5qrRuIiIiFTFr+Nqo93DUi1alIGdnSkAoFWrsggIGMXEhoiItBJ7btRFy4elnJzM8ddfXXH9ejCmT28EPT3tS9CIiIgA9tyojxYNS6WkpOHXXy8iMjJeob1ly7L47rvGTGyIiEirsedGbbRjnpvXr6PRu/duXLwYhL//foM9ezw5GR8REemUwnsW1jZa0HNz+PBj1Ky5ChcvBgEADh16DH//UA1HRUREpF5MbtSm8BYUJyen4ptvTqBDh+0ID08fiipVyhIXLgxG7dpOGo6OiIhIvTgspS6FtKD45cv36NVrN65efS1v69y5AjZs6Axra2MNRkZERJQ/mNyoSyEcltq//x8MHrwfkZHp94MyNNTDggUtMX58XdbZEBGRzmJyozaFa1jq8uVX6NLFR/68dGkr+Pj0wBdfFNdgVERERPmv8IyfaLtCNixVv34JeHpWAQB0714Jt26NZGJDRERFAntu1CXzsFQh6LmRSCRYs6YD2rQpi0GDanIYioiIigzNdzHojMw9NwWbSCQkpGDcuCM4fPixQrulpREGD67FxIaIiIoUJjdqo5lhqSdPwtGgwXosX34dAwfuw+vX0QX23kRERIURkxt10cCw1I4d91C79hr5RHyxscm4dSukQN6biIiosGLNjbqIghuWio9PxsSJx7BmzS15W4UKNvD17Ynq1R3y9b2JiIgKOyY3alMww1L//BMGT8+duHv3rbytf//qWLGiPczMpPn2vkRERNqCyY26FMCw1JYttzF69GHExiYDAExMDLF8eTsMGlQzX96PiIhIGzG5UZd8HpYKC4vD118flSc2VarYwde3JypXtlP7exEREWkzFhSrTeYZitV/WG1tTbBhQ2cAwNChtXDt2nAmNkRERNlgz426qPneUkIIpKSkwdBQX97WrVslXLs2jDMNExER5YLJjdqo795SHz4kYdSoQ9DTk2DTpi4Kk/AxsSFSXfqXhRSkpqZqOhQiyoWhoSH09fU/veAnMLlRFzXdW+r27VB4eu7C48fhAIDmzV0xeHCtz42OqMhKSkpCSEgI4uLiNB0KEX2CRCJBiRIlYGZm9lnbYXKjLp85LCWEwJo1NzFhwjEkJqZ/uzQ3l8LcXKauCImKnLS0NAQGBkJfXx/Ozs6QSqW8HQlRISWEwLt37/D69WuUL1/+s3pwmNyoTd57bqKjEzF8+EH4+t6Xt9Wu7QQfnx4oV85aXQESFTlJSUlIS0tDyZIlYWJioulwiOgT7Ozs8OLFCyQnJzO5KRTyOM/NrVsh8PTciWfPIuVtX39dBwsWtIRMxh8PkTro6fHCUCJtoK6eVZ491UWoVlAshMDy5dcxZcoJJCWlD0NZWsqwYUNndOtWKZ+CJCIi0n1MbtRGtWEpIYAjR57IE5svvnCGj08PlC5dLL8CJCIiKhLYV6suKhYUZ1zmXby4OSZProeLF4cwsSEi+kzh4eGwt7fHixcvNB0KfWTZsmXo1KlTgbwXkxu1yX1YSgiBV6+iFNrs7Exx794YLFrUGlLp51/XT0S6YdCgQZBIJJBIJDAwMECpUqUwevRoREZGZln28uXLaNeuHYoVKwYjIyNUq1YNixYtynZOnzNnzqBdu3awsbGBiYkJKleujClTpuDNmzcFsVsFYt68eejYsSNcXV2zvNaqVSvo6+vj6tWrWV5r1qwZJk6cmKV93759WepAkpKSMH/+fNSoUQMmJiawtbVFw4YN4e3tjeTkZHXtShZBQUHo2LEjTE1NYWtri/HjxyMpKSnXdUJDQ9G/f384OjrC1NQUtWvXxq5duxSWefz4MTp37gxbW1tYWFigYcOGOHPmjPz18PBwtGnTBs7OzpDJZChZsiTGjRuH6Oho+TIJCQkYNGgQqlWrBgMDA3Tp0iVLLMOHD8f169dx8eLFzzsQSmByoy65zHMTERGPzp13oG7ddXj7NlbhNSsro4KIjoi0TJs2bRASEoIXL15g3bp1OHjwIMaMGaOwzN69e9G0aVOUKFECZ86cwT///IMJEybg559/Rq9evSAyfS6tXr0aLVq0gKOjI3bv3o0HDx5g1apViIqKwqJFiwpsvz51Mv4c8fHxWL9+PYYNG5bltaCgIFy5cgXjxo3D+vXr8/weSUlJaN26NX799VeMGDECly9fxrVr1zB27Fj8+eefuH///qc3kgepqalo3749YmNjcfHiRezYsQO7d+/GlClTcl2vf//+ePToEQ4cOIC7d++iW7du8PLygr+/v3yZ9u3bIyUlBadPn8bNmzdRs2ZNdOjQAaGhoQDSC/I7d+6MAwcO4PHjx9i4cSNOnjyJUaNGKcRnbGyM8ePHo0WLFtnGIpPJ0KdPH/z5559qOCKfIIqYqKgoAUBERUWpd8O3lgmxEOmP+5vlzZcuBYmSJRcLYLYAZos2bf4SaWlp6n1vIspWfHy8ePDggYiPj9d0KCoZOHCg6Ny5s0Lb5MmThbW1tfz5hw8fhI2NjejWrVuW9Q8cOCAAiB07dgghhHj16pWQSqVi4sSJ2b5fZGRkjrFERkaK4cOHC3t7eyGTyUSVKlXEwYMHhRBCzJo1S9SoUUNh+SVLlggXF5cs+/LLL78IJycn4eLiIqZPny7q1q2b5b2qVasmZs6cKX++YcMGUbFiRSGTyUSFChXE8uXLc4xTCCF2794tbG1ts31t9uzZolevXuLhw4fC3NxcfPjwQeH1pk2bigkTJmRZb+/evSLzqfK3334Tenp64tatW1mWTUpKyrJddTly5IjQ09MTb968kbdt375dyGSyXM9npqamYvPmzQpt1tbWYt26dUIIId69eycAiPPnz8tfj46OFgDEyZMnc9zu77//LkqUKJHta9n9/mY4e/askEqlIi4uLtvXc/ubVeX8zYJitVEclkpLE1i48DK+++4UUlPTX7OxMcbXX9fhJGJEmvSXBxAbWvDva+oI9LuRp1WfP3+OY8eOwdDQUN524sQJhIeHY+rUqVmW79ixI9zc3LB9+3Z4eXlh586dSEpKwrRp07LdvpWVVbbtaWlpaNu2LWJiYvDXX3+hbNmyePDggcrzj5w6dQoWFhbw8/OT9yb9+uuvePbsGcqWLQsAuH//Pu7evSsfMlm7di1mzZqFZcuWoVatWvD398fw4cNhamqKgQMHZvs+58+fh4eHR5Z2IQS8vb2xfPlyVKxYEW5ubvD19cXgwYNV2g8A2Lp1K1q0aIFatbLOHG9oaKjwM8osKCgIlStXznXb/fr1w6pVq7J97cqVK6hatSqcnZ3lba1bt0ZiYiJu3ryJ5s2bZ7teo0aN4OPjg/bt28PKygq+vr5ITExEs2bNAAA2NjaoVKkSNm/ejNq1a0Mmk2H16tVwcHCAu7t7ttsMDg7Gnj170LRp01z3JzseHh5ITk7GtWvX8rS+sjSe3KxYsQILFixASEgIqlSpgqVLl6Jx48Y5Ln/u3DlMnjwZ9+/fh7OzM6ZNm6bQNaYxmbp/30WmYWCHbTh69Km8rXHjUti2rTtKlLDQRHRElCE2FPhQ+GtMDh06BDMzM6SmpiIhIQEAsHjxYvnrjx8/BgBUqpT91BEVK1aUL/PkyRNYWFjAyclJpRhOnjyJa9eu4eHDh3BzcwMAlClTRuV9MTU1xbp16yCVSuVt1atXx7Zt2/DDDz8ASE8avvjiC/n7/Pjjj1i0aBG6desGAChdujQePHiA1atX55jcvHjxQuHkn3k/4uLi0Lp1awDpScT69evzlNw8efJEnhiowtnZGQEBAbkuY2GR8/khNDQUDg4OCm3FihWDVCqVDx9lx8fHB15eXrCxsYGBgQFMTEywd+9eeVIpkUjg5+eHzp07w9zcHHp6enBwcMCxY8eyJL29e/fG/v37ER8fj44dO2LdunW573Q2TE1NYWVlhRcvXuhucuPj44OJEydixYoVaNiwIVavXo22bdviwYMHKFWqVJblAwMD0a5dOwwfPhx//fUXLl26hDFjxsDOzg7du3fXwB5kln611PlnLug9PwjBb9OL+SQS4PvvG2PWrGYwMGCJE5HGmTpqxfs2b94cK1euRFxcHNatW4fHjx/j66+/zrKcUJhjS7E9o5c48/9VERAQgBIlSsgTjryqVq2aQmIDAH379sWGDRvwww8/QAiB7du3ywt63717h1evXmHo0KEYPny4fJ2UlBRYWlrm+D7x8fEwMspax7h+/Xp4eXnBwCD9lNe7d2988803ePToESpUqKDSvuT1WBoYGKBcuXIqr5dZdu/7qXj+97//ITIyEidPnoStrS327duHnj174sKFC6hWrRqEEBgzZgzs7e1x4cIFGBsbY926dejQoQOuX7+ukBAvWbIEs2bNwqNHj/Ddd99h8uTJWLFihcr7YWxsnO/3etNocrN48WIMHTpUXvy1dOlSHD9+HCtXrsS8efOyLL9q1SqUKlUKS5cuBZD+jeXGjRtYuHCh5pMbIbDobH1MO9wSaSI9sbG3N8Vff3VFy5ZlNRsbEf0nj0NDBc3U1FR+Mvzjjz/QvHlzzJkzBz/++CMAyBOOhw8fokGDBlnW/+eff+TDIG5uboiKikJISIhKvTfGxsa5vq6np5clucruaiFTU9MsbX369MH06dNx69YtxMfH49WrV+jVqxeA9OEwIH1oqm7dugrr5TYkZmtrm+WKsoiICOzbtw/JyclYuXKlvD01NRUbNmzAb7/9BiC91yQqSvGKVgB4//69Qo+Km5sbHj58mGMMOfncYSlHR0f8/fffCm2RkZFITk7O0qOT4dmzZ1i2bBnu3buHKlWqAABq1KiBCxcuYPny5Vi1ahVOnz6NQ4cOITIyUr6fK1asgJ+fHzZt2oTp06crxODo6IiKFSvCxsYGjRs3xg8//KByj2BERATs7OxUWkdVGutKSEpKws2bN9GqVSuF9latWuHy5cvZrnPlypUsy7du3Ro3btzI8fK7xMREREdHKzzyh4CtaRzSRPohbd7cFQEBI5nYEJFazJo1CwsXLkRwcDCA9M9Ka2vrbK90OnDgAJ48eYLevXsDAHr06AGpVIr58+dnu+33799n2169enW8fv1aPrz1MTs7O4SGhiokOJ8aeslQokQJNGnSBFu3bpXXsWScpB0cHFC8eHE8f/4c5cqVU3iULl06x23WqlULDx48UGjbunUrSpQogdu3byMgIED+WLp0KTZt2oSUlBQA6cN4N25kTXyvX7+u0LvTp08fnDx5UuFqowwpKSmIjY3N0g78NyyV22Pu3Lk57lv9+vVx7949hISEyNtOnDgBmUyWY21MRu/Ix7cf0dfXlyeQOS2jp6cnXyY7GT/zxMTEHJfJzrNnz5CQkJBtzZJafbLkOJ+8efNGABCXLl1SaP/555+Fm5tbtuuUL19e/Pzzzwptly5dEgBEcHBwtuvMmjVLIL3aV+Gh9qulri8UYiHEkDqdxOxxy0VKSqp6t09EKtOlq6WEEMLd3V2MHTtW/nznzp1CX19fDB8+XNy+fVsEBgaKdevWiWLFiokePXooXJm5fPlyIZFIxJAhQ8TZs2fFixcvxMWLF8WIESPE5MmTc4ylWbNmomrVquLEiRPi+fPn4siRI+Lo0aNCCCEePHggJBKJ+PXXX8XTp0/FsmXLRLFixbK9Wio7a9asEc7OzsLW1lZs2bJF4bW1a9cKY2NjsXTpUvHo0SNx584dsWHDBrFo0aIcY71z544wMDAQERER8rYaNWqIb7/9Nsuy0dHRQiaTiX379gkhhAgMDBTGxsZizJgxIiAgQDx69EgsW7ZMyGQy4evrK18vISFBNG7cWBQrVkwsW7ZMBAQEiGfPngkfHx9Ru3Zt4e/vn2N8nyMlJUVUrVpVfPXVV+LWrVvi5MmTokSJEmLcuHHyZV6/fi0qVKgg/v77byFE+tVb5cqVE40bNxZ///23ePr0qVi4cKGQSCTi8OHDQoj0q6UyrrrL2O+pU6cKQ0NDERAQIIQQ4vDhw2LDhg3i7t27IjAwUBw+fFhUqVJFNGzYUCHG+/fvC39/f9GxY0fRrFkz4e/vn+V4eHt7izJlyuS4n+q6Wkrjyc3ly5cV2n/66SdRoUKFbNcpX768+OWXXxTaLl68KACIkJCQbNdJSEgQUVFR8serV6/yJ7mJeCLEk30i7dFuIaJfq3fbRJQnupbcbN26VUilUhEUFCRvO3/+vGjTpo2wtLQUUqlUVK5cWSxcuFCkpKRkWd/Pz0+0bt1aFCtWTBgZGYmKFSuKqVOn5vjlUAghwsPDxeDBg4WNjY0wMjISVatWFYcOHZK/vnLlSlGyZElhamoqBgwYIH7++Welk5vIyEghk8mEiYmJiImJyXZ/a9asKaRSqShWrJho0qSJ2LNnT46xCiFEvXr1xKpVq4QQQty4cUMAENeuXct22Y4dO4qOHTvKn9+4cUO0bt1a2NvbCwsLC+Hh4SG2b9+eZb2EhAQxb948Ua1aNWFkZCSsra1Fw4YNxcaNG0VycnKu8X2Oly9fivbt2wtjY2NhbW0txo0bJxISEuSvBwYGCgDizJkz8rbHjx+Lbt26CXt7e2FiYiKqV6+e5dLw69evi1atWglra2thbm4u6tWrJ44cOSJ//fTp06J+/frC0tJSGBkZifLly4tvv/02yxQCLi4u2XYmZNaqVSsxb968HPdRXcmNRIgcqtHyWVJSEkxMTLBz50507dpV3j5hwgQEBATg3LlzWdZp0qQJatWqhd9//13etnfvXnh6eiIuLi7HS/Ayi46OhqWlJaKionKtTCci7ZeQkIDAwECULl0620JT0j1HjhzB1KlTce/ePd4NvpC5d+8evvrqKzx+/DjHwvDc/mZVOX9r7CcvlUrh7u4OPz8/hXY/P79si+OA9DHHj5c/ceIEPDw8lEpsiIhIt7Vr1w4jR47UqVtK6Irg4GBs3rw51yve1EWjV0tNnjwZ/fv3h4eHB+rXr481a9YgKChIPm/NjBkz8ObNG2zevBkAMGrUKCxbtgyTJ0/G8OHDceXKFaxfvx7bt2/X5G4QEVEhMmHCBE2HQNn4+IKg/KTR5MbLywvh4eGYO3cuQkJCULVqVRw5cgQuLi4AgJCQEAQFBcmXL126NI4cOYJJkyZh+fLlcHZ2xh9//KH5y8CJiIio0NBYzY2msOaGqOhgzQ2RdtH6mhsiooJSxL7DEWktdf2tMrkhIp2VcaFBfk/1TkTqkZSUBCD3maiVofEbZxIR5Rd9fX1YWVnh7du3AAATE5M83ReIiPJfWloa3r17BxMTE/l9wPKKyQ0R6TRHx/QbVmYkOERUeOnp6aFUqVKf/SWEyQ0R6TSJRAInJyfY29vneA86IiocpFKpWiZfZHJDREWCvr7+Z4/jE5F2YEExERER6RQmN0RERKRTmNwQERGRTilyNTcZEwRFR0drOBIiIiJSVsZ5W5mJ/opcchMTEwMAKFmypIYjISIiIlXFxMR88s7iRe7eUmlpaQgODoa5ubnaJ/OKjo5GyZIl8erVK963Kh/xOBcMHueCweNccHisC0Z+HWchBGJiYuDs7PzJy8WLXM+Nnp4eSpQoka/vYWFhwT+cAsDjXDB4nAsGj3PB4bEuGPlxnD/VY5OBBcVERESkU5jcEBERkU5hcqNGMpkMs2bNgkwm03QoOo3HuWDwOBcMHueCw2NdMArDcS5yBcVERESk29hzQ0RERDqFyQ0RERHpFCY3REREpFOY3BAREZFOYXKjohUrVqB06dIwMjKCu7s7Lly4kOvy586dg7u7O4yMjFCmTBmsWrWqgCLVbqoc5z179qBly5aws7ODhYUF6tevj+PHjxdgtNpL1d/nDJcuXYKBgQFq1qyZvwHqCFWPc2JiIr7//nu4uLhAJpOhbNmy2LBhQwFFq71UPc5bt25FjRo1YGJiAicnJwwePBjh4eEFFK12On/+PDp27AhnZ2dIJBLs27fvk+to5DwoSGk7duwQhoaGYu3ateLBgwdiwoQJwtTUVLx8+TLb5Z8/fy5MTEzEhAkTxIMHD8TatWuFoaGh2LVrVwFHrl1UPc4TJkwQv/32m7h27Zp4/PixmDFjhjA0NBS3bt0q4Mi1i6rHOcP79+9FmTJlRKtWrUSNGjUKJlgtlpfj3KlTJ1G3bl3h5+cnAgMDxd9//y0uXbpUgFFrH1WP84ULF4Senp74/fffxfPnz8WFCxdElSpVRJcuXQo4cu1y5MgR8f3334vdu3cLAGLv3r25Lq+p8yCTGxXUqVNHjBo1SqGtYsWKYvr06dkuP23aNFGxYkWFtpEjR4p69erlW4y6QNXjnJ3KlSuLOXPmqDs0nZLX4+zl5SX+97//iVmzZjG5UYKqx/no0aPC0tJShIeHF0R4OkPV47xgwQJRpkwZhbY//vhDlChRIt9i1DXKJDeaOg9yWEpJSUlJuHnzJlq1aqXQ3qpVK1y+fDnbda5cuZJl+datW+PGjRtITk7Ot1i1WV6O88fS0tIQExMDa2vr/AhRJ+T1OHt7e+PZs2eYNWtWfoeoE/JynA8cOAAPDw/Mnz8fxYsXh5ubG6ZOnYr4+PiCCFkr5eU4N2jQAK9fv8aRI0cghMC///6LXbt2oX379gURcpGhqfNgkbtxZl6FhYUhNTUVDg4OCu0ODg4IDQ3Ndp3Q0NBsl09JSUFYWBicnJzyLV5tlZfj/LFFixYhNjYWnp6e+RGiTsjLcX7y5AmmT5+OCxcuwMCAHx3KyMtxfv78OS5evAgjIyPs3bsXYWFhGDNmDCIiIlh3k4O8HOcGDRpg69at8PLyQkJCAlJSUtCpUyf8+eefBRFykaGp8yB7blQkkUgUngshsrR9avns2kmRqsc5w/bt2zF79mz4+PjA3t4+v8LTGcoe59TUVPTp0wdz5syBm5tbQYWnM1T5fU5LS4NEIsHWrVtRp04dtGvXDosXL8bGjRvZe/MJqhznBw8eYPz48Zg5cyZu3ryJY8eOITAwEKNGjSqIUIsUTZwH+fVLSba2ttDX18/yLeDt27dZstIMjo6O2S5vYGAAGxubfItVm+XlOGfw8fHB0KFDsXPnTrRo0SI/w9R6qh7nmJgY3LhxA/7+/hg3bhyA9JOwEAIGBgY4ceIEvvzyywKJXZvk5ffZyckJxYsXh6WlpbytUqVKEELg9evXKF++fL7GrI3ycpznzZuHhg0b4ptvvgEAVK9eHaampmjcuDF++ukn9qyriabOg+y5UZJUKoW7uzv8/PwU2v38/NCgQYNs16lfv36W5U+cOAEPDw8YGhrmW6zaLC/HGUjvsRk0aBC2bdvGMXMlqHqcLSwscPfuXQQEBMgfo0aNQoUKFRAQEIC6desWVOhaJS+/zw0bNkRwcDA+fPggb3v8+DH09PRQokSJfI1XW+XlOMfFxUFPT/EUqK+vD+C/ngX6fBo7D+ZrubKOybjUcP369eLBgwdi4sSJwtTUVLx48UIIIcT06dNF//795ctnXAI3adIk8eDBA7F+/XpeCq4EVY/ztm3bhIGBgVi+fLkICQmRP96/f6+pXdAKqh7nj/FqKeWoepxjYmJEiRIlRI8ePcT9+/fFuXPnRPny5cWwYcM0tQtaQdXj7O3tLQwMDMSKFSvEs2fPxMWLF4WHh4eoU6eOpnZBK8TExAh/f3/h7+8vAIjFixcLf39/+SX3heU8yORGRcuXLxcuLi5CKpWK2rVri3PnzslfGzhwoGjatKnC8mfPnhW1atUSUqlUuLq6ipUrVxZwxNpJlePctGlTASDLY+DAgQUfuJZR9fc5MyY3ylP1OD98+FC0aNFCGBsbixIlSojJkyeLuLi4Ao5a+6h6nP/44w9RuXJlYWxsLJycnETfvn3F69evCzhq7XLmzJlcP28Ly3lQIgT734iIiEh3sOaGiIiIdAqTGyIiItIpTG6IiIhIpzC5ISIiIp3C5IaIiIh0CpMbIiIi0ilMboiIiEinMLkhIgUbN26ElZWVpsPIM1dXVyxdujTXZWbPno2aNWsWSDxEVPCY3BDpoEGDBkEikWR5PH36VNOhYePGjQoxOTk5wdPTE4GBgWrZ/vXr1zFixAj5c4lEgn379iksM3XqVJw6dUot75eTj/fTwcEBHTt2xP3791XejjYnm0SawOSGSEe1adMGISEhCo/SpUtrOiwA6TfiDAkJQXBwMLZt24aAgAB06tQJqampn71tOzs7mJiY5LqMmZlZvt6ROEPm/Tx8+DBiY2PRvn17JCUl5ft7ExVlTG6IdJRMJoOjo6PCQ19fH4sXL0a1atVgamqKkiVLYsyYMQp3oP7Y7du30bx5c5ibm8PCwgLu7u64ceOG/PXLly+jSZMmMDY2RsmSJTF+/HjExsbmGptEIoGjoyOcnJzQvHlzzJo1C/fu3ZP3LK1cuRJly5aFVCpFhQoVsGXLFoX1Z8+ejVKlSkEmk8HZ2Rnjx4+Xv5Z5WMrV1RUA0LVrV0gkEvnzzMNSx48fh5GREd6/f6/wHuPHj0fTpk3Vtp8eHh6YNGkSXr58iUePHsmXye3ncfbsWQwePBhRUVHyHqDZs2cDAJKSkjBt2jQUL14cpqamqFu3Ls6ePZtrPERFBZMboiJGT08Pf/zxB+7du4dNmzbh9OnTmDZtWo7L9+3bFyVKlMD169dx8+ZNTJ8+HYaGhgCAu3fvonXr1ujWrRvu3LkDHx8fXLx4EePGjVMpJmNjYwBAcnIy9u7diwkTJmDKlCm4d+8eRo4cicGDB+PMmTMAgF27dmHJkiVYvXo1njx5gn379qFatWrZbvf69esAAG9vb4SEhMifZ9aiRQtYWVlh9+7d8rbU1FT4+vqib9++atvP9+/fY9u2bQAgP35A7j+PBg0aYOnSpfIeoJCQEEydOhUAMHjwYFy6dAk7duzAnTt30LNnT7Rp0wZPnjxROiYinZXvt+YkogI3cOBAoa+vL0xNTeWPHj16ZLusr6+vsLGxkT/39vYWlpaW8ufm5uZi48aN2a7bv39/MWLECIW2CxcuCD09PREfH5/tOh9v/9WrV6JevXqiRIkSIjExUTRo0EAMHz5cYZ2ePXuKdu3aCSGEWLRokXBzcxNJSUnZbt/FxUUsWbJE/hyA2Lt3r8IyH9/RfPz48eLLL7+UPz9+/LiQSqUiIiLis/YTgDA1NRUmJibyuyd36tQp2+UzfOrnIYQQT58+FRKJRLx580ah/auvvhIzZszIdftERYGBZlMrIsovzZs3x8qVK+XPTU1NAQBnzpzBL7/8ggcPHiA6OhopKSlISEhAbGysfJnMJk+ejGHDhmHLli1o0aIFevbsibJlywIAbt68iadPn2Lr1q3y5YUQSEtLQ2BgICpVqpRtbFFRUTAzM4MQAnFxcahduzb27NkDqVSKhw8fKhQEA0DDhg3x+++/AwB69uyJpUuXokyZMmjTpg3atWuHjh07wsAg7x9nffv2Rf369REcHAxnZ2ds3boV7dq1Q7FixT5rP83NzXHr1i2kpKTg3LlzWLBgAVatWqWwjKo/DwC4desWhBBwc3NTaE9MTCyQWiKiwo7JDZGOMjU1Rbly5RTaXr58iXbt2mHUqFH48ccfYW1tjYsXL2Lo0KFITk7OdjuzZ89Gnz59cPjwYRw9ehSzZs3Cjh070LVrV6SlpWHkyJEKNS8ZSpUqlWNsGSd9PT09ODg4ZDmJSyQShedCCHlbyZIl8ejRI/j5+eHkyZMYM2YMFixYgHPnzikM96iiTp06KFu2LHbs2IHRo0dj79698Pb2lr+e1/3U09OT/wwqVqyI0NBQeHl54fz58wDy9vPIiEdfXx83b96Evr6+wmtmZmYq7TuRLmJyQ1SE3LhxAykpKVi0aBH09NJL7nx9fT+5npubG9zc3DBp0iT07t0b3t7e6Nq1K2rXro379+9nSaI+JfNJ/2OVKlXCxYsXMWDAAHnb5cuXFXpHjI2N0alTJ3Tq1Aljx45FxYoVcffuXdSuXTvL9gwNDZW6CqtPnz7YunUrSpQoAT09PbRv317+Wl7382OTJk3C4sWLsXfvXnTt2lWpn4dUKs0Sf61atZCamoq3b9+icePGnxUTkS5iQTFREVK2bFmkpKTgzz//xPPnz7Fly5YswySZxcfHY9y4cTh79ixevnyJS5cu4fr16/JE49tvv8WVK1cwduxYBAQE4MmTJzhw4AC+/vrrPMf4zTffYOPGjVi1ahWePHmCxYsXY8+ePfJC2o0bN2L9+vW4d++efB+MjY3h4uKS7fZcXV1x6tQphIaGIjIyMsf37du3L27duoWff/4ZPXr0gJGRkfw1de2nhYUFhg0bhlmzZkEIodTPw9XVFR8+fMCpU6cQFhaGuLg4uLm5oW/fvhgwYAD27NmDwMBAXL9+Hb/99huOHDmiUkxEOkmTBT9ElD8GDhwoOnfunO1rixcvFk5OTsLY2Fi0bt1abN68WQAQkZGRQgjFAtbExETRq1cvUbJkSSGVSoWzs7MYN26cQhHttWvXRMuWLYWZmZkwNTUV1atXFz///HOOsWVXIPuxFStWiDJlyghDQ0Ph5uYmNm/eLH9t7969om7dusLCwkKYmpqKevXqiZMnT8pf/7ig+MCBA6JcuXLCwMBAuLi4CCGyFhRn+OKLLwQAcfr06SyvqWs/X758KQwMDISPj48Q4tM/DyGEGDVqlLCxsREAxKxZs4QQQiQlJYmZM2cKV1dXYWhoKBwdHUXXrl3FnTt3coyJqKiQCCGEZtMrIiIiIvXhsBQRERHpFCY3REREpFOY3BAREZFOYXJDREREOoXJDREREekUJjdERESkU5jcEBERkU5hckNEREQ6hckNERER6RQmN0RERKRTmNwQERGRTmFyQ0RERDrl/wD/XCiPTE5YXQAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Set batch size and number of chunks\n",
    "batch_size = 8\n",
    "num_chunks = 30\n",
    "\n",
    "# Split test data and demographic variables into chunks\n",
    "chunk_size = len(test_input) // num_chunks\n",
    "test_chunks = [test_input[i:i+chunk_size] for i in range(0, len(test_input), chunk_size)]\n",
    "dv_chunks = [test_dv[i:i+chunk_size] for i in range(0, len(test_dv), chunk_size)]\n",
    "\n",
    "# Get predictions for each chunk of data\n",
    "predictions = []\n",
    "for i, chunk in enumerate(test_chunks):\n",
    "    dv_chunk = dv_chunks[i]\n",
    "    chunk_predictions = model.predict([chunk, dv_chunk], batch_size=batch_size)\n",
    "    predictions.append(chunk_predictions)\n",
    "\n",
    "# Concatenate predictions from all chunks\n",
    "predictions = np.concatenate(predictions, axis=0)\n",
    "\n",
    "# Convert the continuous predictions to binary classes\n",
    "preds = np.copy(predictions)\n",
    "preds = [1 if y > best_threshold else 0 for y in preds]\n",
    "\n",
    "# Compute the confusion matrix\n",
    "cm = confusion_matrix(test_target, preds)\n",
    "\n",
    "# Create a heatmap of the confusion matrix\n",
    "sns.heatmap(cm, annot=True, fmt='g', cmap='Blues')\n",
    "\n",
    "# Add labels and title\n",
    "plt.xlabel('Predicted labels')\n",
    "plt.ylabel('True labels')\n",
    "plt.title('Confusion Matrix')\n",
    "\n",
    "# Show the plot\n",
    "plt.show()\n",
    "\n",
    "\n",
    "# Compute the AUC\n",
    "auc_score = roc_auc_score(test_target, preds)\n",
    "print(\"AUC: {:.4f}\".format(auc_score))\n",
    "\n",
    "# Compute the precision with recall set to 0.9\n",
    "precision, recall, thresholds = precision_recall_curve(test_target, preds)\n",
    "precision_at_recall_09 = np.interp(0.9, recall[::-1], precision[::-1])\n",
    "print(\"Precision with recall set to 0.9: {:.4f}\".format(precision_at_recall_09))\n",
    "\n",
    "# Plot the precision-recall curve with recall set to 0.9\n",
    "plt.plot(recall, precision, label='Precision-Recall curve')\n",
    "plt.scatter(0.9, precision_at_recall_09, s=50, color='red', label='Precision at Recall=0.9')\n",
    "plt.xlabel('Recall')\n",
    "plt.ylabel('Precision')\n",
    "plt.title('Precision-Recall curve with Recall set to 0.9')\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n",
    "# Compute the false positive rate and true positive rate\n",
    "fpr, tpr, thresholds = roc_curve(test_target, preds)\n",
    "\n",
    "# Compute the area under the curve (AUC)\n",
    "roc_auc_curve = auc(fpr, tpr)\n",
    "\n",
    "# Plot the ROC curve\n",
    "plt.figure()\n",
    "plt.plot(fpr, tpr, color='darkorange', lw=2, label='ROC curve (AUC = {:.4f})'.format(roc_auc_curve))\n",
    "plt.plot([0, 1], [0, 1], color='navy', lw=2, linestyle='--')\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.title('Receiver operating characteristic (ROC) curve')\n",
    "plt.legend(loc='lower right')\n",
    "plt.show()\n"
   ]
  }
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