cnn_model copy_orig_60k.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-06-29 16:53:29.534203: 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",
      "2023-06-29 16:53:29.648340: 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 os\n",
    "os.environ['TF_GPU_ALLOCATOR'] = 'cuda_malloc_async'\n",
    "import tensorflow as tf\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "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": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "os.environ['CUDA_VISIBLE_DEVICES'] = ''\n",
    "import tensorflow as tf\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/local-scratch/localhome/rka73/local_rk_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",
      "/local-scratch/localhome/rka73/local_rk_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",
      "/local-scratch/localhome/rka73/local_rk_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=60000, start_year=2008, end_year=2010, random_state = 42, col_num=6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:There are non-GPU devices in `tf.distribute.Strategy`, not using nccl allreduce.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-06-29 17:03:24.193637: E tensorflow/compiler/xla/stream_executor/cuda/cuda_driver.cc:267] failed call to cuInit: CUDA_ERROR_NO_DEVICE: no CUDA-capable device is detected\n",
      "2023-06-29 17:03:24.194224: I tensorflow/compiler/xla/stream_executor/cuda/cuda_diagnostics.cc:169] retrieving CUDA diagnostic information for host: bdhub-its54313\n",
      "2023-06-29 17:03:24.194424: I tensorflow/compiler/xla/stream_executor/cuda/cuda_diagnostics.cc:176] hostname: bdhub-its54313\n",
      "2023-06-29 17:03:24.195334: I tensorflow/compiler/xla/stream_executor/cuda/cuda_diagnostics.cc:200] libcuda reported version is: 530.30.2\n",
      "2023-06-29 17:03:24.196036: I tensorflow/compiler/xla/stream_executor/cuda/cuda_diagnostics.cc:204] kernel reported version is: 530.30.2\n",
      "2023-06-29 17:03:24.196040: I tensorflow/compiler/xla/stream_executor/cuda/cuda_diagnostics.cc:310] kernel version seems to match DSO: 530.30.2\n",
      "2023-06-29 17:03:24.204921: 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"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Using MirroredStrategy with devices ('/job:localhost/replica:0/task:0/device:CPU:0',)\n",
      "Epoch 1/10\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-06-29 17:04:16.563494: W tensorflow/core/framework/dataset.cc:769] Input of GeneratorDatasetOp::Dataset will not be optimized because the dataset does not implement the AsGraphDefInternal() method needed to apply optimizations.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1200/1200 [==============================] - ETA: 0s - loss: 1.1009 - accuracy: 0.8734"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-06-29 17:07:16.242078: W tensorflow/core/framework/dataset.cc:769] Input of GeneratorDatasetOp::Dataset will not be optimized because the dataset does not implement the AsGraphDefInternal() method needed to apply optimizations.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1200/1200 [==============================] - 182s 149ms/step - loss: 1.1009 - accuracy: 0.8734 - val_loss: 0.0083 - val_accuracy: 0.9990\n",
      "Epoch 2/10\n",
      "1200/1200 [==============================] - ETA: 0s - loss: 0.0054 - accuracy: 0.9990"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-06-29 17:08:50.765804: W tensorflow/core/framework/dataset.cc:769] Input of GeneratorDatasetOp::Dataset will not be optimized because the dataset does not implement the AsGraphDefInternal() method needed to apply optimizations.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1200/1200 [==============================] - 94s 79ms/step - loss: 0.0054 - accuracy: 0.9990 - val_loss: 0.0085 - val_accuracy: 0.9990\n",
      "Epoch 3/10\n",
      "1200/1200 [==============================] - ETA: 0s - loss: 0.0033 - accuracy: 0.9993"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-06-29 17:10:25.423775: W tensorflow/core/framework/dataset.cc:769] Input of GeneratorDatasetOp::Dataset will not be optimized because the dataset does not implement the AsGraphDefInternal() method needed to apply optimizations.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1200/1200 [==============================] - 95s 79ms/step - loss: 0.0033 - accuracy: 0.9993 - val_loss: 0.0086 - val_accuracy: 0.9990\n",
      "Epoch 4/10\n",
      "1200/1200 [==============================] - ETA: 0s - loss: 0.0018 - accuracy: 0.9996"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-06-29 17:12:00.134878: W tensorflow/core/framework/dataset.cc:769] Input of GeneratorDatasetOp::Dataset will not be optimized because the dataset does not implement the AsGraphDefInternal() method needed to apply optimizations.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1200/1200 [==============================] - 95s 79ms/step - loss: 0.0018 - accuracy: 0.9996 - val_loss: 0.0091 - val_accuracy: 0.9990\n",
      "Epoch 5/10\n",
      "1200/1200 [==============================] - ETA: 0s - loss: 0.0012 - accuracy: 0.9996"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-06-29 17:13:34.904050: W tensorflow/core/framework/dataset.cc:769] Input of GeneratorDatasetOp::Dataset will not be optimized because the dataset does not implement the AsGraphDefInternal() method needed to apply optimizations.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1200/1200 [==============================] - 95s 79ms/step - loss: 0.0012 - accuracy: 0.9996 - val_loss: 0.0094 - val_accuracy: 0.9991\n",
      "Epoch 6/10\n",
      "1200/1200 [==============================] - ETA: 0s - loss: 7.5712e-04 - accuracy: 0.9997"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-06-29 17:15:09.659631: W tensorflow/core/framework/dataset.cc:769] Input of GeneratorDatasetOp::Dataset will not be optimized because the dataset does not implement the AsGraphDefInternal() method needed to apply optimizations.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1200/1200 [==============================] - 95s 79ms/step - loss: 7.5712e-04 - accuracy: 0.9997 - val_loss: 0.0096 - val_accuracy: 0.9991\n",
      "Epoch 7/10\n",
      "1200/1200 [==============================] - ETA: 0s - loss: 5.0451e-04 - accuracy: 0.9999"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-06-29 17:16:44.353549: W tensorflow/core/framework/dataset.cc:769] Input of GeneratorDatasetOp::Dataset will not be optimized because the dataset does not implement the AsGraphDefInternal() method needed to apply optimizations.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1200/1200 [==============================] - 95s 79ms/step - loss: 5.0451e-04 - accuracy: 0.9999 - val_loss: 0.0106 - val_accuracy: 0.9992\n",
      "Epoch 8/10\n",
      "1200/1200 [==============================] - ETA: 0s - loss: 3.0325e-04 - accuracy: 0.9999"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-06-29 17:18:19.067713: W tensorflow/core/framework/dataset.cc:769] Input of GeneratorDatasetOp::Dataset will not be optimized because the dataset does not implement the AsGraphDefInternal() method needed to apply optimizations.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1200/1200 [==============================] - 95s 79ms/step - loss: 3.0325e-04 - accuracy: 0.9999 - val_loss: 0.0118 - val_accuracy: 0.9992\n",
      "Epoch 9/10\n",
      "1200/1200 [==============================] - ETA: 0s - loss: 1.4577e-04 - accuracy: 1.0000"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-06-29 17:19:53.841188: W tensorflow/core/framework/dataset.cc:769] Input of GeneratorDatasetOp::Dataset will not be optimized because the dataset does not implement the AsGraphDefInternal() method needed to apply optimizations.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1200/1200 [==============================] - 95s 79ms/step - loss: 1.4577e-04 - accuracy: 1.0000 - val_loss: 0.0129 - val_accuracy: 0.9992\n",
      "Epoch 10/10\n",
      "1200/1200 [==============================] - ETA: 0s - loss: 7.5970e-05 - accuracy: 1.0000"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-06-29 17:21:28.610585: W tensorflow/core/framework/dataset.cc:769] Input of GeneratorDatasetOp::Dataset will not be optimized because the dataset does not implement the AsGraphDefInternal() method needed to apply optimizations.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1200/1200 [==============================] - 95s 79ms/step - loss: 7.5970e-05 - accuracy: 1.0000 - val_loss: 0.0143 - val_accuracy: 0.9992\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": 6,
   "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": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "160/160 [==============================] - 1s 5ms/step\n",
      "160/160 [==============================] - 1s 5ms/step\n",
      "160/160 [==============================] - 1s 5ms/step\n",
      "160/160 [==============================] - 1s 5ms/step\n",
      "160/160 [==============================] - 1s 6ms/step\n",
      "160/160 [==============================] - 1s 5ms/step\n",
      " 23/160 [===>..........................] - ETA: 0s"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-06-29 17:22:00.832101: W tensorflow/core/framework/dataset.cc:769] Input of GeneratorDatasetOp::Dataset will not be optimized because the dataset does not implement the AsGraphDefInternal() method needed to apply optimizations.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "160/160 [==============================] - 1s 5ms/step\n",
      "160/160 [==============================] - 1s 5ms/step\n",
      "160/160 [==============================] - 1s 5ms/step\n",
      "160/160 [==============================] - 1s 5ms/step\n",
      "160/160 [==============================] - 1s 5ms/step\n",
      "160/160 [==============================] - 1s 5ms/step\n",
      "160/160 [==============================] - 1s 5ms/step\n",
      "160/160 [==============================] - 1s 5ms/step\n",
      " 31/160 [====>.........................] - ETA: 0s"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-06-29 17:22:34.077480: W tensorflow/core/framework/dataset.cc:769] Input of GeneratorDatasetOp::Dataset will not be optimized because the dataset does not implement the AsGraphDefInternal() method needed to apply optimizations.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "160/160 [==============================] - 1s 5ms/step\n",
      "160/160 [==============================] - 1s 5ms/step\n",
      "160/160 [==============================] - 1s 5ms/step\n",
      "160/160 [==============================] - 1s 5ms/step\n",
      "160/160 [==============================] - 1s 5ms/step\n",
      "160/160 [==============================] - 1s 5ms/step\n",
      "160/160 [==============================] - 1s 5ms/step\n",
      " 34/160 [=====>........................] - ETA: 0s"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-06-29 17:23:10.415861: W tensorflow/core/framework/dataset.cc:769] Input of GeneratorDatasetOp::Dataset will not be optimized because the dataset does not implement the AsGraphDefInternal() method needed to apply optimizations.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "160/160 [==============================] - 1s 5ms/step\n",
      "160/160 [==============================] - 1s 5ms/step\n",
      "160/160 [==============================] - 1s 5ms/step\n",
      "160/160 [==============================] - 1s 5ms/step\n",
      "160/160 [==============================] - 1s 5ms/step\n",
      "160/160 [==============================] - 1s 5ms/step\n",
      "160/160 [==============================] - 1s 5ms/step\n",
      " 30/160 [====>.........................] - ETA: 0s"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-06-29 17:23:40.995052: W tensorflow/core/framework/dataset.cc:769] Input of GeneratorDatasetOp::Dataset will not be optimized because the dataset does not implement the AsGraphDefInternal() method needed to apply optimizations.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "160/160 [==============================] - 1s 5ms/step\n",
      "160/160 [==============================] - 1s 5ms/step\n",
      "Best threshold: 0.03\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": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 1s 1s/step\n",
      "Prediction: [[6.1659310e-05]\n",
      " [5.1290810e-10]\n",
      " [9.5801420e-09]\n",
      " [9.9338621e-01]\n",
      " [1.9099870e-08]\n",
      " [7.7228498e-01]\n",
      " [9.8229688e-01]\n",
      " [1.8641487e-05]\n",
      " [7.7228498e-01]\n",
      " [3.7071710e-10]\n",
      " [9.6425939e-01]\n",
      " [4.9651401e-08]\n",
      " [9.9115515e-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": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "50/50 [==============================] - 0s 5ms/step\n",
      "50/50 [==============================] - 0s 5ms/step\n",
      "50/50 [==============================] - 0s 5ms/step\n",
      "50/50 [==============================] - 0s 5ms/step\n",
      "50/50 [==============================] - 0s 5ms/step\n",
      "50/50 [==============================] - 0s 5ms/step\n",
      "50/50 [==============================] - 0s 5ms/step\n",
      "50/50 [==============================] - 0s 5ms/step\n",
      "50/50 [==============================] - 0s 5ms/step\n",
      "50/50 [==============================] - 0s 5ms/step\n",
      "50/50 [==============================] - 0s 5ms/step\n",
      "50/50 [==============================] - 0s 5ms/step\n",
      "50/50 [==============================] - 0s 5ms/step\n",
      "50/50 [==============================] - 0s 5ms/step\n",
      "50/50 [==============================] - 0s 5ms/step\n",
      "50/50 [==============================] - 0s 5ms/step\n",
      "50/50 [==============================] - 0s 5ms/step\n",
      "50/50 [==============================] - 0s 5ms/step\n",
      "50/50 [==============================] - 0s 5ms/step\n",
      "32/50 [==================>...........] - ETA: 0s"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-06-29 17:24:11.791443: W tensorflow/core/framework/dataset.cc:769] Input of GeneratorDatasetOp::Dataset will not be optimized because the dataset does not implement the AsGraphDefInternal() method needed to apply optimizations.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "50/50 [==============================] - 0s 5ms/step\n",
      "50/50 [==============================] - 0s 5ms/step\n",
      "50/50 [==============================] - 0s 5ms/step\n",
      "50/50 [==============================] - 0s 5ms/step\n",
      "50/50 [==============================] - 0s 5ms/step\n",
      "50/50 [==============================] - 0s 5ms/step\n",
      "50/50 [==============================] - 0s 5ms/step\n",
      "50/50 [==============================] - 0s 5ms/step\n",
      "50/50 [==============================] - 0s 5ms/step\n",
      "50/50 [==============================] - 0s 5ms/step\n",
      "50/50 [==============================] - 0s 5ms/step\n"
     ]
    },
    {
     "data": {
      "image/png": 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sWKGBAwfq6NGjCgoKKtd7oNMCAICHyM/P18mTJ12O/Pz8S5orMzNTWVlZ6tKli/Oc3W5X+/bttXnzZknSzp07VVhY6DImIiJC0dHRzjFbtmyRw+FwBhZJatOmjRwOh8uY6OhoZ2CRpK5duyo/P187d+4sd82EFgAALGaz2Uw5UlNTnftGzh2pqamXVFNWVpYkKSwszOV8WFiY81pWVpZ8fX1VvXr1i44JDQ0tNX9oaKjLmPNfp3r16vL19XWOKQ/uHgIAwGJm3T2UnJys0aNHu5yz2+2XNef5tRmG8Zf1nj+mrPGXMuav0GkBAMBD2O12BQUFuRyXGlrCw8MlqVSn4+jRo86uSHh4uAoKCpSdnX3RMb/++mup+Y8dO+Yy5vzXyc7OVmFhYakOzMUQWgAAsJhZy0Nmqlu3rsLDw7V+/XrnuYKCAm3cuFFt27aVJMXExMjHx8dlzJEjR7R7927nmLi4OOXk5Gj79u3OMdu2bVNOTo7LmN27d+vIkSPOMevWrZPdbldMTEy5a2Z5CAAAq7npu+VOnz6tH3/80fk4MzNTGRkZCg4OVq1atZSUlKSUlBQ1aNBADRo0UEpKigICApSQkCBJcjgcGjRokMaMGaOQkBAFBwdr7Nixatq0qTp16iRJioqKUrdu3TR48GDNmzdPkjRkyBDFx8erUaNGkqQuXbqocePGSkxM1D/+8Q/9/vvvGjt2rAYPHlzuO4ckQgsAAJXWjh07dOeddzofn9sPM2DAAC1evFhPP/208vLyNHz4cGVnZys2Nlbr1q1T1apVnc+ZNm2aqlSpor59+yovL08dO3bU4sWL5e3t7RyzfPlyjRo1ynmXUa9evVy+G8bb21sff/yxhg8frttuu03+/v5KSEjQK6+8UqH3w/e0ANcQvqcFKO1KfE9LjYErTJnn+OL+pszjqei0AABgMX57yByEFgAALEZoMQd3DwEAAI9ApwUAAKvRaDEFoQUAAIuxPGQOlocAAIBHoNMCAIDF6LSYg9ACAIDFCC3mYHkIAAB4BDotAABYjE6LOQgtAABYjcxiCpaHAACAR6DTAgCAxVgeMgehBQAAixFazEFoAQDAYoQWc7CnBQAAeAQ6LQAAWI1GiykILQAAWIzlIXOwPAQAADwCnRa4uK1FfT31cCe1aFxLNa93qO9T8/Xh5984r/e+61YNuvd2NY+KVI3q1ym2X6q++eE/LnPUvbGGXn7qvxTXvJ7sPlW0fvNejZ78vzr6+ynnmJtqhSrlqT6Ku7WefH28tefHw5r02kf6145/O8fk7ZpVqr6RL63Q6+9ssuCdA9ZauGCePl2/TpmZP8vu56dmzZorafRY1albz92l4Qqg02IOOi1wEehv17c//EdPvbyyzOsB/r7a8vVPenbmB2Vf9/PVR7NHyDAMdR8yU3c9Mk2+Pt5699WhLn9oV80cpireXuo+dIbaPjhFX+/7j96bMUxhIVVd5hv83FLV6ZTsPJZ9uM28NwtcQTvSt6vfAw9q6VsrNW/BIhUVF2vY4EE6c+aMu0vDFWCz2Uw5rnV0WuBi3Zffad2X313w+lsfp0uSatUMLvN6XLN6qh0RojYPTNap3LOSpCETl+nIv/6hDq0b6rNt+xRSLVA31QrVsEnLtfvfhyVJz874QMP63aGo+jX162//ryOTcyrP5THgqebMX+jy+IW/p+rOdnHa+90exbRs5aaqAM/i1k7LoUOHNGHCBN15552KiopS48aNdeedd2rChAk6ePCgO0vDJbL7VpFhGMovKHKeO1tQpOLiErVtVl+S9NuJXO39+YgS4lsrwM9X3t5eeuze25V1/KR2fef6733a3+7XwQ0va9OycXrsvtv5Lw1UGqdP/RHGgxwON1eCK4FOiznc1mnZtGmTunfvrsjISHXp0kVdunSRYRg6evSo3n//fc2cOVNr167Vbbfd5q4ScQm2f7tfuXkFeunJ3npu1mrZZNNLT/aWt7eXwmsEOcfFD5ulldOH6tiXr6ikxNDR30+p94jXlHM6zzlm0msf6vPtPyjvbIHujG2kl0f/l0KqBWry6//njrcGmMYwDL0yJVXNW8SoQYOG7i4HVwJ5wxRuCy1PPfWUHnvsMU2bNu2C15OSkpSenn7RefLz85Wfn+9yzigpls3L27RaUX7Hs0/rwacXasYz/TT8gfYqKTG0Mm2nvvrugIpLSpzjpj/TT8d+P6VOj05XXn6BBv5XW703Y5huf+gfyjp+UpJcwsm5zb7Jg7sTWuDxUv/+gv79ww9avPRNd5cCeBS3LQ/t3r1bw4YNu+D1oUOHavfu3X85T2pqqhwOh8tR9OtOM0tFBX269Xs16fW8anVM1o13/k2Dnv0fRYRW0y//+U2S1KF1Q93dLloP/22Rtnz9szK+P6Sk1JXKyy/UQz1jLzjv9m/2y1HVX6HBVS84Brjapb70oj7/fIMWLFqisPBwd5eDK4TlIXO4LbTUrFlTmzdvvuD1LVu2qGbNmn85T3JysnJyclyOKmExZpaKS/TbiVzlnM5T+1YNFRp8nT7a+K2kP+4wkqSSP3Ve/nhsXPQP5a0336i8swU6cSrvgmOAq5VhGEr5+wv69JN1WvDGEt14Y6S7S8IVRGgxh9uWh8aOHathw4Zp586d6ty5s8LCwmSz2ZSVlaX169fr9ddf1/Tp0/9yHrvdLrvd7nKOpaFLF+jvq/qR1zsf17khRLc0vEHZJ8/oYFa2qgcFKDK8umqG/rF5sGGdMEnSr7+ddN7lk9irjfZlZulY9mnF3lJXr4y7TzOXf6Z//3JUkrTtm0xlnzyj1198WCnz1yrvbKEevaet6twQorRNeyRJd98RrbCQIG37JlN5+YVq36qBJo3oqTfe+1IFhUUCPE3Ki89r7ZqPNH3mbAUGBOr4sWOSpOuqVpWfn5+bq4PVyBvmsBmGYbjrxd9++21NmzZNO3fuVHFxsSTJ29tbMTExGj16tPr27XtJ8/o3f8LMMq8p7WIaaN3rT5Y6v3T1Vg2ZuEwP9YzVghcSS13/+9w1emneGknSi6N66aGebRTsCNAvh3/X6+9s0oxlG1zGt2hcS5NG9FSLxrXkU8VLe3/OUsr8tc7brTu3jdILI3upfuT18vKyKfPQb1q8arPmrvyXiotLSr0+yic7vfQX9uHKuLVJozLPv/D3VPX+r3uucDX4M78r8J/vN41da8o8P77S3ZR5PJVbQ8s5hYWFOn78uCSpRo0a8vHxuaz5CC1A2QgtQGlXIrQ0GJdmyjz//kc3U+bxVFfFl8v5+PiUa/8KAACeiOUhc/A1/gAAwCNcFZ0WAAAqM+78MQehBQAAi5FZzMHyEAAA8Ah0WgAAsJiXF60WMxBaAACwGMtD5mB5CAAAeAQ6LQAAWIy7h8xBaAEAwGJkFnMQWgAAsBidFnOwpwUAAHgEOi0AAFiMTos5CC0AAFiMzGIOlocAAIBHoNMCAIDFWB4yB6EFAACLkVnMwfIQAADwCHRaAACwGMtD5iC0AABgMTKLOVgeAgAAHoHQAgCAxWw2mylHRRQVFem///u/VbduXfn7+6tevXp64YUXVFJS4hxjGIYmTZqkiIgI+fv7q0OHDtqzZ4/LPPn5+Ro5cqRq1KihwMBA9erVS4cOHXIZk52drcTERDkcDjkcDiUmJurEiROX/HldCKEFAACL2WzmHBUxefJkzZ07V7NmzdLevXs1ZcoU/eMf/9DMmTOdY6ZMmaKpU6dq1qxZSk9PV3h4uDp37qxTp045xyQlJWnVqlVasWKFNm3apNOnTys+Pl7FxcXOMQkJCcrIyFBaWprS0tKUkZGhxMTEy/7czmczDMMwfVY382/+hLtLAK5K2emz3F0CcNXxuwK7O2NTN5oyz7bk9uUeGx8fr7CwMC1cuNB57t5771VAQICWLl0qwzAUERGhpKQkjR8/XtIfXZWwsDBNnjxZQ4cOVU5Ojq6//notXbpU/fr1kyQdPnxYkZGRWrNmjbp27aq9e/eqcePG2rp1q2JjYyVJW7duVVxcnL7//ns1atTIlPcu0WkBAMBj5Ofn6+TJky5Hfn5+mWNvv/12ffrpp/rhhx8kSV9//bU2bdqku+++W5KUmZmprKwsdenSxfkcu92u9u3ba/PmzZKknTt3qrCw0GVMRESEoqOjnWO2bNkih8PhDCyS1KZNGzkcDucYsxBaAACwmFnLQ6mpqc59I+eO1NTUMl9z/PjxeuCBB3TzzTfLx8dHzZs3V1JSkh544AFJUlZWliQpLCzM5XlhYWHOa1lZWfL19VX16tUvOiY0NLTU64eGhjrHmIVbngEAsJhZ39OSnJys0aNHu5yz2+1ljn377be1bNkyvfnmm2rSpIkyMjKUlJSkiIgIDRgw4IK1GYbxl/WeP6as8eWZp6IILQAAeAi73X7BkHK+cePG6W9/+5v69+8vSWratKl++eUXpaamasCAAQoPD5f0R6ekZs2azucdPXrU2X0JDw9XQUGBsrOzXbotR48eVdu2bZ1jfv3111Kvf+zYsVJdnMvF8hAAABZzx91DZ86ckZeX61/z3t7ezlue69atq/DwcK1fv955vaCgQBs3bnQGkpiYGPn4+LiMOXLkiHbv3u0cExcXp5ycHG3fvt05Ztu2bcrJyXGOMQudFgAALOaOr/Hv2bOnXnrpJdWqVUtNmjTRrl27NHXqVD366KPOmpKSkpSSkqIGDRqoQYMGSklJUUBAgBISEiRJDodDgwYN0pgxYxQSEqLg4GCNHTtWTZs2VadOnSRJUVFR6tatmwYPHqx58+ZJkoYMGaL4+HhT7xySCC0AAFRKM2fO1LPPPqvhw4fr6NGjioiI0NChQ/Xcc885xzz99NPKy8vT8OHDlZ2drdjYWK1bt05Vq1Z1jpk2bZqqVKmivn37Ki8vTx07dtTixYvl7e3tHLN8+XKNGjXKeZdRr169NGuW+V+xwPe0ANcQvqcFKO1KfE/L7a98Yco8m8a2M2UeT0WnBQAAi/Erz+ZgIy4AAPAIdFoAALAYnRZzEFoAALAYmcUchBYAACxGp8Uc7GkBAAAegU4LAAAWo9FiDkILAAAWY3nIHCwPAQAAj0CnBQAAi9FoMQehBQAAi3mRWkzB8hAAAPAIdFoAALAYjRZzEFoAALAYdw+Zg9ACAIDFvMgspmBPCwAA8Ah0WgAAsBjLQ+YgtAAAYDEyizlYHgIAAB7hskNLcXGxMjIylJ2dbUY9AABUOjaT/netq3BoSUpK0sKFCyX9EVjat2+vFi1aKDIyUp9//rnZ9QEA4PG8bOYc17oKh5Z33nlHt956qyTpww8/VGZmpr7//nslJSVpwoQJphcIAAAgXUJoOX78uMLDwyVJa9as0f3336+GDRtq0KBB+vbbb00vEAAAT2ez2Uw5rnUVDi1hYWH67rvvVFxcrLS0NHXq1EmSdObMGXl7e5teIAAAns5mM+e41lX4ludHHnlEffv2Vc2aNWWz2dS5c2dJ0rZt23TzzTebXiAAAIB0CaFl0qRJio6O1sGDB3X//ffLbrdLkry9vfW3v/3N9AIBAPB0XrRJTHFJXy533333lTo3YMCAyy4GAIDKiMxijnKFlhkzZpR7wlGjRl1yMQAAVEZsojVHuULLtGnTyjWZzWYjtAAAAEuUK7RkZmZaXQcAAJUWjRZzXPLX+BcUFGjfvn0qKioysx4AACodL5vNlONaV+HQcubMGQ0aNEgBAQFq0qSJDhw4IOmPvSwvv/yy6QUCAABIlxBakpOT9fXXX+vzzz+Xn5+f83ynTp309ttvm1ocAACVgc2k41pX4Vue33//fb399ttq06aNy27oxo0b66effjK1OAAAKgPuHjJHhTstx44dU2hoaKnzubm5/EsBAACWqXBoadWqlT7++GPn43NBZcGCBYqLizOvMgAAKgkvmznHta7Cy0Opqanq1q2bvvvuOxUVFenVV1/Vnj17tGXLFm3cuNGKGgEA8GisRJijwp2Wtm3b6ssvv9SZM2dUv359rVu3TmFhYdqyZYtiYmKsqBEAAODSfnuoadOmWrJkidm1AABQKdFoMcclhZbi4mKtWrVKe/fulc1mU1RUlHr37q0qVS5pOgAAKjWWh8xR4ZSxe/du9e7dW1lZWWrUqJEk6YcfftD111+v1atXq2nTpqYXCQCAJ2MTrTkqvKflscceU5MmTXTo0CF99dVX+uqrr3Tw4EHdcsstGjJkiBU1AgAAVLzT8vXXX2vHjh2qXr2681z16tX10ksvqVWrVqYWBwBAZcDykDkq3Glp1KiRfv3111Lnjx49qptuusmUogAAqEz4Gn9zlCu0nDx50nmkpKRo1KhReuedd3To0CEdOnRI77zzjpKSkjR58mSr6wUAANeoci0PVatWzaW1ZRiG+vbt6zxnGIYkqWfPniouLragTAAAPJcXy0OmKFdo+eyzz6yuAwCASovMYo5yhZb27dtbXQcAAMBFXfK3wZ05c0YHDhxQQUGBy/lbbrnlsosCAKAy4e4hc1Q4tBw7dkyPPPKI1q5dW+Z19rQAAOCKzGKOCt/ynJSUpOzsbG3dulX+/v5KS0vTkiVL1KBBA61evdqKGgEAwCX4z3/+o4ceekghISEKCAhQs2bNtHPnTud1wzA0adIkRUREyN/fXx06dNCePXtc5sjPz9fIkSNVo0YNBQYGqlevXjp06JDLmOzsbCUmJsrhcMjhcCgxMVEnTpww/f1UOLRs2LBB06ZNU6tWreTl5aXatWvroYce0pQpU5Sammp6gQAAeDovm82UoyKys7N12223ycfHR2vXrtV3332nf/7zn6pWrZpzzJQpUzR16lTNmjVL6enpCg8PV+fOnXXq1CnnmKSkJK1atUorVqzQpk2bdPr0acXHx7usrCQkJCgjI0NpaWlKS0tTRkaGEhMTL/tzO1+Fl4dyc3MVGhoqSQoODtaxY8fUsGFDNW3aVF999ZXpBQIA4OncsTw0efJkRUZGatGiRc5zderUcf6zYRiaPn26JkyYoHvuuUeStGTJEoWFhenNN9/U0KFDlZOTo4ULF2rp0qXq1KmTJGnZsmWKjIzUJ598oq5du2rv3r1KS0vT1q1bFRsbK0lasGCB4uLitG/fPufvFJrhkr4Rd9++fZKkZs2aad68efrPf/6juXPnqmbNmqYVBgBAZWGz2Uw5KmL16tVq2bKl7r//foWGhqp58+ZasGCB83pmZqaysrLUpUsX5zm73a727dtr8+bNkqSdO3eqsLDQZUxERISio6OdY7Zs2SKHw+EMLJLUpk0bORwO5xizVLjTkpSUpCNHjkiSJk6cqK5du2r58uXy9fXV4sWLTS0OAAD8P/n5+crPz3c5Z7fbZbfbS439+eefNWfOHI0ePVrPPPOMtm/frlGjRslut+vhhx9WVlaWJCksLMzleWFhYfrll18kSVlZWfL19XX5vcFzY849Pysry7kC82ehoaHOMWapcGh58MEHnf/cvHlz7d+/X99//71q1aqlGjVqmFrcpcpOn+XuEgAAcKrwssYFpKam6vnnn3c5N3HiRE2aNKnU2JKSErVs2VIpKSmS/vg7e8+ePZozZ44efvhh57jzOziGYfxlV+f8MWWNL888FXXZn2NAQIBatGhx1QQWAACuNmYtDyUnJysnJ8flSE5OLvM1a9asqcaNG7uci4qK0oEDByRJ4eHhklSqG3L06FFn9yU8PFwFBQXKzs6+6Jiyfkj52LFjpbo4l6tcnZbRo0eXe8KpU6decjEAAODCLrQUVJbbbrvNuQf1nB9++EG1a9eWJNWtW1fh4eFav369mjdvLkkqKCjQxo0bnT+AHBMTIx8fH61fv159+/aVJB05ckS7d+/WlClTJElxcXHKycnR9u3b1bp1a0nStm3blJOTo7Zt217+m/6TcoWWXbt2lWsyvvEPAIDSvNzw1+NTTz2ltm3bKiUlRX379tX27ds1f/58zZ8/X9Iff2cnJSUpJSVFDRo0UIMGDZSSkqKAgAAlJCRIkhwOhwYNGqQxY8YoJCREwcHBGjt2rJo2beq8mygqKkrdunXT4MGDNW/ePEnSkCFDFB8fb+qdQxI/mAgAgOXcEVpatWqlVatWKTk5WS+88ILq1q2r6dOnu+xNffrpp5WXl6fhw4crOztbsbGxWrdunapWreocM23aNFWpUkV9+/ZVXl6eOnbsqMWLF8vb29s5Zvny5Ro1apTzLqNevXpp1izz95faDMMwTJ/Vzc4WubsCAICn8LvkX+Erv9Grvzdlnqm9bjZlHk91Bf5VAQBwbWP7hDkILQAAWMwdy0OVkVm3jgMAAFiKTgsAABZjdcgcl9RpWbp0qW677TZFREQ4v+p3+vTp+uCDD0wtDgCAysAdv/JcGVU4tJz7HYO7775bJ06ccP40dbVq1TR9+nSz6wMAwON5mXRc6yr8GcycOVMLFizQhAkTXO7Rbtmypb799ltTiwMAADinwntaMjMznV/3+2d2u125ubmmFAUAQGXCyo45KtxpqVu3rjIyMkqdX7t2bakfZgIAAOxpMUuFOy3jxo3TiBEjdPbsWRmGoe3bt+utt95SamqqXn/9dStqBAAAqHhoeeSRR1RUVKSnn35aZ86cUUJCgm644Qa9+uqr6t+/vxU1AgDg0WiSmOOyfnvo+PHjKikpUWhoqJk1XTZ+ewgAUF5X4reHJq37tznzdGlgyjye6rL+VdWoUcOsOgAAAC6qwqGlbt26F/3hp59//vmyCgIAoLJhE605KhxakpKSXB4XFhZq165dSktL07hx48yqCwCASoPMYo4Kh5Ynn3yyzPOvvfaaduzYcdkFAQAAlMW0bwXu3r273n33XbOmAwCg0vCymXNc60zbM/3OO+8oODjYrOkAAKg0bCJxmKHCoaV58+YuG3ENw1BWVpaOHTum2bNnm1ocAACVAV0Sc1Q4tPTp08flsZeXl66//np16NBBN998s1l1AQAAuKhQaCkqKlKdOnXUtWtXhYeHW1UTAACVCp0Wc1RoI26VKlX0+OOPKz8/36p6AACodGw2mynHta7Cdw/FxsZq165dVtQCAABwQRXe0zJ8+HCNGTNGhw4dUkxMjAIDA12u33LLLaYVBwBAZcDykDnK/YOJjz76qKZPn65q1aqVnsRmk2EYstlsKi4uNrvGCuMHEwEA5XUlfjBx6r/M+Ymb0XfUM2UeT1Xu0OLt7a0jR44oLy/vouNq165tSmGXg9ACACgvQovnKPe/qnPZ5moIJQAAeBJ+MNEcFcqX7FwGAKDi2NNijgqFloYNG/5lcPn9998vqyAAAICyVCi0PP/883I4HFbVAgBApcRChTkqFFr69++v0NBQq2oBAKBS8uIHE01R7tDCfhYAAC4Nf4Wao9zfiFvOO6MBAAAsUe5OS0lJiZV1AABQaXH3kDmuwFfqAABwbeN7WsxR4R9MBAAAcAc6LQAAWIxGizkILQAAWIzlIXOwPAQAADwCnRYAACxGo8UchBYAACzGsoY5+BwBAIBHoNMCAIDF+CkccxBaAACwGJHFHIQWAAAsxi3P5mBPCwAA8Ah0WgAAsBh9FnMQWgAAsBirQ+ZgeQgAAHgEOi0AAFiMW57NQWgBAMBiLGuYg88RAIBrQGpqqmw2m5KSkpznDMPQpEmTFBERIX9/f3Xo0EF79uxxeV5+fr5GjhypGjVqKDAwUL169dKhQ4dcxmRnZysxMVEOh0MOh0OJiYk6ceKE6e+B0AIAgMVsNpspx6VKT0/X/Pnzdcstt7icnzJliqZOnapZs2YpPT1d4eHh6ty5s06dOuUck5SUpFWrVmnFihXatGmTTp8+rfj4eBUXFzvHJCQkKCMjQ2lpaUpLS1NGRoYSExMvud4LIbQAAGAxm0nHpTh9+rQefPBBLViwQNWrV3eeNwxD06dP14QJE3TPPfcoOjpaS5Ys0ZkzZ/Tmm29KknJycrRw4UL985//VKdOndS8eXMtW7ZM3377rT755BNJ0t69e5WWlqbXX39dcXFxiouL04IFC/TRRx9p3759l1h12QgtAAB4iPz8fJ08edLlyM/Pv+hzRowYoR49eqhTp04u5zMzM5WVlaUuXbo4z9ntdrVv316bN2+WJO3cuVOFhYUuYyIiIhQdHe0cs2XLFjkcDsXGxjrHtGnTRg6HwznGLIQWAAAsZtbyUGpqqnPfyLkjNTX1gq+7YsUKffXVV2WOycrKkiSFhYW5nA8LC3Ney8rKkq+vr0uHpqwxoaGhpeYPDQ11jjELdw8BAGAxszoEycnJGj16tMs5u91e5tiDBw/qySef1Lp16+Tn53fBOc/fK2MYxl/unzl/TFnjyzNPRdFpAQDAYmZ1Wux2u4KCglyOC4WWnTt36ujRo4qJiVGVKlVUpUoVbdy4UTNmzFCVKlWcHZbzuyFHjx51XgsPD1dBQYGys7MvOubXX38t9frHjh0r1cW5XIQWAAAqoY4dO+rbb79VRkaG82jZsqUefPBBZWRkqF69egoPD9f69eudzykoKNDGjRvVtm1bSVJMTIx8fHxcxhw5ckS7d+92jomLi1NOTo62b9/uHLNt2zbl5OQ4x5iF5SEAACzmju/DrVq1qqKjo13OBQYGKiQkxHk+KSlJKSkpatCggRo0aKCUlBQFBAQoISFBkuRwODRo0CCNGTNGISEhCg4O1tixY9W0aVPnxt6oqCh169ZNgwcP1rx58yRJQ4YMUXx8vBo1amTqeyK0AABgsav1W/yffvpp5eXlafjw4crOzlZsbKzWrVunqlWrOsdMmzZNVapUUd++fZWXl6eOHTtq8eLF8vb2do5Zvny5Ro0a5bzLqFevXpo1a5bp9doMwzBMn9XNzha5uwIAgKfwuwL/+f7Bt+bcRdO7abgp83gqOi0AAFjMyy0LRJUPoQUAAItdrctDnoa7hwAAgEeg0wIAgMVsLA+ZgtACAIDFWB4yB8tDAADAI9BpAQDAYtw9ZA5CCwAAFmN5yByEFgAALEZoMQd7WgAAgEeg0wIAgMW45dkchBYAACzmRWYxBctDAADAI9BpAQDAYiwPmYPQAgCAxbh7yBwsDwEAAI9ApwUAAIuxPGQOQgsAABbj7iFzsDwEAAA8Ap0WmK5757t0+PB/Sp3v1z9Bzzw70Q0VAVeHX3/9VdOn/kNffvGF8vPPqnbtOpr04ktq3CTa3aXBYiwPmYPQAtMtf/sdlRQXOx//+OO/NfSxR9S5azc3VgW418mcHA186AG1bB2r1+YuUHBIsA4dPKiqVYPcXRquAO4eMgehBaYLDg52efzG6/MVGVlLLVu1dlNFgPu9sXCBwsLD9eJLqc5zN9xwoxsrwpVEZjEHe1pgqcKCAn380Wr1uede2fhPDVzDNn62QU2aRGvsU6PUoV2c+t7bR+/+70p3lwV4lKs6tBw8eFCPPvroRcfk5+fr5MmTLkd+fv4VqhB/ZcOGT3Tq1Cn16vNf7i4FcKtDhw5q5dtvqVbtOpozf6Hu79dfk1P/rg8/eN/dpeEK8LLZTDmudVd1aPn999+1ZMmSi45JTU2Vw+FwOf4xOfWiz8GVs+rdd3Xb7XcoNDTM3aUAblVSYiiqcRONShqtqKjGur9vf91zX1+tfPstd5eGK8Bm0nGtc+ueltWrV1/0+s8///yXcyQnJ2v06NEu5wxv+2XVBXMcPvwfbdu6WVNfnenuUgC3u/7661Wvfn2Xc/Xq1dMn6//PTRUBnsetoaVPnz6y2WwyDOOCY/5qH4Tdbpfd7hpSzhaZUh4u0wer3lNwcIja3dHB3aUAbteseQvtz8x0OffL/v2KiLjBTRXhiqJNYgq3Lg/VrFlT7777rkpKSso8vvrqK3eWh8tQUlKiD1a9p569+6hKFW5SAx56eIC+/eZrvT5/rg788ovWfPSh3nlnpfo9kODu0nAF2Ez637XOraElJibmosHkr7owuHpt3bJZR44cVp977nV3KcBVIbrpLZr66iytXfOx7u0Tr/nzZuvp8c+oR3wvd5cGeAyb4cZU8MUXXyg3N1fdupX9pWO5ubnasWOH2rdvX6F5WR4CAJSX3xVoBm//OceUeVrXc5gyj6dya2ixCqEFAFBeVyK0pJsUWlpd46Hlqr7lGQAA4Bx2SAIAYDX20JqC0AIAgMW488cchBYAACzGN/Cbgz0tAADAI9BpAQDAYjRazEFoAQDAaqQWU7A8BAAAPAKdFgAALMbdQ+YgtAAAYDHuHjIHy0MAAMAj0GkBAMBiNFrMQWgBAMBqpBZTsDwEAAA8Ap0WAAAsxt1D5iC0AABgMe4eMgehBQAAi5FZzMGeFgAA4BHotAAAYDVaLaYgtAAAYDE24pqD5SEAACqh1NRUtWrVSlWrVlVoaKj69Omjffv2uYwxDEOTJk1SRESE/P391aFDB+3Zs8dlTH5+vkaOHKkaNWooMDBQvXr10qFDh1zGZGdnKzExUQ6HQw6HQ4mJiTpx4oTp74nQAgCAxWw2c46K2Lhxo0aMGKGtW7dq/fr1KioqUpcuXZSbm+scM2XKFE2dOlWzZs1Senq6wsPD1blzZ506dco5JikpSatWrdKKFSu0adMmnT59WvHx8SouLnaOSUhIUEZGhtLS0pSWlqaMjAwlJiZe9ud2PpthGIbps7rZ2SJ3VwAA8BR+V2CjxN7DuX89qByiIgIv+bnHjh1TaGioNm7cqDvuuEOGYSgiIkJJSUkaP368pD+6KmFhYZo8ebKGDh2qnJwcXX/99Vq6dKn69esnSTp8+LAiIyO1Zs0ade3aVXv37lXjxo21detWxcbGSpK2bt2quLg4ff/992rUqNHlv/H/H50WAAA8RH5+vk6ePOly5Ofnl+u5OTk5kqTg4GBJUmZmprKystSlSxfnGLvdrvbt22vz5s2SpJ07d6qwsNBlTEREhKKjo51jtmzZIofD4QwsktSmTRs5HA7nGLMQWgAAsJrNnCM1NdW5b+TckZqa+pcvbxiGRo8erdtvv13R0dGSpKysLElSWFiYy9iwsDDntaysLPn6+qp69eoXHRMaGlrqNUNDQ51jzMLdQwAAWMysu4eSk5M1evRol3N2u/0vn/fEE0/om2++0aZNm0rXdt5mGcMwSp073/ljyhpfnnkqik4LAAAewm63KygoyOX4q9AycuRIrV69Wp999pluvPFG5/nw8HBJKtUNOXr0qLP7Eh4eroKCAmVnZ190zK+//lrqdY8dO1aqi3O5CC0AAFjMHXcPGYahJ554Qu+99542bNigunXrulyvW7euwsPDtX79eue5goICbdy4UW3btpUkxcTEyMfHx2XMkSNHtHv3bueYuLg45eTkaPv27c4x27ZtU05OjnOMWVgeAgDAYu74arkRI0bozTff1AcffKCqVas6OyoOh0P+/v6y2WxKSkpSSkqKGjRooAYNGiglJUUBAQFKSEhwjh00aJDGjBmjkJAQBQcHa+zYsWratKk6deokSYqKilK3bt00ePBgzZs3T5I0ZMgQxcfHm3rnkMQtzwCAa9yVuOX5h1/PmDJPw7CAco+90H6SRYsWaeDAgZL+6MY8//zzmjdvnrKzsxUbG6vXXnvNuVlXks6ePatx48bpzTffVF5enjp27KjZs2crMjLSOeb333/XqFGjtHr1aklSr169NGvWLFWrVq3ib/Ji74nQAgC4llXW0FIZsTwEAIDF+O0hcxBaAACwmMl3/l6zuHsIAAB4BDotAABYjEaLOQgtAABYjdRiCpaHAACAR6DTAgCAxbh7yByEFgAALMbdQ+ZgeQgAAHgEOi0AAFiMRos5CC0AAFiN1GIKQgsAABZjI6452NMCAAA8Ap0WAAAsxt1D5iC0AABgMTKLOVgeAgAAHoFOCwAAFmN5yByEFgAALEdqMQPLQwAAwCPQaQEAwGIsD5mD0AIAgMXILOZgeQgAAHgEOi0AAFiM5SFzEFoAALAYvz1kDkILAABWI7OYgj0tAADAI9BpAQDAYjRazEFoAQDAYmzENQfLQwAAwCPQaQEAwGLcPWQOQgsAAFYjs5iC5SEAAOAR6LQAAGAxGi3mILQAAGAx7h4yB8tDAADAI9BpAQDAYtw9ZA5CCwAAFmN5yBwsDwEAAI9AaAEAAB6B5SEAACzG8pA5CC0AAFiMjbjmYHkIAAB4BDotAABYjOUhcxBaAACwGJnFHCwPAQAAj0CnBQAAq9FqMQWhBQAAi3H3kDlYHgIAAB6BTgsAABbj7iFzEFoAALAYmcUchBYAAKxGajEFe1oAAIBHoNMCAIDFuHvIHIQWAAAsxkZcc7A8BAAAPILNMAzD3UWgcsrPz1dqaqqSk5Nlt9vdXQ5w1eDPBnBpCC2wzMmTJ+VwOJSTk6OgoCB3lwNcNfizAVwalocAAIBHILQAAACPQGgBAAAegdACy9jtdk2cOJGNhsB5+LMBXBo24gIAAI9ApwUAAHgEQgsAAPAIhBYAAOARCC0AAMAjEFpgmdmzZ6tu3bry8/NTTEyMvvjiC3eXBLjVv/71L/Xs2VMRERGy2Wx6//333V0S4FEILbDE22+/raSkJE2YMEG7du1Su3bt1L17dx04cMDdpQFuk5ubq1tvvVWzZs1ydymAR+KWZ1giNjZWLVq00Jw5c5znoqKi1KdPH6WmprqxMuDqYLPZtGrVKvXp08fdpQAeg04LTFdQUKCdO3eqS5cuLue7dOmizZs3u6kqAICnI7TAdMePH1dxcbHCwsJczoeFhSkrK8tNVQEAPB2hBZax2Wwujw3DKHUOAIDyIrTAdDVq1JC3t3eprsrRo0dLdV8AACgvQgtM5+vrq5iYGK1fv97l/Pr169W2bVs3VQUA8HRV3F0AKqfRo0crMTFRLVu2VFxcnObPn68DBw5o2LBh7i4NcJvTp0/rxx9/dD7OzMxURkaGgoODVatWLTdWBngGbnmGZWbPnq0pU6boyJEjio6O1rRp03THHXe4uyzAbT7//HPdeeedpc4PGDBAixcvvvIFAR6G0AIAADwCe1oAAIBHILQAAACPQGgBAAAegdACAAA8AqEFAAB4BEILAADwCIQWAADgEQgtgBtNmjRJzZo1cz4eOHCg+vTpc8Xr2L9/v2w2mzIyMi44pk6dOpo+fXq551y8eLGqVat22bXZbDa9//77lz0PAM9HaAHOM3DgQNlsNtlsNvn4+KhevXoaO3ascnNzLX/tV199tdzfjFqeoAEAlQm/PQSUoVu3blq0aJEKCwv1xRdf6LHHHlNubq7mzJlTamxhYaF8fHxMeV2Hw2HKPABQGdFpAcpgt9sVHh6uyMhIJSQk6MEHH3QuUZxb0nnjjTdUr1492e12GYahnJwcDRkyRKGhoQoKCtJdd92lr7/+2mXel19+WWFhYapataoGDRqks2fPulw/f3mopKREkydP1k033SS73a5atWrppZdekiTVrVtXktS8eXPZbDZ16NDB+bxFixYpKipKfn5+uvnmmzV79myX19m+fbuaN28uPz8/tWzZUrt27arwZzR16lQ1bdpUgYGBioyM1PDhw3X69OlS495//301bNhQfn5+6ty5sw4ePOhy/cMPP1RMTIz8/PxUr149Pf/88yoqKirzNQsKCvTEE0+oZs2a8vPzU506dZSamlrh2gF4JjotQDn4+/ursLDQ+fjHH3/UypUr9e6778rb21uS1KNHDwUHB2vNmjVyOByaN2+eOnbsqB9++EHBwcFauXKlJk6cqNdee03t2rXT0qVLNWPGDNWrV++Cr5ucnKwFCxZo2rRpuv3223XkyBF9//33kv4IHq1bt9Ynn3yiJk2ayNfXV5K0YMECTZw4UbNmzVLz5s21a9cuDR48WIGBgRowYIByc3MVHx+vu+66S8uWLVNmZqaefPLJCn8mXl5emjFjhurUqaPMzEwNHz5cTz/9tEtAOnPmjF566SUtWbJEvr6+Gj58uPr3768vv/xSkvR///d/euihhzRjxgy1a9dOP/30k4YMGSJJmjhxYqnXnDFjhlavXq2VK1eqVq1aOnjwYKkQBKASMwC4GDBggNG7d2/n423bthkhISFG3759DcMwjIkTJxo+Pj7G0aNHnWM+/fRTIygoyDh79qzLXPXr1zfmzZtnGIZhxMXFGcOGDXO5Hhsba9x6661lvvbJkycNu91uLFiwoMw6MzMzDUnGrl27XM5HRkYab775psu5F1980YiLizMMwzDmzZtnBAcHG7m5uc7rc+bMKXOuP6tdu7Yxbdq0C15fuXKlERIS4ny8aNEiQ5KxdetW57m9e/cakoxt27YZhmEY7dq1M1JSUlzmWbp0qVGzZk3nY0nGqlWrDMMwjJEjRxp33XWXUVJScsE6AFRedFqAMnz00Ue67rrrVFRUpMLCQvXu3VszZ850Xq9du7auv/565+OdO3fq9OnTCgkJcZknLy9PP/30kyRp7969GjZsmMv1uLg4ffbZZ2XWsHfvXuXn56tjx47lrvvYsWM6ePCgBg0apMGDBzvPFxUVOffL7N27V7feeqsCAgJc6qiozz77TCkpKfruu+908uRJFRUV6ezZs8rNzVVgYKAkqUqVKmrZsqXzOTfffLOqVaumvXv3qnXr1tq5c6fS09OdS16SVFxcrLNnz+rMmTMuNUp/LJ917txZjRo1Urdu3RQfH68uXbpUuHYAnonQApThzjvv1Jw5c+Tj46OIiIhSG23P/aV8TklJiWrWrKnPP/+81FyXetuvv79/hZ9TUlIi6Y8lotjYWJdr55axDMO4pHr+7JdfftHdd9+tYcOG6cUXX1RwcLA2bdqkQYMGuSyjSX/csny+c+dKSkr0/PPP65577ik1xs/Pr9S5Fi1aKDMzU2vXrtUnn3yivn37qlOnTnrnnXcu+z0BuPoRWoAyBAYG6qabbir3+BYtWigrK0tVqlRRnTp1yhwTFRWlrVu36uGHH3ae27p16wXnbNCggfz9/fXpp5/qscceK3X93B6W4uJi57mwsDDdcMMN+vnnn/Xggw+WOW/jxo21dOlS5eXlOYPRxeooy44dO1RUVKR//vOf8vL6Yz//ypUrS40rKirSjh071Lp1a0nSvn37dOLECd18882S/vjc9u3bV6HPOigoSP369VO/fv103333qVu3bvr9998VHBxcofcAwPMQWgATdOrUSXFxcerTp48mT56sRo0a6fDhw1qzZo369Omjli1b6sknn9SAAQPUsmVL3X777Vq+fLn27NlzwY24fn5+Gj9+vJ5++mn5+vrqtttu07Fjx7Rnzx4NGjRIoaGh8vf3V1pamm688Ub5+fnJ4XBo0qRJGjVqlIKCgtS9e3fl5+drx44dys7O1ujRo5WQkKAJEyZo0KBB+u///m/t379fr7zySoXeb/369VVUVKSZM2eqZ8+e+vLLLzV37txS43x8fDRy5EjNmDFDPj4+euKJJ9SmTRtniHnuuecUHx+vyMhI3X///fLy8tI333yjb7/9Vn//+99LzTdt2jTVrFlTzZo1k5eXl/73f/9X4eHhpnyJHYCrH7c8Ayaw2Wxas2aN7rjjDj366KNq2LCh+vfvr/379yssLEyS1K9fPz333HMaP368YmJi9Msvv+jxxx+/6LzPPvusxowZo+eee05RUVHq16+fjh49KumP/SIzZszQvHnzFBERod69e0uSHnvsMb3++utavHixmjZtqvbt22vx4sXOW6Svu+46ffjhh/ruu+/UvHlzTZgwQZMnT67Q+23WrJmmTp2qyZMnKzo6WsuXLy/z1uOAgACNHz9eCQkJiouLk7+/v1asWOG83rVrV3300Udav369WrVqpTZt2mjq1KmqXbt2ma973XXXafLkyWrZsqVatWql/fv3a82aNc5uD4DKzWaYscANAABgMf7zBAAAeARCCwAA8AiEFgAA4BEILQAAwCMQWgAAgEcgtAAAAI9AaAEAAB6B0AIAADwCoQUAAHgEQgsAAPAIhBYAAOARCC0AAMAj/H/bc99VRwsDvAAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AUC: 0.7307\n",
      "Precision with recall set to 0.9: 0.1402\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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",
      "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"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "orx",
   "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": "3.10.9"
  },
  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
    "hash": "1cc8dc20d4b2d36f80e43c89fbc91b78036150fd34ea14a0f3f26f3341398113"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}