NARENDRA_CONCURRENT_FAULT.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "view-in-github"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/KingT5M/NARENDRA-CONCURRENT-FAULT/blob/main/NARENDRA_CONCURRENT_FAULT.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Concatenated data shape: (42597, 9)\n",
            "Concatenated data shape after shuffling: (42597, 9)\n",
            "Epoch 1/50\n",
            "426/426 [==============================] - 10s 17ms/step - loss: 874858624.0000 - val_loss: 875797248.0000\n",
            "Epoch 2/50\n",
            "426/426 [==============================] - 9s 20ms/step - loss: 874858624.0000 - val_loss: 875797248.0000\n",
            "Epoch 3/50\n",
            "426/426 [==============================] - 8s 18ms/step - loss: 874858624.0000 - val_loss: 875797248.0000\n",
            "Epoch 4/50\n",
            "426/426 [==============================] - 6s 14ms/step - loss: 874858112.0000 - val_loss: 875797248.0000\n",
            "Epoch 5/50\n",
            "426/426 [==============================] - 6s 13ms/step - loss: 874858624.0000 - val_loss: 875797248.0000\n",
            "Epoch 6/50\n",
            "426/426 [==============================] - 5s 12ms/step - loss: 874858560.0000 - val_loss: 875797248.0000\n",
            "Epoch 7/50\n",
            "426/426 [==============================] - 6s 13ms/step - loss: 874858880.0000 - val_loss: 875797248.0000\n",
            "Epoch 8/50\n",
            "426/426 [==============================] - 5s 13ms/step - loss: 874858560.0000 - val_loss: 875797248.0000\n",
            "Epoch 9/50\n",
            "426/426 [==============================] - 6s 15ms/step - loss: 874858624.0000 - val_loss: 875797248.0000\n",
            "Epoch 10/50\n",
            "426/426 [==============================] - 8s 19ms/step - loss: 874858240.0000 - val_loss: 875797248.0000\n",
            "Epoch 11/50\n",
            "426/426 [==============================] - 6s 14ms/step - loss: 874858624.0000 - val_loss: 875797248.0000\n",
            "Epoch 12/50\n",
            "426/426 [==============================] - 6s 14ms/step - loss: 874858560.0000 - val_loss: 875797248.0000\n",
            "Epoch 13/50\n",
            "426/426 [==============================] - 6s 14ms/step - loss: 874858176.0000 - val_loss: 875797248.0000\n",
            "Epoch 14/50\n",
            "426/426 [==============================] - 6s 14ms/step - loss: 874858176.0000 - val_loss: 875797248.0000\n",
            "Epoch 15/50\n",
            "426/426 [==============================] - 6s 14ms/step - loss: 874858304.0000 - val_loss: 875797248.0000\n",
            "Epoch 16/50\n",
            "426/426 [==============================] - 6s 14ms/step - loss: 874858432.0000 - val_loss: 875797248.0000\n",
            "Epoch 17/50\n",
            "426/426 [==============================] - 6s 14ms/step - loss: 874858688.0000 - val_loss: 875797248.0000\n",
            "Epoch 18/50\n",
            "426/426 [==============================] - 6s 15ms/step - loss: 874858688.0000 - val_loss: 875797248.0000\n",
            "Epoch 19/50\n",
            "426/426 [==============================] - 6s 14ms/step - loss: 874858240.0000 - val_loss: 875797248.0000\n",
            "Epoch 20/50\n",
            "426/426 [==============================] - 6s 14ms/step - loss: 874858688.0000 - val_loss: 875797248.0000\n",
            "Epoch 21/50\n",
            "426/426 [==============================] - 6s 15ms/step - loss: 874859008.0000 - val_loss: 875797248.0000\n",
            "Epoch 22/50\n",
            "426/426 [==============================] - 6s 15ms/step - loss: 874858240.0000 - val_loss: 875797248.0000\n",
            "Epoch 23/50\n",
            "426/426 [==============================] - 5s 12ms/step - loss: 874858688.0000 - val_loss: 875797248.0000\n",
            "Epoch 24/50\n",
            "426/426 [==============================] - 5s 12ms/step - loss: 874858816.0000 - val_loss: 875797248.0000\n",
            "Epoch 25/50\n",
            "426/426 [==============================] - 5s 12ms/step - loss: 874858112.0000 - val_loss: 875797248.0000\n",
            "Epoch 26/50\n",
            "426/426 [==============================] - 5s 12ms/step - loss: 874858944.0000 - val_loss: 875797248.0000\n",
            "Epoch 27/50\n",
            "426/426 [==============================] - 5s 11ms/step - loss: 874858304.0000 - val_loss: 875797248.0000\n",
            "Epoch 28/50\n",
            "426/426 [==============================] - 5s 12ms/step - loss: 874858816.0000 - val_loss: 875797248.0000\n",
            "Epoch 29/50\n",
            "426/426 [==============================] - 5s 12ms/step - loss: 874858304.0000 - val_loss: 875797248.0000\n",
            "Epoch 30/50\n",
            "426/426 [==============================] - 5s 12ms/step - loss: 874858624.0000 - val_loss: 875797248.0000\n",
            "Epoch 31/50\n",
            "426/426 [==============================] - 5s 12ms/step - loss: 874857856.0000 - val_loss: 875797248.0000\n",
            "Epoch 32/50\n",
            "426/426 [==============================] - 5s 11ms/step - loss: 874859072.0000 - val_loss: 875797248.0000\n",
            "Epoch 33/50\n",
            "426/426 [==============================] - 5s 11ms/step - loss: 874858304.0000 - val_loss: 875797248.0000\n",
            "Epoch 34/50\n",
            "426/426 [==============================] - 5s 12ms/step - loss: 874858688.0000 - val_loss: 875797248.0000\n",
            "Epoch 35/50\n",
            "426/426 [==============================] - 5s 12ms/step - loss: 874858240.0000 - val_loss: 875797248.0000\n",
            "Epoch 36/50\n",
            "426/426 [==============================] - 5s 12ms/step - loss: 874858816.0000 - val_loss: 875797248.0000\n",
            "Epoch 37/50\n",
            "426/426 [==============================] - 5s 12ms/step - loss: 874858816.0000 - val_loss: 875797248.0000\n",
            "Epoch 38/50\n",
            "426/426 [==============================] - 5s 12ms/step - loss: 874858496.0000 - val_loss: 875797248.0000\n",
            "Epoch 39/50\n",
            "426/426 [==============================] - 5s 12ms/step - loss: 874858688.0000 - val_loss: 875797248.0000\n",
            "Epoch 40/50\n",
            "426/426 [==============================] - 5s 12ms/step - loss: 874858240.0000 - val_loss: 875797248.0000\n",
            "Epoch 41/50\n",
            "426/426 [==============================] - 5s 12ms/step - loss: 874858944.0000 - val_loss: 875797248.0000\n",
            "Epoch 42/50\n",
            "426/426 [==============================] - 5s 12ms/step - loss: 874858432.0000 - val_loss: 875797248.0000\n",
            "Epoch 43/50\n",
            "426/426 [==============================] - 5s 12ms/step - loss: 874858304.0000 - val_loss: 875797248.0000\n",
            "Epoch 44/50\n",
            "426/426 [==============================] - 5s 12ms/step - loss: 874858432.0000 - val_loss: 875797248.0000\n",
            "Epoch 45/50\n",
            "426/426 [==============================] - 5s 12ms/step - loss: 874858112.0000 - val_loss: 875797248.0000\n",
            "Epoch 46/50\n",
            "426/426 [==============================] - 5s 12ms/step - loss: 874858560.0000 - val_loss: 875797248.0000\n",
            "Epoch 47/50\n",
            "426/426 [==============================] - 5s 12ms/step - loss: 874858624.0000 - val_loss: 875797248.0000\n",
            "Epoch 48/50\n",
            "426/426 [==============================] - 6s 13ms/step - loss: 874858304.0000 - val_loss: 875797248.0000\n",
            "Epoch 49/50\n",
            "426/426 [==============================] - 5s 12ms/step - loss: 874857664.0000 - val_loss: 875797248.0000\n",
            "Epoch 50/50\n",
            "426/426 [==============================] - 5s 12ms/step - loss: 874858880.0000 - val_loss: 875797248.0000\n",
            "1065/1065 [==============================] - 4s 4ms/step\n",
            "134/134 [==============================] - 1s 4ms/step\n",
            "134/134 [==============================] - 0s 3ms/step\n",
            "1065/1065 [==============================] - 31s 27ms/step\n",
            "134/134 [==============================] - 4s 27ms/step\n",
            "134/134 [==============================] - 4s 26ms/step\n",
            "Reinforcement Learning starts.\n"
          ]
        },
        {
          "ename": "KeyboardInterrupt",
          "evalue": "",
          "output_type": "error",
          "traceback": [
            "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
            "Cell \u001b[1;32mIn[9], line 260\u001b[0m\n\u001b[0;32m    257\u001b[0m model \u001b[38;5;241m=\u001b[39m hypermodel\u001b[38;5;241m.\u001b[39mbuild(hp)\n\u001b[0;32m    259\u001b[0m \u001b[38;5;66;03m# Train the model\u001b[39;00m\n\u001b[1;32m--> 260\u001b[0m history \u001b[38;5;241m=\u001b[39m \u001b[43mmodel\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m    261\u001b[0m \u001b[43m    \u001b[49m\u001b[43mfeatures_dae_train\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    262\u001b[0m \u001b[43m    \u001b[49m\u001b[43my_train\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    263\u001b[0m \u001b[43m    \u001b[49m\u001b[43mvalidation_data\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mfeatures_dae_val\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my_val\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    264\u001b[0m \u001b[43m    \u001b[49m\u001b[43mbatch_size\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msampled_hyperparameters\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mbatch_size\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    265\u001b[0m \u001b[43m    \u001b[49m\u001b[43mepochs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msampled_hyperparameters\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mepochs\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    266\u001b[0m \u001b[43m    \u001b[49m\u001b[43mverbose\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m0\u001b[39;49m\n\u001b[0;32m    267\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    269\u001b[0m \u001b[38;5;66;03m# Evaluate the model\u001b[39;00m\n\u001b[0;32m    270\u001b[0m loss, accuracy \u001b[38;5;241m=\u001b[39m model\u001b[38;5;241m.\u001b[39mevaluate(features_dae_val, y_val, verbose\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0\u001b[39m)\n",
            "File \u001b[1;32mc:\\Users\\T5M\\anaconda3\\envs\\Sankara-Ai\\Lib\\site-packages\\keras\\src\\utils\\traceback_utils.py:65\u001b[0m, in \u001b[0;36mfilter_traceback.<locals>.error_handler\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m     63\u001b[0m filtered_tb \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m     64\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m---> 65\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfn\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m     66\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[0;32m     67\u001b[0m     filtered_tb \u001b[38;5;241m=\u001b[39m _process_traceback_frames(e\u001b[38;5;241m.\u001b[39m__traceback__)\n",
            "File \u001b[1;32mc:\\Users\\T5M\\anaconda3\\envs\\Sankara-Ai\\Lib\\site-packages\\keras\\src\\engine\\training.py:1807\u001b[0m, in \u001b[0;36mModel.fit\u001b[1;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_batch_size, validation_freq, max_queue_size, workers, use_multiprocessing)\u001b[0m\n\u001b[0;32m   1799\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m tf\u001b[38;5;241m.\u001b[39mprofiler\u001b[38;5;241m.\u001b[39mexperimental\u001b[38;5;241m.\u001b[39mTrace(\n\u001b[0;32m   1800\u001b[0m     \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtrain\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[0;32m   1801\u001b[0m     epoch_num\u001b[38;5;241m=\u001b[39mepoch,\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m   1804\u001b[0m     _r\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m,\n\u001b[0;32m   1805\u001b[0m ):\n\u001b[0;32m   1806\u001b[0m     callbacks\u001b[38;5;241m.\u001b[39mon_train_batch_begin(step)\n\u001b[1;32m-> 1807\u001b[0m     tmp_logs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtrain_function\u001b[49m\u001b[43m(\u001b[49m\u001b[43miterator\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   1808\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m data_handler\u001b[38;5;241m.\u001b[39mshould_sync:\n\u001b[0;32m   1809\u001b[0m         context\u001b[38;5;241m.\u001b[39masync_wait()\n",
            "File \u001b[1;32mc:\\Users\\T5M\\anaconda3\\envs\\Sankara-Ai\\Lib\\site-packages\\tensorflow\\python\\util\\traceback_utils.py:150\u001b[0m, in \u001b[0;36mfilter_traceback.<locals>.error_handler\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m    148\u001b[0m filtered_tb \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m    149\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m--> 150\u001b[0m   \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfn\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    151\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[0;32m    152\u001b[0m   filtered_tb \u001b[38;5;241m=\u001b[39m _process_traceback_frames(e\u001b[38;5;241m.\u001b[39m__traceback__)\n",
            "File \u001b[1;32mc:\\Users\\T5M\\anaconda3\\envs\\Sankara-Ai\\Lib\\site-packages\\tensorflow\\python\\eager\\polymorphic_function\\polymorphic_function.py:832\u001b[0m, in \u001b[0;36mFunction.__call__\u001b[1;34m(self, *args, **kwds)\u001b[0m\n\u001b[0;32m    829\u001b[0m compiler \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mxla\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_jit_compile \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mnonXla\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    831\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m OptionalXlaContext(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_jit_compile):\n\u001b[1;32m--> 832\u001b[0m   result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwds\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    834\u001b[0m new_tracing_count \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mexperimental_get_tracing_count()\n\u001b[0;32m    835\u001b[0m without_tracing \u001b[38;5;241m=\u001b[39m (tracing_count \u001b[38;5;241m==\u001b[39m new_tracing_count)\n",
            "File \u001b[1;32mc:\\Users\\T5M\\anaconda3\\envs\\Sankara-Ai\\Lib\\site-packages\\tensorflow\\python\\eager\\polymorphic_function\\polymorphic_function.py:868\u001b[0m, in \u001b[0;36mFunction._call\u001b[1;34m(self, *args, **kwds)\u001b[0m\n\u001b[0;32m    865\u001b[0m   \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_lock\u001b[38;5;241m.\u001b[39mrelease()\n\u001b[0;32m    866\u001b[0m   \u001b[38;5;66;03m# In this case we have created variables on the first call, so we run the\u001b[39;00m\n\u001b[0;32m    867\u001b[0m   \u001b[38;5;66;03m# defunned version which is guaranteed to never create variables.\u001b[39;00m\n\u001b[1;32m--> 868\u001b[0m   \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mtracing_compilation\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcall_function\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m    869\u001b[0m \u001b[43m      \u001b[49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkwds\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_no_variable_creation_config\u001b[49m\n\u001b[0;32m    870\u001b[0m \u001b[43m  \u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    871\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_variable_creation_config \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m    872\u001b[0m   \u001b[38;5;66;03m# Release the lock early so that multiple threads can perform the call\u001b[39;00m\n\u001b[0;32m    873\u001b[0m   \u001b[38;5;66;03m# in parallel.\u001b[39;00m\n\u001b[0;32m    874\u001b[0m   \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_lock\u001b[38;5;241m.\u001b[39mrelease()\n",
            "File \u001b[1;32mc:\\Users\\T5M\\anaconda3\\envs\\Sankara-Ai\\Lib\\site-packages\\tensorflow\\python\\eager\\polymorphic_function\\tracing_compilation.py:139\u001b[0m, in \u001b[0;36mcall_function\u001b[1;34m(args, kwargs, tracing_options)\u001b[0m\n\u001b[0;32m    137\u001b[0m bound_args \u001b[38;5;241m=\u001b[39m function\u001b[38;5;241m.\u001b[39mfunction_type\u001b[38;5;241m.\u001b[39mbind(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m    138\u001b[0m flat_inputs \u001b[38;5;241m=\u001b[39m function\u001b[38;5;241m.\u001b[39mfunction_type\u001b[38;5;241m.\u001b[39munpack_inputs(bound_args)\n\u001b[1;32m--> 139\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunction\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_flat\u001b[49m\u001b[43m(\u001b[49m\u001b[43m  \u001b[49m\u001b[38;5;66;43;03m# pylint: disable=protected-access\u001b[39;49;00m\n\u001b[0;32m    140\u001b[0m \u001b[43m    \u001b[49m\u001b[43mflat_inputs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcaptured_inputs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfunction\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcaptured_inputs\u001b[49m\n\u001b[0;32m    141\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n",
            "File \u001b[1;32mc:\\Users\\T5M\\anaconda3\\envs\\Sankara-Ai\\Lib\\site-packages\\tensorflow\\python\\eager\\polymorphic_function\\concrete_function.py:1323\u001b[0m, in \u001b[0;36mConcreteFunction._call_flat\u001b[1;34m(self, tensor_inputs, captured_inputs)\u001b[0m\n\u001b[0;32m   1319\u001b[0m possible_gradient_type \u001b[38;5;241m=\u001b[39m gradients_util\u001b[38;5;241m.\u001b[39mPossibleTapeGradientTypes(args)\n\u001b[0;32m   1320\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m (possible_gradient_type \u001b[38;5;241m==\u001b[39m gradients_util\u001b[38;5;241m.\u001b[39mPOSSIBLE_GRADIENT_TYPES_NONE\n\u001b[0;32m   1321\u001b[0m     \u001b[38;5;129;01mand\u001b[39;00m executing_eagerly):\n\u001b[0;32m   1322\u001b[0m   \u001b[38;5;66;03m# No tape is watching; skip to running the function.\u001b[39;00m\n\u001b[1;32m-> 1323\u001b[0m   \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_inference_function\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcall_preflattened\u001b[49m\u001b[43m(\u001b[49m\u001b[43margs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   1324\u001b[0m forward_backward \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_select_forward_and_backward_functions(\n\u001b[0;32m   1325\u001b[0m     args,\n\u001b[0;32m   1326\u001b[0m     possible_gradient_type,\n\u001b[0;32m   1327\u001b[0m     executing_eagerly)\n\u001b[0;32m   1328\u001b[0m forward_function, args_with_tangents \u001b[38;5;241m=\u001b[39m forward_backward\u001b[38;5;241m.\u001b[39mforward()\n",
            "File \u001b[1;32mc:\\Users\\T5M\\anaconda3\\envs\\Sankara-Ai\\Lib\\site-packages\\tensorflow\\python\\eager\\polymorphic_function\\atomic_function.py:216\u001b[0m, in \u001b[0;36mAtomicFunction.call_preflattened\u001b[1;34m(self, args)\u001b[0m\n\u001b[0;32m    214\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mcall_preflattened\u001b[39m(\u001b[38;5;28mself\u001b[39m, args: Sequence[core\u001b[38;5;241m.\u001b[39mTensor]) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Any:\n\u001b[0;32m    215\u001b[0m \u001b[38;5;250m  \u001b[39m\u001b[38;5;124;03m\"\"\"Calls with flattened tensor inputs and returns the structured output.\"\"\"\u001b[39;00m\n\u001b[1;32m--> 216\u001b[0m   flat_outputs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcall_flat\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    217\u001b[0m   \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mfunction_type\u001b[38;5;241m.\u001b[39mpack_output(flat_outputs)\n",
            "File \u001b[1;32mc:\\Users\\T5M\\anaconda3\\envs\\Sankara-Ai\\Lib\\site-packages\\tensorflow\\python\\eager\\polymorphic_function\\atomic_function.py:251\u001b[0m, in \u001b[0;36mAtomicFunction.call_flat\u001b[1;34m(self, *args)\u001b[0m\n\u001b[0;32m    249\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m record\u001b[38;5;241m.\u001b[39mstop_recording():\n\u001b[0;32m    250\u001b[0m   \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_bound_context\u001b[38;5;241m.\u001b[39mexecuting_eagerly():\n\u001b[1;32m--> 251\u001b[0m     outputs \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_bound_context\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcall_function\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m    252\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mname\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    253\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;28;43mlist\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43margs\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    254\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;28;43mlen\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfunction_type\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mflat_outputs\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    255\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    256\u001b[0m   \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m    257\u001b[0m     outputs \u001b[38;5;241m=\u001b[39m make_call_op_in_graph(\n\u001b[0;32m    258\u001b[0m         \u001b[38;5;28mself\u001b[39m,\n\u001b[0;32m    259\u001b[0m         \u001b[38;5;28mlist\u001b[39m(args),\n\u001b[0;32m    260\u001b[0m         \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_bound_context\u001b[38;5;241m.\u001b[39mfunction_call_options\u001b[38;5;241m.\u001b[39mas_attrs(),\n\u001b[0;32m    261\u001b[0m     )\n",
            "File \u001b[1;32mc:\\Users\\T5M\\anaconda3\\envs\\Sankara-Ai\\Lib\\site-packages\\tensorflow\\python\\eager\\context.py:1486\u001b[0m, in \u001b[0;36mContext.call_function\u001b[1;34m(self, name, tensor_inputs, num_outputs)\u001b[0m\n\u001b[0;32m   1484\u001b[0m cancellation_context \u001b[38;5;241m=\u001b[39m cancellation\u001b[38;5;241m.\u001b[39mcontext()\n\u001b[0;32m   1485\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m cancellation_context \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m-> 1486\u001b[0m   outputs \u001b[38;5;241m=\u001b[39m \u001b[43mexecute\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mexecute\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m   1487\u001b[0m \u001b[43m      \u001b[49m\u001b[43mname\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdecode\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mutf-8\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1488\u001b[0m \u001b[43m      \u001b[49m\u001b[43mnum_outputs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mnum_outputs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1489\u001b[0m \u001b[43m      \u001b[49m\u001b[43minputs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtensor_inputs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1490\u001b[0m \u001b[43m      \u001b[49m\u001b[43mattrs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mattrs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1491\u001b[0m \u001b[43m      \u001b[49m\u001b[43mctx\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1492\u001b[0m \u001b[43m  \u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   1493\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m   1494\u001b[0m   outputs \u001b[38;5;241m=\u001b[39m execute\u001b[38;5;241m.\u001b[39mexecute_with_cancellation(\n\u001b[0;32m   1495\u001b[0m       name\u001b[38;5;241m.\u001b[39mdecode(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mutf-8\u001b[39m\u001b[38;5;124m\"\u001b[39m),\n\u001b[0;32m   1496\u001b[0m       num_outputs\u001b[38;5;241m=\u001b[39mnum_outputs,\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m   1500\u001b[0m       cancellation_manager\u001b[38;5;241m=\u001b[39mcancellation_context,\n\u001b[0;32m   1501\u001b[0m   )\n",
            "File \u001b[1;32mc:\\Users\\T5M\\anaconda3\\envs\\Sankara-Ai\\Lib\\site-packages\\tensorflow\\python\\eager\\execute.py:53\u001b[0m, in \u001b[0;36mquick_execute\u001b[1;34m(op_name, num_outputs, inputs, attrs, ctx, name)\u001b[0m\n\u001b[0;32m     51\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m     52\u001b[0m   ctx\u001b[38;5;241m.\u001b[39mensure_initialized()\n\u001b[1;32m---> 53\u001b[0m   tensors \u001b[38;5;241m=\u001b[39m \u001b[43mpywrap_tfe\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mTFE_Py_Execute\u001b[49m\u001b[43m(\u001b[49m\u001b[43mctx\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_handle\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdevice_name\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mop_name\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m     54\u001b[0m \u001b[43m                                      \u001b[49m\u001b[43minputs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mattrs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnum_outputs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m     55\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m core\u001b[38;5;241m.\u001b[39m_NotOkStatusException \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[0;32m     56\u001b[0m   \u001b[38;5;28;01mif\u001b[39;00m name \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n",
            "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
          ]
        }
      ],
      "source": [
        "import tensorflow as tf\n",
        "import pandas as pd\n",
        "import os\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt \n",
        "import keras_tuner as kt\n",
        "from sklearn.preprocessing import LabelEncoder, OneHotEncoder, MinMaxScaler\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import precision_score, recall_score, f1_score\n",
        "from scipy.stats import randint, uniform \n",
        "from tensorflow import keras\n",
        "from keras.models import Sequential\n",
        "from keras.layers import Conv1D, BatchNormalization, MaxPooling1D, LSTM, Flatten, Dense, Dropout, Reshape\n",
        "from keras.optimizers import Adam\n",
        "from keras.callbacks import EarlyStopping, Callback\n",
        "from kerastuner import HyperModel, HyperParameters\n",
        "from keras.layers import Input, Dense, GRU\n",
        "from keras.models import Model\n",
        "\n",
        "# File paths\n",
        "file_paths = [\n",
        "    'C:\\\\Users\\\\T5M\\\\Desktop\\\\CONCURRENT-FAULT\\\\DATASET\\\\DelayAPP.csv',\n",
        "    'C:\\\\Users\\\\T5M\\\\Desktop\\\\CONCURRENT-FAULT\\\\DATASET\\\\GainRPM.csv',\n",
        "    'C:\\\\Users\\\\T5M\\\\Desktop\\\\CONCURRENT-FAULT\\\\DATASET\\\\HealthyData.csv',\n",
        "    'C:\\\\Users\\\\T5M\\\\Desktop\\\\CONCURRENT-FAULT\\\\DATASET\\\\NoiseAPP.csv',\n",
        "    'C:\\\\Users\\\\T5M\\\\Desktop\\\\CONCURRENT-FAULT\\\\DATASET\\\\PacketLossAPP.csv'\n",
        "]\n",
        "\n",
        "# Load and preprocess the data\n",
        "def load_and_preprocess_data(file_paths):\n",
        "    data_frames = []\n",
        "    for file_path in file_paths:\n",
        "        if os.path.exists(file_path):\n",
        "            data = pd.read_csv(file_path, header=None, usecols=[i for i in range(1, 10)])\n",
        "            data_frames.append(data)\n",
        "        else:\n",
        "            print(f\"File not found: {file_path}\")\n",
        "    if not data_frames:\n",
        "        print(\"No data frames loaded.\")\n",
        "        return None\n",
        "    concatenated_data = pd.concat(data_frames, ignore_index=True)\n",
        "    print(\"Concatenated data shape:\", concatenated_data.shape)\n",
        "    concatenated_data = concatenated_data.sample(frac=1).reset_index(drop=True)\n",
        "    print(\"Concatenated data shape after shuffling:\", concatenated_data.shape)\n",
        "    return concatenated_data\n",
        "\n",
        "# Load and preprocess the data\n",
        "concatenated_data = load_and_preprocess_data(file_paths)\n",
        "\n",
        "# Preprocess the concatenated data\n",
        "def preprocess_data(concatenated_data):\n",
        "    features = concatenated_data.iloc[:, :-1].values\n",
        "    labels = concatenated_data.iloc[:, -1].values\n",
        "    \n",
        "    # Label Encoding\n",
        "    label_encoder = LabelEncoder()\n",
        "    labels_encoded = label_encoder.fit_transform(labels)\n",
        "    \n",
        "    # One-Hot Encoding\n",
        "    onehot_encoder = OneHotEncoder()\n",
        "    labels_encoded = labels_encoded.reshape(-1, 1)\n",
        "    labels_onehot = onehot_encoder.fit_transform(labels_encoded).toarray()\n",
        "    \n",
        "    return features, labels_onehot\n",
        "\n",
        "# Create sequences\n",
        "def create_sequences(features, labels, sequence_length=30):\n",
        "    X, y = [], []\n",
        "    for i in range(len(features) - sequence_length + 1):\n",
        "        end_ix = i + sequence_length\n",
        "        seq_x, seq_y = features[i:end_ix], labels[end_ix - 1]\n",
        "        X.append(seq_x)\n",
        "        y.append(seq_y)\n",
        "    return np.array(X), np.array(y)\n",
        "\n",
        "# Preprocess the data\n",
        "features, labels_onehot = preprocess_data(concatenated_data)\n",
        "\n",
        "# Split data into train, validation, and test sets\n",
        "sequence_length = 30\n",
        "X_seq, y_seq = create_sequences(features, labels_onehot, sequence_length)\n",
        "\n",
        "X_train, X_temp, y_train, y_temp = train_test_split(X_seq, y_seq, test_size=0.2, random_state=42)\n",
        "X_val, X_test, y_val, y_test = train_test_split(X_temp, y_temp, test_size=0.5, random_state=42)\n",
        "\n",
        "# Denoising Autoencoder (DAE) for feature extraction\n",
        "input_dim = X_train.shape[2]\n",
        "latent_dim = 64\n",
        "\n",
        "input_layer = Input(shape=(sequence_length, input_dim))\n",
        "encoded = Dense(128, activation='relu')(input_layer)\n",
        "encoded = Dense(latent_dim, activation='relu')(encoded)\n",
        "decoded = Dense(128, activation='relu')(encoded)\n",
        "decoded = Dense(input_dim, activation='sigmoid')(decoded)\n",
        "\n",
        "dae = Model(input_layer, decoded)\n",
        "dae.compile(optimizer='adam', loss='mean_squared_error')\n",
        "\n",
        "dae.fit(X_train, X_train, epochs=50, batch_size=64, validation_split=0.2)\n",
        "\n",
        "encoder = Model(input_layer, encoded)\n",
        "features_dae_train = encoder.predict(X_train)\n",
        "features_dae_val = encoder.predict(X_val)\n",
        "features_dae_test = encoder.predict(X_test)\n",
        "\n",
        "features_dae_train = np.array(features_dae_train)\n",
        "features_dae_val = np.array(features_dae_val)\n",
        "features_dae_test = np.array(features_dae_test)\n",
        "\n",
        "# Gated Recurrent Unit (GRU) for feature learning\n",
        "gru_input_shape = features_dae_train.shape[1:]\n",
        "\n",
        "gru_model = Sequential()\n",
        "gru_model.add(GRU(64, input_shape=gru_input_shape, return_sequences=True))\n",
        "gru_model.add(GRU(64, return_sequences=True))\n",
        "gru_model.add(GRU(64, return_sequences=True))\n",
        "gru_model.add(GRU(64))\n",
        "\n",
        "gru_model.add(Reshape((1, 64)))\n",
        "\n",
        "gru_output_train = gru_model.predict(features_dae_train)\n",
        "gru_output_val = gru_model.predict(features_dae_val)\n",
        "gru_output_test = gru_model.predict(features_dae_test)\n",
        "\n",
        "# Define metrics callback with REINFORCE algorithm\n",
        "class MetricsCallback(Callback):\n",
        "    def __init__(self, validation_data, fault_types):\n",
        "        super(MetricsCallback, self).__init__()\n",
        "        self.validation_data = validation_data\n",
        "        self.fault_types = fault_types\n",
        "        self.rewards = []\n",
        "\n",
        "    def on_epoch_end(self, epoch, logs=None):\n",
        "        X_val, y_val = self.validation_data\n",
        "        y_pred = self.model.predict(X_val)\n",
        "        y_pred_classes = np.argmax(y_pred, axis=1)\n",
        "        y_true = np.argmax(y_val, axis=1)\n",
        "\n",
        "        precision = precision_score(y_true, y_pred_classes, average='weighted')\n",
        "        recall = recall_score(y_true, y_pred_classes, average='weighted')\n",
        "        f1 = f1_score(y_true, y_pred_classes, average='weighted')\n",
        "\n",
        "        # Calculate reward based on achieved metrics\n",
        "        reward = (precision >= 0.9886 and recall >= 0.9890 and f1 >= 0.9888) - 1\n",
        "        self.rewards.append(reward)\n",
        "\n",
        "        print(f\"Validation Precision: {precision:.4f}, Recall: {recall:.4f}, F1-Score: {f1:.4f}\")\n",
        "\n",
        "        if precision >= 0.9886 and recall >= 0.9890 and f1 >= 0.9888:\n",
        "            print(\"Achieved desired metrics. Stopping training.\")\n",
        "            self.model.stop_training = True\n",
        "            \n",
        "# Define fault_types\n",
        "fault_types = ['delay-time', 'gain', 'healthy', 'noise', 'packetloss']\n",
        "\n",
        "# Define hypermodel with REINFORCE\n",
        "class CustomHyperModel(HyperParameters):\n",
        "    def __init__(self, X_train, y_train, X_val, y_val, fault_types):\n",
        "        self.X_train = X_train\n",
        "        self.y_train = y_train\n",
        "        self.X_val = X_val\n",
        "        self.y_val = y_val\n",
        "        self.fault_types = fault_types  \n",
        "\n",
        "    def build(self, hp):\n",
        "        model = Sequential()\n",
        "        \n",
        "        cnn_layers = hp.Int('cnn_layers', min_value=0, max_value=5, default=5)\n",
        "        lstm_layers = hp.Int('lstm_layers', min_value=0, max_value=5, default=4)\n",
        "        dense_layers = hp.Int('dense_layers', min_value=0, max_value=5, default=0)\n",
        "        epochs = hp.Int('epochs', min_value=50, max_value=900, default=850)\n",
        "        max_pooling = hp.Int('max_pooling', min_value=0, max_value=1, default=1)\n",
        "        dropout = hp.Float('dropout', min_value=0, max_value=0.5, default=0)\n",
        "        batch_norm = hp.Int('batch_norm', min_value=0, max_value=2, default=2)\n",
        "        batch_size = hp.Int('batch_size', min_value=64, max_value=150, default=64)\n",
        "        learning_rate = hp.Float('learning_rate', min_value=0.0001, max_value=0.001, default=0.0005, sampling='linear')\n",
        "\n",
        "        for i in range(cnn_layers):\n",
        "            model.add(Conv1D(filters=8, kernel_size=2, activation='relu', padding='same', input_shape=(sequence_length, latent_dim)))\n",
        "            if batch_norm:\n",
        "                model.add(BatchNormalization())\n",
        "\n",
        "        if max_pooling:\n",
        "            model.add(MaxPooling1D(pool_size=2))\n",
        "\n",
        "        for i in range(lstm_layers):\n",
        "            model.add(LSTM(units=64, activation='relu', return_sequences=True))\n",
        "            if batch_norm:\n",
        "                model.add(BatchNormalization())\n",
        "\n",
        "        model.add(Flatten())\n",
        "\n",
        "        for i in range(dense_layers):\n",
        "            model.add(Dense(units=64, activation='relu'))\n",
        "            if dropout:\n",
        "                model.add(Dropout(dropout))\n",
        "            if batch_norm:\n",
        "                model.add(BatchNormalization())\n",
        "\n",
        "        model.add(Dense(units=len(self.fault_types), activation='softmax'))\n",
        "\n",
        "        model.compile(optimizer=Adam(learning_rate=learning_rate),\n",
        "                      loss='categorical_crossentropy',\n",
        "                      metrics=['accuracy'])\n",
        "\n",
        "        return model\n",
        "\n",
        "# Define parameters\n",
        "num_episodes = 100\n",
        "max_steps_per_episode = 50\n",
        "current_hyperparameters = {\n",
        "    'cnn_layers': 5,\n",
        "    'lstm_layers': 4,\n",
        "    'dense_layers': 0,\n",
        "    'epochs': 850,\n",
        "    'max_pooling': 1,\n",
        "    'dropout': 0,\n",
        "    'batch_norm': 2,\n",
        "    'batch_size': 64,\n",
        "    'learning_rate': 0.0005\n",
        "}\n",
        "\n",
        "print(\"Reinforcement Learning starts.\")\n",
        "# Training loop with REINFORCE\n",
        "for episode in range(num_episodes):\n",
        "    episode_rewards = []\n",
        "\n",
        "    for step in range(max_steps_per_episode):\n",
        "        sampled_hyperparameters = {\n",
        "            'cnn_layers': int(np.random.normal(current_hyperparameters['cnn_layers'], 0.1)),\n",
        "            'lstm_layers': int(np.random.normal(current_hyperparameters['lstm_layers'], 0.1)),\n",
        "            'dense_layers': int(np.random.normal(current_hyperparameters['dense_layers'], 0.1)),\n",
        "            'epochs': int(np.random.normal(current_hyperparameters['epochs'], 0.1)),\n",
        "            'max_pooling': int(np.random.normal(current_hyperparameters['max_pooling'], 0.1)),\n",
        "            'dropout': int(np.random.normal(current_hyperparameters['dropout'], 0.1)),\n",
        "            'batch_norm': int(np.random.normal(current_hyperparameters['batch_norm'], 0.1)),\n",
        "            'batch_size': int(np.random.normal(current_hyperparameters['batch_size'], 0.1)),\n",
        "            'learning_rate': np.random.normal(current_hyperparameters['learning_rate'], 0.1)\n",
        "        }\n",
        "        hp = HyperParameters()\n",
        "        # Define hyper-parameter ranges\n",
        "        hp.Int('cnn_layers', min_value=0, max_value=5, default=5)\n",
        "        hp.Int('lstm_layers', min_value=0, max_value=5, default=4)\n",
        "        hp.Int('dense_layers', min_value=0, max_value=5, default=0)\n",
        "        hp.Int('epochs', min_value=50, max_value=900, default=850)\n",
        "        hp.Int('max_pooling', min_value=0, max_value=1, default=1)\n",
        "        hp.Float('dropout', min_value=0, max_value=0.5, default=0)\n",
        "        hp.Int('batch_norm', min_value=0, max_value=2, default=2)\n",
        "        hp.Int('batch_size', min_value=64, max_value=150, default=64)\n",
        "        hp.Float('learning_rate', min_value=0.0001, max_value=0.001, default=0.0005, sampling='linear')\n",
        "\n",
        "        # Pass the HyperParameters object to the CustomHyperModel\n",
        "        hypermodel = CustomHyperModel(features_dae_train, y_train, features_dae_val, y_val, fault_types)\n",
        "\n",
        "        # Build the model with sampled hyperparameters\n",
        "        model = hypermodel.build(hp)\n",
        "\n",
        "        # Train the model\n",
        "        history = model.fit(\n",
        "            features_dae_train,\n",
        "            y_train,\n",
        "            validation_data=(features_dae_val, y_val),\n",
        "            batch_size=sampled_hyperparameters['batch_size'],\n",
        "            epochs=sampled_hyperparameters['epochs'],\n",
        "            verbose=0\n",
        "        )\n",
        "\n",
        "        # Evaluate the model\n",
        "        loss, accuracy = model.evaluate(features_dae_val, y_val, verbose=0)\n",
        "\n",
        "        # Calculate reward based on accuracy\n",
        "        reward = accuracy - 0.5  # Example reward function, you can adjust this based on your specific objectives\n",
        "\n",
        "        # Append reward to episode rewards\n",
        "        episode_rewards.append(reward)\n",
        "\n",
        "    # Update hyperparameters using REINFORCE algorithm\n",
        "    for hp_name, reward in zip(current_hyperparameters.keys(), episode_rewards):\n",
        "        current_hyperparameters[hp_name] += 0.01 * reward  # Adjust the update factor as needed\n",
        "\n",
        "    print(f\"Episode {episode + 1}: Rewards - {np.mean(episode_rewards)}\")\n",
        "\n",
        "print(\"Reinforcement Learning stops.\")\n",
        "\n",
        "# Get the best hyperparameters\n",
        "best_hyperparameters = current_hyperparameters\n",
        "print(\"Best Hyperparameters:\", best_hyperparameters)\n",
        "\n",
        "# Build the final model with the best hyperparameters\n",
        "best_hypermodel = CustomHyperModel(features_dae_train, y_train, features_dae_val, y_val, fault_types)\n",
        "best_model = best_hypermodel.build(best_hyperparameters)\n",
        "\n",
        "# Train the final model\n",
        "history = best_model.fit(\n",
        "    features_dae_train,\n",
        "    y_train,\n",
        "    validation_data=(features_dae_val, y_val),\n",
        "    batch_size=best_hyperparameters['batch_size'],\n",
        "    epochs=best_hyperparameters['epochs'],\n",
        "    callbacks=[MetricsCallback((features_dae_val, y_val), fault_types)]\n",
        ")\n",
        "\n",
        "# Save the best model\n",
        "best_model.save('best_model.h5')\n",
        "\n",
        "# Calculate metrics on the test set\n",
        "y_pred = best_model.predict(features_dae_test)\n",
        "y_pred_classes = np.argmax(y_pred, axis=1)\n",
        "y_true = np.argmax(y_test, axis=1)\n",
        "\n",
        "precision = precision_score(y_true, y_pred_classes, average=None, zero_division=1)\n",
        "recall = recall_score(y_true, y_pred_classes, average=None)\n",
        "f1 = f1_score(y_true, y_pred_classes, average=None)\n",
        "\n",
        "# Plotting precision, recall, and F1-score in one graph\n",
        "x = np.arange(len(fault_types))\n",
        "width = 0.2\n",
        "\n",
        "fig, ax = plt.subplots(figsize=(10, 6))\n",
        "rects1 = ax.bar(x - width, precision, width, label='Precision')\n",
        "rects2 = ax.bar(x, recall, width, label='Recall')\n",
        "rects3 = ax.bar(x + width, f1, width, label='F1-score')\n",
        "\n",
        "ax.set_ylabel('Scores')\n",
        "ax.set_title('Scores by fault types')\n",
        "ax.set_xticks(x)\n",
        "ax.set_xticklabels(fault_types)\n",
        "ax.legend()\n",
        "\n",
        "fig.tight_layout()\n",
        "\n",
        "# Save the graph\n",
        "plt.savefig('scores.png')\n"
      ]
    }
  ],
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