Lab3-AE-For-Anomaly-Detection.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"images/header.png\" alt=\"Header\" style=\"width: 400px;\"/>\n",
    "<h1 align=\"center\">Predictive Maintenance using NVIDIA RAPIDS and Deep Learning Models</h1>\n",
    "<h4 align=\"center\">Part 3: Training Autoencoder for Anomaly Detection</h4>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Overview"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the previous lab, we used LSTMs to predict hard drive failures. While timeseries prediction is a very well-studied approach in predictive maintenance, in this section we focus on a different deep learning technique, which is called __anomaly detection__. In a nutshell, considering the fact that only a small portion of the hard drives are defective in our use case, we can treat those as anomalies. For example, out of three million samples we dealt with in our training set within the previous lab, only 256 samples were defective. Those are \"anomalies\" among the big pool of well-functioning hard drives. Now, if we could have a model capable of learning what a proper functioning hard drive looks like, it could detect hard drives with a different behavior pattern.\n",
    "\n",
    "\n",
    "This lab covers the following topics:\n",
    "\n",
    "* [Autoencoders](#1)\n",
    "    * [Exercise 1: Retrieve the output labels](#e1)\n",
    "    * [Exercise 2: Filtering the labels](#e2)\n",
    "    * [Testing Models](#2)\n",
    "        * [LSTM Autoencoder Model](#3)\n",
    "        * [1D Convolution Model](#3b)\n",
    "* [Hyperparameter Optimization](#e4)\n",
    "    * [Exercise 3: Experimenting with Hyperparameters](#e4)\n",
    "    * [Exercise 4: Update code to use specified validation data](#e5)\n",
    "    * [Exercise 5: Create HPO result plots](#e6)\n",
    "\n",
    "In this lab, we will leverage the same Backblaze Hard Drive SMART data to train an Autoencoder model that will detect anomaly activity.  We'll leverage the time series sequences that we created in the previous step to train an Autoencoder Model to identify when there are significant variances from expected sensor data.  These variances can be used to alert support teams that there are potential issues with the system.\n",
    "\n",
    "You'll see that autoencoders (AEs) are relatively easy to use, but let's first walk through the basics of using autoencoders.\n",
    "\n",
    "We can use unsupervised learning algorithms like a deep autoencoder network to perform anomaly detection. A deep autoencoder takes an input X and attempts to map it back to itself; in essence, the autoencoder is trying to learn the identity function of the input data. Since learning the identity function is pretty trivial, we force the hidden layers of our autoencoder to be of lower dimensionality than the previous layer. This constraint forces the autoencoder to learn patterns in the data, and really learn efficient representations. \n",
    "\n",
    "<img src=\"img/AE_img-2.png\" style=\"margin-top:10px;\"/>\n",
    "<p style=\"text-align: center;color:gray\"> Figure 1. Using an autoencoder to map a timeseries data back to itself</p>\n",
    "\n",
    "To train this type of neural network, we can calculate the difference between the input and the output and use that loss, typically called a reconstruction loss, as our loss function for training our model. The idea is that our autoencoder is essentially learning the data generating distribution for a unique dataset, and when we feed data generated from a different distribution into our autoencoder, our autoencoder will do a very poor job of reconstructing the input from the second source and our reconstruction error will be high; this indicates that our input is anomalous instead of just an extreme value.\n",
    "\n",
    "<img src=\"img/AE_img-3.png\" style=\"margin-top:10px;\"/>\n",
    "<p style=\"text-align: center;color:gray\"> Figure 2. The predicted and ground-truth timeseries comparison after applying the autoencoder</p>\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a name=\"1\"></a>\n",
    "## Autoencoders"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Autoencoders are a subset of neural network architectures where the output dimension is the same as the input dimension. Autoencoders have two networks, an encoder and a decoder.  The encoder encodes its input data into a smaller dimensional space, called the latent space.  The decoder network tries to reconstruct the original data from the latent encoding. Typically, the encoder and decoder are symmetric, and the latent space is a bottleneck. The autoencoder has to learn essential characteristics of the data to be able to do a high-quality reconstruction of the data during decode.\n",
    "\n",
    "\n",
    "\n",
    "<img src=\"img/AE_img-1.png\" style=\"margin-top:10px;\"/>\n",
    "<p style=\"text-align: center;color:gray\"> Figure 3.  Structure of an auto-encoder model</p>\n",
    "\n",
    "Autoencoders can be built using many different layer types based on the desire goal of the model.  In this lab, we'll experiment using two different autoencoder models.  One built with LSTM layers and one built on top of 1D-Convolution.  You'll have the opportunity to test both.  Additionally, you'll see that we leverage Dropout layers as a way to control overfitting by randomly omitting subsets of features at each iteration of a training procedure. To begin, let’s import some libraries that we are going to use throughout this lab."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "# Import libraries that will be needed for the lab\n",
    "import os\n",
    "import sys\n",
    "import time\n",
    "import logging\n",
    "import importlib\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import collections\n",
    "from tqdm import tqdm\n",
    "\n",
    "import warnings\n",
    "warnings.simplefilter(action='ignore', category=FutureWarning)\n",
    "\n",
    "import tensorflow as tf\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.preprocessing import MinMaxScaler, StandardScaler\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import roc_curve, auc,precision_recall_fscore_support, average_precision_score, precision_recall_curve, auc, confusion_matrix,accuracy_score\n",
    "\n",
    "from tensorflow.keras import initializers, regularizers\n",
    "from tensorflow.keras.models import Model, Sequential\n",
    "from tensorflow.keras.optimizers import Adam, RMSprop\n",
    "from tensorflow.keras.layers import *\n",
    "from tensorflow.keras.callbacks import ModelCheckpoint\n",
    "from tensorflow.keras.layers import Input, LSTM, BatchNormalization, LeakyReLU, Dense, Reshape, Flatten, Dropout, multiply, GaussianNoise, MaxPooling2D, concatenate\n",
    "\n",
    "from tensorflow.keras import backend as K\n",
    "\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn import preprocessing\n",
    "from sklearn.metrics import *\n",
    "\n",
    "import pickle\n",
    "import gc\n",
    "\n",
    "import random\n",
    "\n",
    "random.seed(123)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a name=\"1b\"></a>\n",
    "### Input Data Set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using 16-day sequence data set\n"
     ]
    }
   ],
   "source": [
    "data_dir = \"./data/\"\n",
    "\n",
    "print('Using 16-day sequence data set')\n",
    "sequence_length = 16        # Number of days in sequence to train\n",
    "wsize = 16\n",
    "train_fname = data_dir + 'Lab3-AE-train-16day-8step.pkl'\n",
    "test_fname = data_dir + 'Lab3-AE-test-16day-8step.pkl'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Set Information"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we experienced in the previous exercises, preparing large data sets can take some time.  So, for this exercise, we have created larger training and validation datasets which we will use for our training and testing.  \n",
    "\n",
    "The datasets consists of 50000 sequences.  Each sequence is for an individual drive and tracks the SMART data features over 7 continuous days.  Since we will be training an Autoencoder to identify sequences for normal hard drive operation, we have not include any sequences that ended in drive failures.\n",
    "\n",
    "When we created these sequences, we assigned them a sequence id (field: \"ae_seq_id\") and a day number in the sequence (field: \"ae_seq_data_id\" which goes from 0-15)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Reading Training and Validation Datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train Original shape: (74000, 46)\n"
     ]
    }
   ],
   "source": [
    "# Reading larger training and test data set with only ST4000DM000 data\n",
    "df = pd.read_pickle(train_fname)\n",
    "print('Train Original shape:', df.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's take a look at our data set.  We'll print out the first 2 sequences.  You'll see the sequence id fields we discussed above."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
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       "      <td>100.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>94.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>72.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>200.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>195480</th>\n",
       "      <td>0.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>2017-08-09</td>\n",
       "      <td>Z300HM1N</td>\n",
       "      <td>ST4000DM000</td>\n",
       "      <td>4.000787e+12</td>\n",
       "      <td>0.0</td>\n",
       "      <td>171503104.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>100.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>94.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>200.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>195481</th>\n",
       "      <td>0.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>2017-08-10</td>\n",
       "      <td>Z300HM1N</td>\n",
       "      <td>ST4000DM000</td>\n",
       "      <td>4.000787e+12</td>\n",
       "      <td>0.0</td>\n",
       "      <td>202534848.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>100.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>94.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>71.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>200.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>195482</th>\n",
       "      <td>0.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>2017-08-11</td>\n",
       "      <td>Z300HM1N</td>\n",
       "      <td>ST4000DM000</td>\n",
       "      <td>4.000787e+12</td>\n",
       "      <td>0.0</td>\n",
       "      <td>243275368.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>100.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>94.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>200.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>195483</th>\n",
       "      <td>0.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>2017-08-12</td>\n",
       "      <td>Z300HM1N</td>\n",
       "      <td>ST4000DM000</td>\n",
       "      <td>4.000787e+12</td>\n",
       "      <td>0.0</td>\n",
       "      <td>76567144.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>100.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>94.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>200.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>200466</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2017-04-07</td>\n",
       "      <td>W300AZR3</td>\n",
       "      <td>ST4000DM000</td>\n",
       "      <td>4.000787e+12</td>\n",
       "      <td>0.0</td>\n",
       "      <td>116265744.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>100.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>97.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>87.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>200.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>200467</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2017-04-08</td>\n",
       "      <td>W300AZR3</td>\n",
       "      <td>ST4000DM000</td>\n",
       "      <td>4.000787e+12</td>\n",
       "      <td>0.0</td>\n",
       "      <td>154691184.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>100.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>97.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>86.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>200.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>200468</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2017-04-09</td>\n",
       "      <td>W300AZR3</td>\n",
       "      <td>ST4000DM000</td>\n",
       "      <td>4.000787e+12</td>\n",
       "      <td>0.0</td>\n",
       "      <td>201368272.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>100.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>97.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>86.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>200.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>200469</th>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2017-04-10</td>\n",
       "      <td>W300AZR3</td>\n",
       "      <td>ST4000DM000</td>\n",
       "      <td>4.000787e+12</td>\n",
       "      <td>0.0</td>\n",
       "      <td>243208416.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>100.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>97.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>200.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>200470</th>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2017-04-11</td>\n",
       "      <td>W300AZR3</td>\n",
       "      <td>ST4000DM000</td>\n",
       "      <td>4.000787e+12</td>\n",
       "      <td>0.0</td>\n",
       "      <td>42572824.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>100.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>97.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>200.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>200471</th>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2017-04-12</td>\n",
       "      <td>W300AZR3</td>\n",
       "      <td>ST4000DM000</td>\n",
       "      <td>4.000787e+12</td>\n",
       "      <td>0.0</td>\n",
       "      <td>80559216.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>100.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>97.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>200.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>200472</th>\n",
       "      <td>1.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2017-04-13</td>\n",
       "      <td>W300AZR3</td>\n",
       "      <td>ST4000DM000</td>\n",
       "      <td>4.000787e+12</td>\n",
       "      <td>0.0</td>\n",
       "      <td>120589400.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>100.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>97.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>83.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>200.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>200473</th>\n",
       "      <td>1.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2017-04-14</td>\n",
       "      <td>W300AZR3</td>\n",
       "      <td>ST4000DM000</td>\n",
       "      <td>4.000787e+12</td>\n",
       "      <td>0.0</td>\n",
       "      <td>160877680.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>100.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>97.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>83.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>200.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>200474</th>\n",
       "      <td>1.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>2017-04-15</td>\n",
       "      <td>W300AZR3</td>\n",
       "      <td>ST4000DM000</td>\n",
       "      <td>4.000787e+12</td>\n",
       "      <td>0.0</td>\n",
       "      <td>198030056.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>100.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>97.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>82.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>200.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>200475</th>\n",
       "      <td>1.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>2017-04-16</td>\n",
       "      <td>W300AZR3</td>\n",
       "      <td>ST4000DM000</td>\n",
       "      <td>4.000787e+12</td>\n",
       "      <td>0.0</td>\n",
       "      <td>237670544.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>100.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>97.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>81.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>200.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>200476</th>\n",
       "      <td>1.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>2017-04-17</td>\n",
       "      <td>W300AZR3</td>\n",
       "      <td>ST4000DM000</td>\n",
       "      <td>4.000787e+12</td>\n",
       "      <td>0.0</td>\n",
       "      <td>32806016.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>100.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>97.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>81.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>200.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>200477</th>\n",
       "      <td>1.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>2017-04-18</td>\n",
       "      <td>W300AZR3</td>\n",
       "      <td>ST4000DM000</td>\n",
       "      <td>4.000787e+12</td>\n",
       "      <td>0.0</td>\n",
       "      <td>74222592.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>100.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>97.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>200.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>200478</th>\n",
       "      <td>1.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>2017-04-19</td>\n",
       "      <td>W300AZR3</td>\n",
       "      <td>ST4000DM000</td>\n",
       "      <td>4.000787e+12</td>\n",
       "      <td>0.0</td>\n",
       "      <td>118242064.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>100.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>97.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>200.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>200479</th>\n",
       "      <td>1.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>2017-04-20</td>\n",
       "      <td>W300AZR3</td>\n",
       "      <td>ST4000DM000</td>\n",
       "      <td>4.000787e+12</td>\n",
       "      <td>0.0</td>\n",
       "      <td>159253192.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>100.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>97.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>79.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>200.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>200480</th>\n",
       "      <td>1.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>2017-04-21</td>\n",
       "      <td>W300AZR3</td>\n",
       "      <td>ST4000DM000</td>\n",
       "      <td>4.000787e+12</td>\n",
       "      <td>0.0</td>\n",
       "      <td>199694096.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>100.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>97.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>200.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>200481</th>\n",
       "      <td>1.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>2017-04-22</td>\n",
       "      <td>W300AZR3</td>\n",
       "      <td>ST4000DM000</td>\n",
       "      <td>4.000787e+12</td>\n",
       "      <td>0.0</td>\n",
       "      <td>239649088.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>100.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>97.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>200.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>32 rows × 46 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        ae_seq_id  ae_seq_data_id       date serial_number        model  \\\n",
       "195468        0.0             0.0 2017-07-28      Z300HM1N  ST4000DM000   \n",
       "195469        0.0             1.0 2017-07-29      Z300HM1N  ST4000DM000   \n",
       "195470        0.0             2.0 2017-07-30      Z300HM1N  ST4000DM000   \n",
       "195471        0.0             3.0 2017-07-31      Z300HM1N  ST4000DM000   \n",
       "195472        0.0             4.0 2017-08-01      Z300HM1N  ST4000DM000   \n",
       "195473        0.0             5.0 2017-08-02      Z300HM1N  ST4000DM000   \n",
       "195474        0.0             6.0 2017-08-03      Z300HM1N  ST4000DM000   \n",
       "195475        0.0             7.0 2017-08-04      Z300HM1N  ST4000DM000   \n",
       "195476        0.0             8.0 2017-08-05      Z300HM1N  ST4000DM000   \n",
       "195477        0.0             9.0 2017-08-06      Z300HM1N  ST4000DM000   \n",
       "195478        0.0            10.0 2017-08-07      Z300HM1N  ST4000DM000   \n",
       "195479        0.0            11.0 2017-08-08      Z300HM1N  ST4000DM000   \n",
       "195480        0.0            12.0 2017-08-09      Z300HM1N  ST4000DM000   \n",
       "195481        0.0            13.0 2017-08-10      Z300HM1N  ST4000DM000   \n",
       "195482        0.0            14.0 2017-08-11      Z300HM1N  ST4000DM000   \n",
       "195483        0.0            15.0 2017-08-12      Z300HM1N  ST4000DM000   \n",
       "200466        1.0             0.0 2017-04-07      W300AZR3  ST4000DM000   \n",
       "200467        1.0             1.0 2017-04-08      W300AZR3  ST4000DM000   \n",
       "200468        1.0             2.0 2017-04-09      W300AZR3  ST4000DM000   \n",
       "200469        1.0             3.0 2017-04-10      W300AZR3  ST4000DM000   \n",
       "200470        1.0             4.0 2017-04-11      W300AZR3  ST4000DM000   \n",
       "200471        1.0             5.0 2017-04-12      W300AZR3  ST4000DM000   \n",
       "200472        1.0             6.0 2017-04-13      W300AZR3  ST4000DM000   \n",
       "200473        1.0             7.0 2017-04-14      W300AZR3  ST4000DM000   \n",
       "200474        1.0             8.0 2017-04-15      W300AZR3  ST4000DM000   \n",
       "200475        1.0             9.0 2017-04-16      W300AZR3  ST4000DM000   \n",
       "200476        1.0            10.0 2017-04-17      W300AZR3  ST4000DM000   \n",
       "200477        1.0            11.0 2017-04-18      W300AZR3  ST4000DM000   \n",
       "200478        1.0            12.0 2017-04-19      W300AZR3  ST4000DM000   \n",
       "200479        1.0            13.0 2017-04-20      W300AZR3  ST4000DM000   \n",
       "200480        1.0            14.0 2017-04-21      W300AZR3  ST4000DM000   \n",
       "200481        1.0            15.0 2017-04-22      W300AZR3  ST4000DM000   \n",
       "\n",
       "        capacity_bytes  failure  smart_1_raw  smart_4_raw  smart_5_raw  ...  \\\n",
       "195468    4.000787e+12      0.0  169386552.0         24.0          0.0  ...   \n",
       "195469    4.000787e+12      0.0  220437952.0         24.0          0.0  ...   \n",
       "195470    4.000787e+12      0.0   12172112.0         24.0          0.0  ...   \n",
       "195471    4.000787e+12      0.0   33350928.0         24.0          0.0  ...   \n",
       "195472    4.000787e+12      0.0   98402824.0         24.0          0.0  ...   \n",
       "195473    4.000787e+12      0.0  173407640.0         24.0          0.0  ...   \n",
       "195474    4.000787e+12      0.0   59425848.0         24.0          0.0  ...   \n",
       "195475    4.000787e+12      0.0  178379120.0         24.0          0.0  ...   \n",
       "195476    4.000787e+12      0.0   21791640.0         24.0          0.0  ...   \n",
       "195477    4.000787e+12      0.0  151298144.0         24.0          0.0  ...   \n",
       "195478    4.000787e+12      0.0   29705424.0         24.0          0.0  ...   \n",
       "195479    4.000787e+12      0.0  103469832.0         24.0          0.0  ...   \n",
       "195480    4.000787e+12      0.0  171503104.0         25.0          0.0  ...   \n",
       "195481    4.000787e+12      0.0  202534848.0         25.0          0.0  ...   \n",
       "195482    4.000787e+12      0.0  243275368.0         25.0          0.0  ...   \n",
       "195483    4.000787e+12      0.0   76567144.0         25.0          0.0  ...   \n",
       "200466    4.000787e+12      0.0  116265744.0         20.0          0.0  ...   \n",
       "200467    4.000787e+12      0.0  154691184.0         20.0          0.0  ...   \n",
       "200468    4.000787e+12      0.0  201368272.0         20.0          0.0  ...   \n",
       "200469    4.000787e+12      0.0  243208416.0         20.0          0.0  ...   \n",
       "200470    4.000787e+12      0.0   42572824.0         20.0          0.0  ...   \n",
       "200471    4.000787e+12      0.0   80559216.0         20.0          0.0  ...   \n",
       "200472    4.000787e+12      0.0  120589400.0         20.0          0.0  ...   \n",
       "200473    4.000787e+12      0.0  160877680.0         20.0          0.0  ...   \n",
       "200474    4.000787e+12      0.0  198030056.0         20.0          0.0  ...   \n",
       "200475    4.000787e+12      0.0  237670544.0         20.0          0.0  ...   \n",
       "200476    4.000787e+12      0.0   32806016.0         20.0          0.0  ...   \n",
       "200477    4.000787e+12      0.0   74222592.0         20.0          0.0  ...   \n",
       "200478    4.000787e+12      0.0  118242064.0         20.0          0.0  ...   \n",
       "200479    4.000787e+12      0.0  159253192.0         20.0          0.0  ...   \n",
       "200480    4.000787e+12      0.0  199694096.0         20.0          0.0  ...   \n",
       "200481    4.000787e+12      0.0  239649088.0         20.0          0.0  ...   \n",
       "\n",
       "        smart_184_normalized  smart_187_normalized  smart_188_normalized  \\\n",
       "195468                 100.0                 100.0                 100.0   \n",
       "195469                 100.0                 100.0                 100.0   \n",
       "195470                 100.0                 100.0                 100.0   \n",
       "195471                 100.0                 100.0                 100.0   \n",
       "195472                 100.0                 100.0                 100.0   \n",
       "195473                 100.0                 100.0                 100.0   \n",
       "195474                 100.0                 100.0                 100.0   \n",
       "195475                 100.0                 100.0                 100.0   \n",
       "195476                 100.0                 100.0                 100.0   \n",
       "195477                 100.0                 100.0                 100.0   \n",
       "195478                 100.0                 100.0                 100.0   \n",
       "195479                 100.0                 100.0                 100.0   \n",
       "195480                 100.0                 100.0                 100.0   \n",
       "195481                 100.0                 100.0                 100.0   \n",
       "195482                 100.0                 100.0                 100.0   \n",
       "195483                 100.0                 100.0                 100.0   \n",
       "200466                 100.0                 100.0                 100.0   \n",
       "200467                 100.0                 100.0                 100.0   \n",
       "200468                 100.0                 100.0                 100.0   \n",
       "200469                 100.0                 100.0                 100.0   \n",
       "200470                 100.0                 100.0                 100.0   \n",
       "200471                 100.0                 100.0                 100.0   \n",
       "200472                 100.0                 100.0                 100.0   \n",
       "200473                 100.0                 100.0                 100.0   \n",
       "200474                 100.0                 100.0                 100.0   \n",
       "200475                 100.0                 100.0                 100.0   \n",
       "200476                 100.0                 100.0                 100.0   \n",
       "200477                 100.0                 100.0                 100.0   \n",
       "200478                 100.0                 100.0                 100.0   \n",
       "200479                 100.0                 100.0                 100.0   \n",
       "200480                 100.0                 100.0                 100.0   \n",
       "200481                 100.0                 100.0                 100.0   \n",
       "\n",
       "        smart_189_normalized  smart_190_normalized  smart_193_normalized  \\\n",
       "195468                  94.0                  75.0                  72.0   \n",
       "195469                  94.0                  76.0                  72.0   \n",
       "195470                  94.0                  75.0                  72.0   \n",
       "195471                  94.0                  75.0                  72.0   \n",
       "195472                  94.0                  75.0                  72.0   \n",
       "195473                  94.0                  75.0                  72.0   \n",
       "195474                  94.0                  75.0                  72.0   \n",
       "195475                  94.0                  75.0                  72.0   \n",
       "195476                  94.0                  75.0                  72.0   \n",
       "195477                  94.0                  75.0                  72.0   \n",
       "195478                  94.0                  75.0                  72.0   \n",
       "195479                  94.0                  75.0                  72.0   \n",
       "195480                  94.0                  75.0                  71.0   \n",
       "195481                  94.0                  76.0                  71.0   \n",
       "195482                  94.0                  76.0                  70.0   \n",
       "195483                  94.0                  75.0                  70.0   \n",
       "200466                  97.0                  75.0                  87.0   \n",
       "200467                  97.0                  75.0                  86.0   \n",
       "200468                  97.0                  74.0                  86.0   \n",
       "200469                  97.0                  74.0                  85.0   \n",
       "200470                  97.0                  75.0                  85.0   \n",
       "200471                  97.0                  74.0                  84.0   \n",
       "200472                  97.0                  75.0                  83.0   \n",
       "200473                  97.0                  75.0                  83.0   \n",
       "200474                  97.0                  75.0                  82.0   \n",
       "200475                  97.0                  75.0                  81.0   \n",
       "200476                  97.0                  75.0                  81.0   \n",
       "200477                  97.0                  75.0                  80.0   \n",
       "200478                  97.0                  76.0                  80.0   \n",
       "200479                  97.0                  76.0                  79.0   \n",
       "200480                  97.0                  77.0                  78.0   \n",
       "200481                  97.0                  77.0                  78.0   \n",
       "\n",
       "        smart_194_normalized  smart_197_normalized  smart_198_normalized  \\\n",
       "195468                  25.0                 100.0                 100.0   \n",
       "195469                  24.0                 100.0                 100.0   \n",
       "195470                  25.0                 100.0                 100.0   \n",
       "195471                  25.0                 100.0                 100.0   \n",
       "195472                  25.0                 100.0                 100.0   \n",
       "195473                  25.0                 100.0                 100.0   \n",
       "195474                  25.0                 100.0                 100.0   \n",
       "195475                  25.0                 100.0                 100.0   \n",
       "195476                  25.0                 100.0                 100.0   \n",
       "195477                  25.0                 100.0                 100.0   \n",
       "195478                  25.0                 100.0                 100.0   \n",
       "195479                  25.0                 100.0                 100.0   \n",
       "195480                  25.0                 100.0                 100.0   \n",
       "195481                  24.0                 100.0                 100.0   \n",
       "195482                  24.0                 100.0                 100.0   \n",
       "195483                  25.0                 100.0                 100.0   \n",
       "200466                  25.0                 100.0                 100.0   \n",
       "200467                  25.0                 100.0                 100.0   \n",
       "200468                  26.0                 100.0                 100.0   \n",
       "200469                  26.0                 100.0                 100.0   \n",
       "200470                  25.0                 100.0                 100.0   \n",
       "200471                  26.0                 100.0                 100.0   \n",
       "200472                  25.0                 100.0                 100.0   \n",
       "200473                  25.0                 100.0                 100.0   \n",
       "200474                  25.0                 100.0                 100.0   \n",
       "200475                  25.0                 100.0                 100.0   \n",
       "200476                  25.0                 100.0                 100.0   \n",
       "200477                  25.0                 100.0                 100.0   \n",
       "200478                  24.0                 100.0                 100.0   \n",
       "200479                  24.0                 100.0                 100.0   \n",
       "200480                  23.0                 100.0                 100.0   \n",
       "200481                  23.0                 100.0                 100.0   \n",
       "\n",
       "        smart_199_normalized  \n",
       "195468                 200.0  \n",
       "195469                 200.0  \n",
       "195470                 200.0  \n",
       "195471                 200.0  \n",
       "195472                 200.0  \n",
       "195473                 200.0  \n",
       "195474                 200.0  \n",
       "195475                 200.0  \n",
       "195476                 200.0  \n",
       "195477                 200.0  \n",
       "195478                 200.0  \n",
       "195479                 200.0  \n",
       "195480                 200.0  \n",
       "195481                 200.0  \n",
       "195482                 200.0  \n",
       "195483                 200.0  \n",
       "200466                 200.0  \n",
       "200467                 200.0  \n",
       "200468                 200.0  \n",
       "200469                 200.0  \n",
       "200470                 200.0  \n",
       "200471                 200.0  \n",
       "200472                 200.0  \n",
       "200473                 200.0  \n",
       "200474                 200.0  \n",
       "200475                 200.0  \n",
       "200476                 200.0  \n",
       "200477                 200.0  \n",
       "200478                 200.0  \n",
       "200479                 200.0  \n",
       "200480                 200.0  \n",
       "200481                 200.0  \n",
       "\n",
       "[32 rows x 46 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(2*sequence_length)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data preparation Routines"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To work with the data, we are going to build a more structured set of functions that are going to help us with data curation. As stated in the previous lab, the dataset contains duplicated columns with \"normalized\" data. Our first function, `remove_normalized`, is aimed to remove those."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Remove the \"normalized\" columns which are simply nomralized copies of the \"raw\" column\n",
    "\n",
    "def remove_normalized(df):\n",
    "    cols = [c for c in df.columns if (c.lower().find(\"normalized\")==-1)]\n",
    "    df=df[cols]\n",
    "    return df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a name=\"e1\"></a>\n",
    "\n",
    "### Exercise 1: Retrieve the output labels\n",
    "\n",
    "We would like to get the failure column in NumPy format. Please complete the following function to perform return the appropriate labels:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_Labels(df):\n",
    "    # return the proper column in NumPy format\n",
    "    y_labels = df['failure'].values\n",
    "    return y_labels"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Sort data based on `ae_seq_id` and `ae_seq_data_id` columns\n",
    "\n",
    "Prior to training, we need to ensure that all the data is properly sorted so that sequences are presented in the correct order.  Typically, we would sort the data by unique id and date/time.  When we created the data set for this exercise, we added two fields:\n",
    "<ul>\n",
    "    <li>ae_seq_id: Id number of unique sequence</li>\n",
    "    <li>ae_seq_data_id: Day number of record in sequence (day 0 - day (sequence_length-1))</li>\n",
    "</ul>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Sort data frame to ensure proper order\n",
    "\n",
    "def sort_dataframe(df):\n",
    "    print(\"Sorting the data frame based on pre-created sequence id and sequence day id\")\n",
    "    df['date'] = pd.to_datetime(df['date'])\n",
    "    \n",
    "    #sort dataframe by 'ae_seq_id' and 'ae_seq_data_id'\n",
    "    df = df.sort_values(by=['ae_seq_id', 'ae_seq_data_id'], axis=0, ascending=True)\n",
    "    df = df.reset_index(drop=True)\n",
    "    return df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next we have created some \"helper\" functions to:\n",
    "<ul>\n",
    "    <li>Remove columns that possess no useful information for training</li>\n",
    "    <li>Normalize the columns</li>\n",
    "</ul>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Remove columns unnecessary for training\n",
    "\n",
    "def remove_columns(df, drop_columns_list = ['date','serial_number',\n",
    "                                               'model', \n",
    "                                               'failure', \n",
    "                                               'capacity_bytes']):\n",
    "    print(\"removing unnecessary columns\")\n",
    "    df = df.drop(columns=drop_columns_list)\n",
    "    df = df.reset_index(drop=True)\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# This routine removes the normalized features and removes unnecessary columns\n",
    "\n",
    "def clean_dataframe(df,column_list=None):\n",
    "        \n",
    "    #sort the dataframe\n",
    "    df = sort_dataframe(df)\n",
    "\n",
    "    #remove normalized columns\n",
    "    df = remove_normalized(df)\n",
    "\n",
    "    #remove other unnecessary columns\n",
    "    if column_list is not None:\n",
    "        df = remove_columns(df, column_list)\n",
    "    \n",
    "    return df\n",
    "\n",
    "# This routine scales values for training and reshapes data as appropriate for LSTM or CONV1D training\n",
    "\n",
    "def normalize_df(df, ae_type=None,scaler=None):\n",
    "    \n",
    "    print(\"Normalizing input for \"+ae_type)\n",
    "    #normalize data with max/min values \n",
    "    df.fillna(0, inplace=True)\n",
    "    x = df.values\n",
    "    x = x.astype(np.float64)\n",
    "    if scaler is None:\n",
    "        scaler = preprocessing.StandardScaler().fit(x)\n",
    "    x = scaler.transform(x)\n",
    "    \n",
    "    #reshape to obtain appropriate input for autoencoder\n",
    "    if ae_type == \"lstm\": \n",
    "        x = x.reshape(int(x.shape[0]/sequence_length), sequence_length, x.shape[1])\n",
    "    elif ae_type == \"conv1d\": \n",
    "        tshape = x.shape\n",
    "        print(tshape, \"dropping \", tshape[0]-int(tshape[0]/w_size)*w_size, end = \" : reshaping to \")       \n",
    "        x = x[:int(tshape[0]/w_size)*w_size,:].reshape(int(tshape[0]/w_size), w_size, tshape[1])\n",
    "\n",
    "    print(x.shape)\n",
    "    \n",
    "    return x, scaler \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We are going to load both training and test sets below, starting with the training set. Note that unlike the LSTM models, here we do not require the `failure` columns. The class labels are not what we learn, rather we learn the training data pattern. In that sense, autoencoders are unsupervised models."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "processing the train set\n",
      "Sorting the data frame based on pre-created sequence id and sequence day id\n",
      "removing unnecessary columns\n",
      "Normalizing input for lstm\n",
      "(4625, 16, 21)\n"
     ]
    }
   ],
   "source": [
    "print(\"processing the train set\")\n",
    "\n",
    "df = clean_dataframe(df,['ae_seq_id', \n",
    "                            'ae_seq_data_id', \n",
    "                            'date','serial_number',\n",
    "                            'model', \n",
    "                            'failure', \n",
    "                           'capacity_bytes'])\n",
    "\n",
    "x_train, scaler = normalize_df(df,\"lstm\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's take a look at a few samples of the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>smart_1_raw</th>\n",
       "      <th>smart_4_raw</th>\n",
       "      <th>smart_5_raw</th>\n",
       "      <th>smart_7_raw</th>\n",
       "      <th>smart_9_raw</th>\n",
       "      <th>smart_12_raw</th>\n",
       "      <th>smart_183_raw</th>\n",
       "      <th>smart_184_raw</th>\n",
       "      <th>smart_187_raw</th>\n",
       "      <th>smart_188_raw</th>\n",
       "      <th>...</th>\n",
       "      <th>smart_190_raw</th>\n",
       "      <th>smart_192_raw</th>\n",
       "      <th>smart_193_raw</th>\n",
       "      <th>smart_194_raw</th>\n",
       "      <th>smart_197_raw</th>\n",
       "      <th>smart_198_raw</th>\n",
       "      <th>smart_199_raw</th>\n",
       "      <th>smart_240_raw</th>\n",
       "      <th>smart_241_raw</th>\n",
       "      <th>smart_242_raw</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>169386552.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>11790406.0</td>\n",
       "      <td>34692.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>25.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>57729.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>34450.0</td>\n",
       "      <td>3.177469e+10</td>\n",
       "      <td>1.425277e+11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>220437952.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12531683.0</td>\n",
       "      <td>34716.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>24.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>57731.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>34474.0</td>\n",
       "      <td>3.177673e+10</td>\n",
       "      <td>1.425649e+11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>12172112.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13249488.0</td>\n",
       "      <td>34740.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>25.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>57731.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>34498.0</td>\n",
       "      <td>3.177876e+10</td>\n",
       "      <td>1.426023e+11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>33350928.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13970733.0</td>\n",
       "      <td>34764.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>25.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>57731.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>34522.0</td>\n",
       "      <td>3.178076e+10</td>\n",
       "      <td>1.426377e+11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>98402824.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>14725252.0</td>\n",
       "      <td>34788.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>25.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>57731.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>34546.0</td>\n",
       "      <td>3.178302e+10</td>\n",
       "      <td>1.426726e+11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>173407640.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>15477836.0</td>\n",
       "      <td>34812.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>25.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>57731.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>34570.0</td>\n",
       "      <td>3.178512e+10</td>\n",
       "      <td>1.427067e+11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>59425848.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>16264486.0</td>\n",
       "      <td>34836.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>25.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>57731.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>34594.0</td>\n",
       "      <td>3.178752e+10</td>\n",
       "      <td>1.427406e+11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>178379120.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>17053336.0</td>\n",
       "      <td>34860.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>25.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>57731.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>34618.0</td>\n",
       "      <td>3.178998e+10</td>\n",
       "      <td>1.427768e+11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>21791640.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>17824256.0</td>\n",
       "      <td>34884.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>25.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>57731.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>34642.0</td>\n",
       "      <td>3.179215e+10</td>\n",
       "      <td>1.428109e+11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>151298144.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>18616634.0</td>\n",
       "      <td>34908.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>25.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>57731.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>34666.0</td>\n",
       "      <td>3.179427e+10</td>\n",
       "      <td>1.428451e+11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>29705424.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>19396235.0</td>\n",
       "      <td>34932.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>25.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>57731.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>34690.0</td>\n",
       "      <td>3.179640e+10</td>\n",
       "      <td>1.428825e+11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>103469832.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>20150989.0</td>\n",
       "      <td>34956.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>25.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>57732.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>34714.0</td>\n",
       "      <td>3.179845e+10</td>\n",
       "      <td>1.429180e+11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>171503104.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>20594596.0</td>\n",
       "      <td>34981.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>25.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>58409.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>34737.0</td>\n",
       "      <td>3.179961e+10</td>\n",
       "      <td>1.429477e+11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>202534848.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>20683853.0</td>\n",
       "      <td>35005.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>24.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>59661.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>34755.0</td>\n",
       "      <td>3.179972e+10</td>\n",
       "      <td>1.429751e+11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>243275368.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>20778485.0</td>\n",
       "      <td>35029.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>24.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>60884.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>34774.0</td>\n",
       "      <td>3.179984e+10</td>\n",
       "      <td>1.430094e+11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>76567144.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>21192916.0</td>\n",
       "      <td>35053.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>25.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>61772.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>34795.0</td>\n",
       "      <td>3.180088e+10</td>\n",
       "      <td>1.430484e+11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>116265744.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>120332000.0</td>\n",
       "      <td>33461.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>25.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>26818.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>33350.0</td>\n",
       "      <td>3.084112e+10</td>\n",
       "      <td>1.919088e+11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>154691184.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>120425993.0</td>\n",
       "      <td>33484.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>25.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>28084.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>33369.0</td>\n",
       "      <td>3.084124e+10</td>\n",
       "      <td>1.919419e+11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>201368272.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>120522492.0</td>\n",
       "      <td>33508.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>26.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>29240.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>33389.0</td>\n",
       "      <td>3.084135e+10</td>\n",
       "      <td>1.919757e+11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>243208416.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>120617730.0</td>\n",
       "      <td>33532.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>26.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>30410.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>33409.0</td>\n",
       "      <td>3.084147e+10</td>\n",
       "      <td>1.920092e+11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>42572824.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>120716059.0</td>\n",
       "      <td>33556.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>25.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>31656.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>33429.0</td>\n",
       "      <td>3.084159e+10</td>\n",
       "      <td>1.920439e+11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>80559216.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>120810048.0</td>\n",
       "      <td>33580.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>26.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>32938.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>33448.0</td>\n",
       "      <td>3.084171e+10</td>\n",
       "      <td>1.920758e+11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>120589400.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>120904269.0</td>\n",
       "      <td>33604.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>25.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>34195.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>33467.0</td>\n",
       "      <td>3.084182e+10</td>\n",
       "      <td>1.921093e+11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>160877680.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>121001196.0</td>\n",
       "      <td>33628.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>25.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>35548.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>33487.0</td>\n",
       "      <td>3.084194e+10</td>\n",
       "      <td>1.921437e+11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>198030056.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>121095069.0</td>\n",
       "      <td>33652.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>25.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>36779.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>33506.0</td>\n",
       "      <td>3.084206e+10</td>\n",
       "      <td>1.921758e+11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>237670544.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>121189166.0</td>\n",
       "      <td>33676.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>25.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>38030.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>33525.0</td>\n",
       "      <td>3.084218e+10</td>\n",
       "      <td>1.922090e+11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>32806016.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>121286158.0</td>\n",
       "      <td>33701.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>25.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>39338.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>33545.0</td>\n",
       "      <td>3.084230e+10</td>\n",
       "      <td>1.922431e+11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>74222592.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>121380839.0</td>\n",
       "      <td>33724.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>25.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>40607.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>33564.0</td>\n",
       "      <td>3.084241e+10</td>\n",
       "      <td>1.922767e+11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>118242064.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>121476046.0</td>\n",
       "      <td>33748.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>24.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>41828.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>33584.0</td>\n",
       "      <td>3.084253e+10</td>\n",
       "      <td>1.923092e+11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>159253192.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>121570379.0</td>\n",
       "      <td>33772.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>24.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>43101.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>33603.0</td>\n",
       "      <td>3.084265e+10</td>\n",
       "      <td>1.923429e+11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>199694096.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>121667225.0</td>\n",
       "      <td>33796.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>23.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>44376.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>33623.0</td>\n",
       "      <td>3.084277e+10</td>\n",
       "      <td>1.923776e+11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>239649088.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>121761333.0</td>\n",
       "      <td>33820.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>23.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>45637.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>33642.0</td>\n",
       "      <td>3.084288e+10</td>\n",
       "      <td>1.924095e+11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>32 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    smart_1_raw  smart_4_raw  smart_5_raw  smart_7_raw  smart_9_raw  \\\n",
       "0   169386552.0         24.0          0.0   11790406.0      34692.0   \n",
       "1   220437952.0         24.0          0.0   12531683.0      34716.0   \n",
       "2    12172112.0         24.0          0.0   13249488.0      34740.0   \n",
       "3    33350928.0         24.0          0.0   13970733.0      34764.0   \n",
       "4    98402824.0         24.0          0.0   14725252.0      34788.0   \n",
       "5   173407640.0         24.0          0.0   15477836.0      34812.0   \n",
       "6    59425848.0         24.0          0.0   16264486.0      34836.0   \n",
       "7   178379120.0         24.0          0.0   17053336.0      34860.0   \n",
       "8    21791640.0         24.0          0.0   17824256.0      34884.0   \n",
       "9   151298144.0         24.0          0.0   18616634.0      34908.0   \n",
       "10   29705424.0         24.0          0.0   19396235.0      34932.0   \n",
       "11  103469832.0         24.0          0.0   20150989.0      34956.0   \n",
       "12  171503104.0         25.0          0.0   20594596.0      34981.0   \n",
       "13  202534848.0         25.0          0.0   20683853.0      35005.0   \n",
       "14  243275368.0         25.0          0.0   20778485.0      35029.0   \n",
       "15   76567144.0         25.0          0.0   21192916.0      35053.0   \n",
       "16  116265744.0         20.0          0.0  120332000.0      33461.0   \n",
       "17  154691184.0         20.0          0.0  120425993.0      33484.0   \n",
       "18  201368272.0         20.0          0.0  120522492.0      33508.0   \n",
       "19  243208416.0         20.0          0.0  120617730.0      33532.0   \n",
       "20   42572824.0         20.0          0.0  120716059.0      33556.0   \n",
       "21   80559216.0         20.0          0.0  120810048.0      33580.0   \n",
       "22  120589400.0         20.0          0.0  120904269.0      33604.0   \n",
       "23  160877680.0         20.0          0.0  121001196.0      33628.0   \n",
       "24  198030056.0         20.0          0.0  121095069.0      33652.0   \n",
       "25  237670544.0         20.0          0.0  121189166.0      33676.0   \n",
       "26   32806016.0         20.0          0.0  121286158.0      33701.0   \n",
       "27   74222592.0         20.0          0.0  121380839.0      33724.0   \n",
       "28  118242064.0         20.0          0.0  121476046.0      33748.0   \n",
       "29  159253192.0         20.0          0.0  121570379.0      33772.0   \n",
       "30  199694096.0         20.0          0.0  121667225.0      33796.0   \n",
       "31  239649088.0         20.0          0.0  121761333.0      33820.0   \n",
       "\n",
       "    smart_12_raw  smart_183_raw  smart_184_raw  smart_187_raw  smart_188_raw  \\\n",
       "0           24.0            1.0            0.0            0.0            0.0   \n",
       "1           24.0            1.0            0.0            0.0            0.0   \n",
       "2           24.0            1.0            0.0            0.0            0.0   \n",
       "3           24.0            1.0            0.0            0.0            0.0   \n",
       "4           24.0            1.0            0.0            0.0            0.0   \n",
       "5           24.0            1.0            0.0            0.0            0.0   \n",
       "6           24.0            1.0            0.0            0.0            0.0   \n",
       "7           24.0            1.0            0.0            0.0            0.0   \n",
       "8           24.0            1.0            0.0            0.0            0.0   \n",
       "9           24.0            1.0            0.0            0.0            0.0   \n",
       "10          24.0            1.0            0.0            0.0            0.0   \n",
       "11          24.0            1.0            0.0            0.0            0.0   \n",
       "12          25.0            1.0            0.0            0.0            0.0   \n",
       "13          25.0            1.0            0.0            0.0            0.0   \n",
       "14          25.0            1.0            0.0            0.0            0.0   \n",
       "15          25.0            1.0            0.0            0.0            0.0   \n",
       "16          20.0            1.0            0.0            0.0            0.0   \n",
       "17          20.0            1.0            0.0            0.0            0.0   \n",
       "18          20.0            1.0            0.0            0.0            0.0   \n",
       "19          20.0            1.0            0.0            0.0            0.0   \n",
       "20          20.0            1.0            0.0            0.0            0.0   \n",
       "21          20.0            1.0            0.0            0.0            0.0   \n",
       "22          20.0            1.0            0.0            0.0            0.0   \n",
       "23          20.0            1.0            0.0            0.0            0.0   \n",
       "24          20.0            1.0            0.0            0.0            0.0   \n",
       "25          20.0            1.0            0.0            0.0            0.0   \n",
       "26          20.0            1.0            0.0            0.0            0.0   \n",
       "27          20.0            1.0            0.0            0.0            0.0   \n",
       "28          20.0            1.0            0.0            0.0            0.0   \n",
       "29          20.0            1.0            0.0            0.0            0.0   \n",
       "30          20.0            1.0            0.0            0.0            0.0   \n",
       "31          20.0            1.0            0.0            0.0            0.0   \n",
       "\n",
       "    ...  smart_190_raw  smart_192_raw  smart_193_raw  smart_194_raw  \\\n",
       "0   ...           25.0           14.0        57729.0           25.0   \n",
       "1   ...           24.0           14.0        57731.0           24.0   \n",
       "2   ...           25.0           14.0        57731.0           25.0   \n",
       "3   ...           25.0           14.0        57731.0           25.0   \n",
       "4   ...           25.0           14.0        57731.0           25.0   \n",
       "5   ...           25.0           14.0        57731.0           25.0   \n",
       "6   ...           25.0           14.0        57731.0           25.0   \n",
       "7   ...           25.0           14.0        57731.0           25.0   \n",
       "8   ...           25.0           14.0        57731.0           25.0   \n",
       "9   ...           25.0           14.0        57731.0           25.0   \n",
       "10  ...           25.0           14.0        57731.0           25.0   \n",
       "11  ...           25.0           14.0        57732.0           25.0   \n",
       "12  ...           25.0           14.0        58409.0           25.0   \n",
       "13  ...           24.0           14.0        59661.0           24.0   \n",
       "14  ...           24.0           14.0        60884.0           24.0   \n",
       "15  ...           25.0           14.0        61772.0           25.0   \n",
       "16  ...           25.0            3.0        26818.0           25.0   \n",
       "17  ...           25.0            3.0        28084.0           25.0   \n",
       "18  ...           26.0            3.0        29240.0           26.0   \n",
       "19  ...           26.0            3.0        30410.0           26.0   \n",
       "20  ...           25.0            3.0        31656.0           25.0   \n",
       "21  ...           26.0            3.0        32938.0           26.0   \n",
       "22  ...           25.0            3.0        34195.0           25.0   \n",
       "23  ...           25.0            3.0        35548.0           25.0   \n",
       "24  ...           25.0            3.0        36779.0           25.0   \n",
       "25  ...           25.0            3.0        38030.0           25.0   \n",
       "26  ...           25.0            3.0        39338.0           25.0   \n",
       "27  ...           25.0            3.0        40607.0           25.0   \n",
       "28  ...           24.0            3.0        41828.0           24.0   \n",
       "29  ...           24.0            3.0        43101.0           24.0   \n",
       "30  ...           23.0            3.0        44376.0           23.0   \n",
       "31  ...           23.0            3.0        45637.0           23.0   \n",
       "\n",
       "    smart_197_raw  smart_198_raw  smart_199_raw  smart_240_raw  smart_241_raw  \\\n",
       "0             0.0            0.0            0.0        34450.0   3.177469e+10   \n",
       "1             0.0            0.0            0.0        34474.0   3.177673e+10   \n",
       "2             0.0            0.0            0.0        34498.0   3.177876e+10   \n",
       "3             0.0            0.0            0.0        34522.0   3.178076e+10   \n",
       "4             0.0            0.0            0.0        34546.0   3.178302e+10   \n",
       "5             0.0            0.0            0.0        34570.0   3.178512e+10   \n",
       "6             0.0            0.0            0.0        34594.0   3.178752e+10   \n",
       "7             0.0            0.0            0.0        34618.0   3.178998e+10   \n",
       "8             0.0            0.0            0.0        34642.0   3.179215e+10   \n",
       "9             0.0            0.0            0.0        34666.0   3.179427e+10   \n",
       "10            0.0            0.0            0.0        34690.0   3.179640e+10   \n",
       "11            0.0            0.0            0.0        34714.0   3.179845e+10   \n",
       "12            0.0            0.0            0.0        34737.0   3.179961e+10   \n",
       "13            0.0            0.0            0.0        34755.0   3.179972e+10   \n",
       "14            0.0            0.0            0.0        34774.0   3.179984e+10   \n",
       "15            0.0            0.0            0.0        34795.0   3.180088e+10   \n",
       "16            0.0            0.0            0.0        33350.0   3.084112e+10   \n",
       "17            0.0            0.0            0.0        33369.0   3.084124e+10   \n",
       "18            0.0            0.0            0.0        33389.0   3.084135e+10   \n",
       "19            0.0            0.0            0.0        33409.0   3.084147e+10   \n",
       "20            0.0            0.0            0.0        33429.0   3.084159e+10   \n",
       "21            0.0            0.0            0.0        33448.0   3.084171e+10   \n",
       "22            0.0            0.0            0.0        33467.0   3.084182e+10   \n",
       "23            0.0            0.0            0.0        33487.0   3.084194e+10   \n",
       "24            0.0            0.0            0.0        33506.0   3.084206e+10   \n",
       "25            0.0            0.0            0.0        33525.0   3.084218e+10   \n",
       "26            0.0            0.0            0.0        33545.0   3.084230e+10   \n",
       "27            0.0            0.0            0.0        33564.0   3.084241e+10   \n",
       "28            0.0            0.0            0.0        33584.0   3.084253e+10   \n",
       "29            0.0            0.0            0.0        33603.0   3.084265e+10   \n",
       "30            0.0            0.0            0.0        33623.0   3.084277e+10   \n",
       "31            0.0            0.0            0.0        33642.0   3.084288e+10   \n",
       "\n",
       "    smart_242_raw  \n",
       "0    1.425277e+11  \n",
       "1    1.425649e+11  \n",
       "2    1.426023e+11  \n",
       "3    1.426377e+11  \n",
       "4    1.426726e+11  \n",
       "5    1.427067e+11  \n",
       "6    1.427406e+11  \n",
       "7    1.427768e+11  \n",
       "8    1.428109e+11  \n",
       "9    1.428451e+11  \n",
       "10   1.428825e+11  \n",
       "11   1.429180e+11  \n",
       "12   1.429477e+11  \n",
       "13   1.429751e+11  \n",
       "14   1.430094e+11  \n",
       "15   1.430484e+11  \n",
       "16   1.919088e+11  \n",
       "17   1.919419e+11  \n",
       "18   1.919757e+11  \n",
       "19   1.920092e+11  \n",
       "20   1.920439e+11  \n",
       "21   1.920758e+11  \n",
       "22   1.921093e+11  \n",
       "23   1.921437e+11  \n",
       "24   1.921758e+11  \n",
       "25   1.922090e+11  \n",
       "26   1.922431e+11  \n",
       "27   1.922767e+11  \n",
       "28   1.923092e+11  \n",
       "29   1.923429e+11  \n",
       "30   1.923776e+11  \n",
       "31   1.924095e+11  \n",
       "\n",
       "[32 rows x 21 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(2*sequence_length)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we are going to load the test set. Unlike, the train set, we require the `failure` column for the test set to evaluate the model accuracy at the end. Again, you can see that we are using the processed dataset from the previous lab for our test purposes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sorting the data frame based on pre-created sequence id and sequence day id\n",
      "removing unnecessary columns\n",
      "Normalizing input for lstm\n",
      "(834, 16, 21)\n"
     ]
    }
   ],
   "source": [
    "df_test = pd.read_pickle(test_fname)\n",
    "y_labels = get_Labels(df_test)\n",
    "\n",
    "df_test = clean_dataframe(df_test, ['ae_seq_id', 'ae_seq_data_id', 'date','serial_number','model', \n",
    "                            'failure', \n",
    "                            'capacity_bytes'])\n",
    "\n",
    "x_test, _ = normalize_df(df_test,'lstm',scaler=scaler)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
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       "      <td>451076087.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>21.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
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       "      <th>7</th>\n",
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       "      <th>8</th>\n",
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       "      <td>451908456.0</td>\n",
       "      <td>10689.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>19300.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10581.0</td>\n",
       "      <td>3.633408e+10</td>\n",
       "      <td>8.916714e+09</td>\n",
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       "    <tr>\n",
       "      <th>9</th>\n",
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       "      <td>452575870.0</td>\n",
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       "      <td>5.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>23.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>19303.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10605.0</td>\n",
       "      <td>3.638426e+10</td>\n",
       "      <td>8.928378e+09</td>\n",
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       "    <tr>\n",
       "      <th>10</th>\n",
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       "      <td>453641142.0</td>\n",
       "      <td>10737.0</td>\n",
       "      <td>5.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>22.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>19303.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10629.0</td>\n",
       "      <td>3.641227e+10</td>\n",
       "      <td>8.952648e+09</td>\n",
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       "    <tr>\n",
       "      <th>11</th>\n",
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       "      <td>454176955.0</td>\n",
       "      <td>10761.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>23.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>19303.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10653.0</td>\n",
       "      <td>3.645253e+10</td>\n",
       "      <td>8.961123e+09</td>\n",
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       "    <tr>\n",
       "      <th>12</th>\n",
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       "      <td>455687902.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>24.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>19303.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10677.0</td>\n",
       "      <td>3.649212e+10</td>\n",
       "      <td>8.984780e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
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       "      <td>0.0</td>\n",
       "      <td>456866368.0</td>\n",
       "      <td>10809.0</td>\n",
       "      <td>5.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>24.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>19303.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10701.0</td>\n",
       "      <td>3.654030e+10</td>\n",
       "      <td>9.006662e+09</td>\n",
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       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>52369144.0</td>\n",
       "      <td>5.0</td>\n",
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       "      <td>457597063.0</td>\n",
       "      <td>10833.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>19303.0</td>\n",
       "      <td>25.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10725.0</td>\n",
       "      <td>3.660039e+10</td>\n",
       "      <td>9.017427e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
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       "      <td>...</td>\n",
       "      <td>24.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>19303.0</td>\n",
       "      <td>24.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10749.0</td>\n",
       "      <td>3.663817e+10</td>\n",
       "      <td>9.024461e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>170489672.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>544653734.0</td>\n",
       "      <td>13739.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>30424.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13582.0</td>\n",
       "      <td>3.857965e+10</td>\n",
       "      <td>2.475205e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>218930264.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>545302736.0</td>\n",
       "      <td>13763.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>24.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>30427.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13606.0</td>\n",
       "      <td>3.861341e+10</td>\n",
       "      <td>2.477526e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>121983304.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>546610816.0</td>\n",
       "      <td>13787.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>23.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>30427.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13630.0</td>\n",
       "      <td>3.864594e+10</td>\n",
       "      <td>2.481269e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>84711632.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>547290773.0</td>\n",
       "      <td>13811.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>22.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>30427.0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13654.0</td>\n",
       "      <td>3.867881e+10</td>\n",
       "      <td>2.483501e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>109898208.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>548939592.0</td>\n",
       "      <td>13834.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>30427.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13678.0</td>\n",
       "      <td>3.873947e+10</td>\n",
       "      <td>2.487543e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>104488776.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>551032632.0</td>\n",
       "      <td>13858.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>30427.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13702.0</td>\n",
       "      <td>3.879835e+10</td>\n",
       "      <td>2.491775e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>181122920.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>551724773.0</td>\n",
       "      <td>13883.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>30427.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13726.0</td>\n",
       "      <td>3.883539e+10</td>\n",
       "      <td>2.493543e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>36315568.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>552278534.0</td>\n",
       "      <td>13906.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>24.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>30427.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13750.0</td>\n",
       "      <td>3.885619e+10</td>\n",
       "      <td>2.495545e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>82759384.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>552843651.0</td>\n",
       "      <td>13930.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>24.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>30427.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13774.0</td>\n",
       "      <td>3.888197e+10</td>\n",
       "      <td>2.497250e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>151460032.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>554130031.0</td>\n",
       "      <td>13954.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>24.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>30427.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13797.0</td>\n",
       "      <td>3.891351e+10</td>\n",
       "      <td>2.500865e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>182983096.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>554784945.0</td>\n",
       "      <td>13979.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>24.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>30427.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13822.0</td>\n",
       "      <td>3.894760e+10</td>\n",
       "      <td>2.502570e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>112044808.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>556215819.0</td>\n",
       "      <td>14002.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>23.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>30427.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13846.0</td>\n",
       "      <td>3.897927e+10</td>\n",
       "      <td>2.506768e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>204594816.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>556815879.0</td>\n",
       "      <td>14026.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>24.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>30427.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13870.0</td>\n",
       "      <td>3.900667e+10</td>\n",
       "      <td>2.508617e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>31216360.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>557416355.0</td>\n",
       "      <td>14050.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>23.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>30427.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13893.0</td>\n",
       "      <td>3.903526e+10</td>\n",
       "      <td>2.510276e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>194976032.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>558093153.0</td>\n",
       "      <td>14075.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>24.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>30427.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13918.0</td>\n",
       "      <td>3.906552e+10</td>\n",
       "      <td>2.512448e+10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>75671016.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>558691985.0</td>\n",
       "      <td>14099.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>23.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>30427.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>13942.0</td>\n",
       "      <td>3.909157e+10</td>\n",
       "      <td>2.514321e+10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>32 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    smart_1_raw  smart_4_raw  smart_5_raw  smart_7_raw  smart_9_raw  \\\n",
       "0    20931656.0          5.0          0.0  445083804.0      10497.0   \n",
       "1   172359472.0          5.0          0.0  445791739.0      10521.0   \n",
       "2    21705248.0          5.0          0.0  447586660.0      10545.0   \n",
       "3   129069792.0          5.0          0.0  448820783.0      10568.0   \n",
       "4    68452888.0          5.0          0.0  449512275.0      10593.0   \n",
       "5    59189440.0          5.0          0.0  449985857.0      10617.0   \n",
       "6   117643832.0          5.0          0.0  451076087.0      10641.0   \n",
       "7   210523216.0          5.0          0.0  451546733.0      10665.0   \n",
       "8     2369744.0          5.0          0.0  451908456.0      10689.0   \n",
       "9   130718904.0          5.0          0.0  452575870.0      10713.0   \n",
       "10  118628456.0          5.0          0.0  453641142.0      10737.0   \n",
       "11  182296152.0          5.0          0.0  454176955.0      10761.0   \n",
       "12   47644936.0          5.0          0.0  455687902.0      10785.0   \n",
       "13  209559408.0          5.0          0.0  456866368.0      10809.0   \n",
       "14   52369144.0          5.0          0.0  457597063.0      10833.0   \n",
       "15  179911504.0          5.0          0.0  458086103.0      10857.0   \n",
       "16  170489672.0          1.0          0.0  544653734.0      13739.0   \n",
       "17  218930264.0          1.0          0.0  545302736.0      13763.0   \n",
       "18  121983304.0          1.0          0.0  546610816.0      13787.0   \n",
       "19   84711632.0          1.0          0.0  547290773.0      13811.0   \n",
       "20  109898208.0          1.0          0.0  548939592.0      13834.0   \n",
       "21  104488776.0          1.0          0.0  551032632.0      13858.0   \n",
       "22  181122920.0          1.0          0.0  551724773.0      13883.0   \n",
       "23   36315568.0          1.0          0.0  552278534.0      13906.0   \n",
       "24   82759384.0          1.0          0.0  552843651.0      13930.0   \n",
       "25  151460032.0          1.0          0.0  554130031.0      13954.0   \n",
       "26  182983096.0          1.0          0.0  554784945.0      13979.0   \n",
       "27  112044808.0          1.0          0.0  556215819.0      14002.0   \n",
       "28  204594816.0          1.0          0.0  556815879.0      14026.0   \n",
       "29   31216360.0          1.0          0.0  557416355.0      14050.0   \n",
       "30  194976032.0          1.0          0.0  558093153.0      14075.0   \n",
       "31   75671016.0          1.0          0.0  558691985.0      14099.0   \n",
       "\n",
       "    smart_12_raw  smart_183_raw  smart_184_raw  smart_187_raw  smart_188_raw  \\\n",
       "0            5.0            0.0            0.0            0.0            0.0   \n",
       "1            5.0            0.0            0.0            0.0            0.0   \n",
       "2            5.0            0.0            0.0            0.0            0.0   \n",
       "3            5.0            0.0            0.0            0.0            0.0   \n",
       "4            5.0            0.0            0.0            0.0            0.0   \n",
       "5            5.0            0.0            0.0            0.0            0.0   \n",
       "6            5.0            0.0            0.0            0.0            0.0   \n",
       "7            5.0            0.0            0.0            0.0            0.0   \n",
       "8            5.0            0.0            0.0            0.0            0.0   \n",
       "9            5.0            0.0            0.0            0.0            0.0   \n",
       "10           5.0            0.0            0.0            0.0            0.0   \n",
       "11           5.0            0.0            0.0            0.0            0.0   \n",
       "12           5.0            0.0            0.0            0.0            0.0   \n",
       "13           5.0            0.0            0.0            0.0            0.0   \n",
       "14           5.0            0.0            0.0            0.0            0.0   \n",
       "15           5.0            0.0            0.0            0.0            0.0   \n",
       "16           1.0            0.0            0.0            0.0            0.0   \n",
       "17           1.0            0.0            0.0            0.0            0.0   \n",
       "18           1.0            0.0            0.0            0.0            0.0   \n",
       "19           1.0            0.0            0.0            0.0            0.0   \n",
       "20           1.0            0.0            0.0            0.0            0.0   \n",
       "21           1.0            0.0            0.0            0.0            0.0   \n",
       "22           1.0            0.0            0.0            0.0            0.0   \n",
       "23           1.0            0.0            0.0            0.0            0.0   \n",
       "24           1.0            0.0            0.0            0.0            0.0   \n",
       "25           1.0            0.0            0.0            0.0            0.0   \n",
       "26           1.0            0.0            0.0            0.0            0.0   \n",
       "27           1.0            0.0            0.0            0.0            0.0   \n",
       "28           1.0            0.0            0.0            0.0            0.0   \n",
       "29           1.0            0.0            0.0            0.0            0.0   \n",
       "30           1.0            0.0            0.0            0.0            0.0   \n",
       "31           1.0            0.0            0.0            0.0            0.0   \n",
       "\n",
       "    ...  smart_190_raw  smart_192_raw  smart_193_raw  smart_194_raw  \\\n",
       "0   ...           19.0            0.0        19298.0           19.0   \n",
       "1   ...           19.0            0.0        19298.0           19.0   \n",
       "2   ...           19.0            0.0        19298.0           19.0   \n",
       "3   ...           20.0            0.0        19298.0           20.0   \n",
       "4   ...           20.0            0.0        19298.0           20.0   \n",
       "5   ...           20.0            0.0        19300.0           20.0   \n",
       "6   ...           21.0            0.0        19300.0           21.0   \n",
       "7   ...           21.0            0.0        19300.0           21.0   \n",
       "8   ...           26.0            0.0        19300.0           26.0   \n",
       "9   ...           23.0            0.0        19303.0           23.0   \n",
       "10  ...           22.0            0.0        19303.0           22.0   \n",
       "11  ...           23.0            0.0        19303.0           23.0   \n",
       "12  ...           24.0            0.0        19303.0           24.0   \n",
       "13  ...           24.0            0.0        19303.0           24.0   \n",
       "14  ...           25.0            0.0        19303.0           25.0   \n",
       "15  ...           24.0            0.0        19303.0           24.0   \n",
       "16  ...           28.0            0.0        30424.0           28.0   \n",
       "17  ...           24.0            0.0        30427.0           24.0   \n",
       "18  ...           23.0            0.0        30427.0           23.0   \n",
       "19  ...           22.0            0.0        30427.0           22.0   \n",
       "20  ...           25.0            0.0        30427.0           25.0   \n",
       "21  ...           25.0            0.0        30427.0           25.0   \n",
       "22  ...           25.0            0.0        30427.0           25.0   \n",
       "23  ...           24.0            0.0        30427.0           24.0   \n",
       "24  ...           24.0            0.0        30427.0           24.0   \n",
       "25  ...           24.0            0.0        30427.0           24.0   \n",
       "26  ...           24.0            0.0        30427.0           24.0   \n",
       "27  ...           23.0            0.0        30427.0           23.0   \n",
       "28  ...           24.0            0.0        30427.0           24.0   \n",
       "29  ...           23.0            0.0        30427.0           23.0   \n",
       "30  ...           24.0            0.0        30427.0           24.0   \n",
       "31  ...           23.0            0.0        30427.0           23.0   \n",
       "\n",
       "    smart_197_raw  smart_198_raw  smart_199_raw  smart_240_raw  smart_241_raw  \\\n",
       "0             0.0            0.0            0.0        10389.0   3.606082e+10   \n",
       "1             0.0            0.0            0.0        10413.0   3.609658e+10   \n",
       "2             0.0            0.0            0.0        10437.0   3.614011e+10   \n",
       "3             0.0            0.0            0.0        10461.0   3.617421e+10   \n",
       "4             0.0            0.0            0.0        10485.0   3.621012e+10   \n",
       "5             0.0            0.0            0.0        10509.0   3.624462e+10   \n",
       "6             0.0            0.0            0.0        10533.0   3.628199e+10   \n",
       "7             0.0            0.0            0.0        10557.0   3.631089e+10   \n",
       "8             0.0            0.0            0.0        10581.0   3.633408e+10   \n",
       "9             0.0            0.0            0.0        10605.0   3.638426e+10   \n",
       "10            0.0            0.0            0.0        10629.0   3.641227e+10   \n",
       "11            0.0            0.0            0.0        10653.0   3.645253e+10   \n",
       "12            0.0            0.0            0.0        10677.0   3.649212e+10   \n",
       "13            0.0            0.0            0.0        10701.0   3.654030e+10   \n",
       "14            0.0            0.0            0.0        10725.0   3.660039e+10   \n",
       "15            0.0            0.0            0.0        10749.0   3.663817e+10   \n",
       "16            0.0            0.0            0.0        13582.0   3.857965e+10   \n",
       "17            0.0            0.0            0.0        13606.0   3.861341e+10   \n",
       "18            0.0            0.0            0.0        13630.0   3.864594e+10   \n",
       "19            0.0            0.0            0.0        13654.0   3.867881e+10   \n",
       "20            0.0            0.0            0.0        13678.0   3.873947e+10   \n",
       "21            0.0            0.0            0.0        13702.0   3.879835e+10   \n",
       "22            0.0            0.0            0.0        13726.0   3.883539e+10   \n",
       "23            0.0            0.0            0.0        13750.0   3.885619e+10   \n",
       "24            0.0            0.0            0.0        13774.0   3.888197e+10   \n",
       "25            0.0            0.0            0.0        13797.0   3.891351e+10   \n",
       "26            0.0            0.0            0.0        13822.0   3.894760e+10   \n",
       "27            0.0            0.0            0.0        13846.0   3.897927e+10   \n",
       "28            0.0            0.0            0.0        13870.0   3.900667e+10   \n",
       "29            0.0            0.0            0.0        13893.0   3.903526e+10   \n",
       "30            0.0            0.0            0.0        13918.0   3.906552e+10   \n",
       "31            0.0            0.0            0.0        13942.0   3.909157e+10   \n",
       "\n",
       "    smart_242_raw  \n",
       "0    8.751856e+09  \n",
       "1    8.776299e+09  \n",
       "2    8.817362e+09  \n",
       "3    8.851069e+09  \n",
       "4    8.869208e+09  \n",
       "5    8.878416e+09  \n",
       "6    8.901264e+09  \n",
       "7    8.910142e+09  \n",
       "8    8.916714e+09  \n",
       "9    8.928378e+09  \n",
       "10   8.952648e+09  \n",
       "11   8.961123e+09  \n",
       "12   8.984780e+09  \n",
       "13   9.006662e+09  \n",
       "14   9.017427e+09  \n",
       "15   9.024461e+09  \n",
       "16   2.475205e+10  \n",
       "17   2.477526e+10  \n",
       "18   2.481269e+10  \n",
       "19   2.483501e+10  \n",
       "20   2.487543e+10  \n",
       "21   2.491775e+10  \n",
       "22   2.493543e+10  \n",
       "23   2.495545e+10  \n",
       "24   2.497250e+10  \n",
       "25   2.500865e+10  \n",
       "26   2.502570e+10  \n",
       "27   2.506768e+10  \n",
       "28   2.508617e+10  \n",
       "29   2.510276e+10  \n",
       "30   2.512448e+10  \n",
       "31   2.514321e+10  \n",
       "\n",
       "[32 rows x 21 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test.head(2*sequence_length)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a name=\"e2\"></a>\n",
    "### Exercise 2: Filtering the labels\n",
    "\n",
    "As for class labels, there is one extra filtering step. Take some time and try to figure the purpose behind the following command:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_labels_seq = y_labels[sequence_length-1::sequence_length]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To label a particular sequence as normal or failed, we will use the 'failure' column from the last day of the sequence.  The above code finds the last day failure label to label the entire sequence."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Part 1 - LSTM based autoencoder or seq2seq model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a name=\"2\"></a>\n",
    "## Defining the LSTM Autoencoder Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this section, we are going to define two autoencoder models. The first model uses LSTMs as autoencoders. In this model, we create the autoencoder for time sequences using LSTM encoder-decoder models. \n",
    "\n",
    "An LSTM unit could be thought of an encoder unit where the input data are represented as a vector of a hidden states. We use this concept to encode the input into 1024 __bottleneck__ vectors. Afterwards we use another set of two LSTM units to reconstruct the original series.\n",
    "\n",
    "For the second model, we use a convolutional autoencoder. In this model, in order to encode the information, we use down-sampling (MaxPooling1D) and up-sampling (UpSampling1D) methods.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "input_1 (InputLayer)         [(None, 16, 21)]          0         \n",
      "_________________________________________________________________\n",
      "lstm (LSTM)                  (None, 128)               76800     \n",
      "_________________________________________________________________\n",
      "repeat_vector (RepeatVector) (None, 16, 128)           0         \n",
      "_________________________________________________________________\n",
      "lstm_1 (LSTM)                (None, 16, 128)           131584    \n",
      "_________________________________________________________________\n",
      "lstm_2 (LSTM)                (None, 16, 21)            12600     \n",
      "=================================================================\n",
      "Total params: 220,984\n",
      "Trainable params: 220,984\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "# Model #1 - Wide LSTM-based Encoder-Decoder\n",
    "data_shape = x_train.shape[1:]   \n",
    "    \n",
    "## Some parameters to change\n",
    "dp_lvl = 0.2\n",
    "lstm_width = 128\n",
    "\n",
    "inputs = Input(shape=(data_shape))\n",
    "encoded = LSTM(lstm_width, dropout = dp_lvl, recurrent_dropout = dp_lvl, return_sequences =  False, activation='relu')(inputs)\n",
    "decoded = RepeatVector(data_shape[0])(encoded)\n",
    "decoded = LSTM(lstm_width, return_sequences=True, activation='relu')(decoded)\n",
    "decoded = LSTM(data_shape[1], activation='tanh', return_sequences=True)(decoded)\n",
    "\n",
    "# Build entire model\n",
    "autoencoder = Model(inputs, decoded)\n",
    "\n",
    "# Parameters to control learning rate\n",
    "learning_rate = 0.001\n",
    "\n",
    "optimizer = Adam(lr=learning_rate)\n",
    "autoencoder.compile(optimizer=optimizer, loss='mae')\n",
    "    \n",
    "print(autoencoder.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We setup callback routines to two callback routines\n",
    "<ul>\n",
    "    <li>ModelCheckpoint - stores a copy of our model weights for our best training epoch.  This can be used for later deployments.</li>\n",
    "    <li>EarlyStopping - stops the training early if the model is no longer improving.</li>\n",
    "</ul>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "from tensorflow.keras.callbacks import ModelCheckpoint, EarlyStopping\n",
    "\n",
    "# checkpoint\n",
    "checkpoint = ModelCheckpoint(data_dir+\"best_model_ae-lstm.h5\", monitor='val_loss', \n",
    "                             verbose=1, save_best_only=True, mode='min')\n",
    "es = EarlyStopping(patience=20, verbose=1)\n",
    "\n",
    "callbacks_list = [es, checkpoint]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a name=\"3\"></a>\n",
    "## Training the LSTM model\n",
    "Now that we've created our model, we'll go ahead and train it.  Then we'll evaluate the performance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 4162 samples, validate on 463 samples\n",
      "Epoch 1/60\n",
      "3968/4162 [===========================>..] - ETA: 0s - loss: 0.3818\n",
      "Epoch 00001: val_loss improved from inf to 0.30357, saving model to ./data/best_model_ae-lstm.h5\n",
      "4162/4162 [==============================] - 6s 1ms/sample - loss: 0.3796 - val_loss: 0.3036\n",
      "Epoch 2/60\n",
      "3968/4162 [===========================>..] - ETA: 0s - loss: 0.2991\n",
      "Epoch 00002: val_loss improved from 0.30357 to 0.27338, saving model to ./data/best_model_ae-lstm.h5\n",
      "4162/4162 [==============================] - 1s 199us/sample - loss: 0.2989 - val_loss: 0.2734\n",
      "Epoch 3/60\n",
      "3968/4162 [===========================>..] - ETA: 0s - loss: 0.2724\n",
      "Epoch 00003: val_loss improved from 0.27338 to 0.25284, saving model to ./data/best_model_ae-lstm.h5\n",
      "4162/4162 [==============================] - 1s 198us/sample - loss: 0.2726 - val_loss: 0.2528\n",
      "Epoch 4/60\n",
      "3968/4162 [===========================>..] - ETA: 0s - loss: 0.2585\n",
      "Epoch 00004: val_loss improved from 0.25284 to 0.23612, saving model to ./data/best_model_ae-lstm.h5\n",
      "4162/4162 [==============================] - 1s 199us/sample - loss: 0.2584 - val_loss: 0.2361\n",
      "Epoch 5/60\n",
      "3968/4162 [===========================>..] - ETA: 0s - loss: 0.2442\n",
      "Epoch 00005: val_loss improved from 0.23612 to 0.22533, saving model to ./data/best_model_ae-lstm.h5\n",
      "4162/4162 [==============================] - 1s 199us/sample - loss: 0.2441 - val_loss: 0.2253\n",
      "Epoch 6/60\n",
      "3968/4162 [===========================>..] - ETA: 0s - loss: 0.2363\n",
      "Epoch 00006: val_loss improved from 0.22533 to 0.21551, saving model to ./data/best_model_ae-lstm.h5\n",
      "4162/4162 [==============================] - 1s 198us/sample - loss: 0.2361 - val_loss: 0.2155\n",
      "Epoch 7/60\n",
      "3968/4162 [===========================>..] - ETA: 0s - loss: 0.2277\n",
      "Epoch 00007: val_loss improved from 0.21551 to 0.21200, saving model to ./data/best_model_ae-lstm.h5\n",
      "4162/4162 [==============================] - 1s 199us/sample - loss: 0.2276 - val_loss: 0.2120\n",
      "Epoch 8/60\n",
      "3968/4162 [===========================>..] - ETA: 0s - loss: 0.2258\n",
      "Epoch 00008: val_loss improved from 0.21200 to 0.20647, saving model to ./data/best_model_ae-lstm.h5\n",
      "4162/4162 [==============================] - 1s 197us/sample - loss: 0.2250 - val_loss: 0.2065\n",
      "Epoch 9/60\n",
      "3968/4162 [===========================>..] - ETA: 0s - loss: 0.2226\n",
      "Epoch 00009: val_loss improved from 0.20647 to 0.20258, saving model to ./data/best_model_ae-lstm.h5\n",
      "4162/4162 [==============================] - 1s 199us/sample - loss: 0.2217 - val_loss: 0.2026\n",
      "Epoch 10/60\n",
      "3968/4162 [===========================>..] - ETA: 0s - loss: 0.2178\n",
      "Epoch 00010: val_loss improved from 0.20258 to 0.19900, saving model to ./data/best_model_ae-lstm.h5\n",
      "4162/4162 [==============================] - 1s 197us/sample - loss: 0.2177 - val_loss: 0.1990\n",
      "Epoch 11/60\n",
      "3968/4162 [===========================>..] - ETA: 0s - loss: 0.2174\n",
      "Epoch 00011: val_loss improved from 0.19900 to 0.19634, saving model to ./data/best_model_ae-lstm.h5\n",
      "4162/4162 [==============================] - 1s 197us/sample - loss: 0.2161 - val_loss: 0.1963\n",
      "Epoch 12/60\n",
      "3968/4162 [===========================>..] - ETA: 0s - loss: 0.2113\n",
      "Epoch 00012: val_loss improved from 0.19634 to 0.19347, saving model to ./data/best_model_ae-lstm.h5\n",
      "4162/4162 [==============================] - 1s 198us/sample - loss: 0.2132 - val_loss: 0.1935\n",
      "Epoch 13/60\n",
      "3968/4162 [===========================>..] - ETA: 0s - loss: 0.2092\n",
      "Epoch 00013: val_loss improved from 0.19347 to 0.19269, saving model to ./data/best_model_ae-lstm.h5\n",
      "4162/4162 [==============================] - 1s 196us/sample - loss: 0.2095 - val_loss: 0.1927\n",
      "Epoch 14/60\n",
      "3968/4162 [===========================>..] - ETA: 0s - loss: 0.2088\n",
      "Epoch 00014: val_loss improved from 0.19269 to 0.19122, saving model to ./data/best_model_ae-lstm.h5\n",
      "4162/4162 [==============================] - 1s 196us/sample - loss: 0.2086 - val_loss: 0.1912\n",
      "Epoch 15/60\n",
      "3968/4162 [===========================>..] - ETA: 0s - loss: 0.2062\n",
      "Epoch 00015: val_loss improved from 0.19122 to 0.18864, saving model to ./data/best_model_ae-lstm.h5\n",
      "4162/4162 [==============================] - 1s 194us/sample - loss: 0.2064 - val_loss: 0.1886\n",
      "Epoch 16/60\n",
      "3968/4162 [===========================>..] - ETA: 0s - loss: 0.2061\n",
      "Epoch 00016: val_loss improved from 0.18864 to 0.18535, saving model to ./data/best_model_ae-lstm.h5\n",
      "4162/4162 [==============================] - 1s 193us/sample - loss: 0.2055 - val_loss: 0.1854\n",
      "Epoch 17/60\n",
      "3968/4162 [===========================>..] - ETA: 0s - loss: 0.2033\n",
      "Epoch 00017: val_loss did not improve from 0.18535\n",
      "4162/4162 [==============================] - 1s 173us/sample - loss: 0.2041 - val_loss: 0.1858\n",
      "Epoch 18/60\n",
      "3968/4162 [===========================>..] - ETA: 0s - loss: 0.2058\n",
      "Epoch 00018: val_loss did not improve from 0.18535\n",
      "4162/4162 [==============================] - 1s 174us/sample - loss: 0.2058 - val_loss: 0.1861\n",
      "Epoch 19/60\n",
      "3968/4162 [===========================>..] - ETA: 0s - loss: 0.2029\n",
      "Epoch 00019: val_loss improved from 0.18535 to 0.18308, saving model to ./data/best_model_ae-lstm.h5\n",
      "4162/4162 [==============================] - 1s 192us/sample - loss: 0.2030 - val_loss: 0.1831\n",
      "Epoch 20/60\n",
      "3968/4162 [===========================>..] - ETA: 0s - loss: 0.2014\n",
      "Epoch 00020: val_loss improved from 0.18308 to 0.18129, saving model to ./data/best_model_ae-lstm.h5\n",
      "4162/4162 [==============================] - 1s 195us/sample - loss: 0.2010 - val_loss: 0.1813\n",
      "Epoch 21/60\n",
      "3968/4162 [===========================>..] - ETA: 0s - loss: 0.1994\n",
      "Epoch 00021: val_loss improved from 0.18129 to 0.17950, saving model to ./data/best_model_ae-lstm.h5\n",
      "4162/4162 [==============================] - 1s 194us/sample - loss: 0.1990 - val_loss: 0.1795\n",
      "Epoch 22/60\n",
      "3968/4162 [===========================>..] - ETA: 0s - loss: 0.1970\n",
      "Epoch 00022: val_loss did not improve from 0.17950\n",
      "4162/4162 [==============================] - 1s 174us/sample - loss: 0.1985 - val_loss: 0.1805\n",
      "Epoch 23/60\n",
      "3968/4162 [===========================>..] - ETA: 0s - loss: 0.1965\n",
      "Epoch 00023: val_loss improved from 0.17950 to 0.17886, saving model to ./data/best_model_ae-lstm.h5\n",
      "4162/4162 [==============================] - 1s 194us/sample - loss: 0.1983 - val_loss: 0.1789\n",
      "Epoch 24/60\n",
      "3968/4162 [===========================>..] - ETA: 0s - loss: 0.1973\n",
      "Epoch 00024: val_loss improved from 0.17886 to 0.17883, saving model to ./data/best_model_ae-lstm.h5\n",
      "4162/4162 [==============================] - 1s 192us/sample - loss: 0.1977 - val_loss: 0.1788\n",
      "Epoch 25/60\n",
      "3968/4162 [===========================>..] - ETA: 0s - loss: 0.1987\n",
      "Epoch 00025: val_loss did not improve from 0.17883\n",
      "4162/4162 [==============================] - 1s 173us/sample - loss: 0.1982 - val_loss: 0.1789\n",
      "Epoch 26/60\n",
      "3968/4162 [===========================>..] - ETA: 0s - loss: 0.2042\n",
      "Epoch 00026: val_loss did not improve from 0.17883\n",
      "4162/4162 [==============================] - 1s 173us/sample - loss: 0.2049 - val_loss: 0.1927\n",
      "Epoch 27/60\n",
      "3968/4162 [===========================>..] - ETA: 0s - loss: 0.2187\n",
      "Epoch 00027: val_loss did not improve from 0.17883\n",
      "4162/4162 [==============================] - 1s 174us/sample - loss: 0.2193 - val_loss: 0.2200\n",
      "Epoch 28/60\n",
      "3968/4162 [===========================>..] - ETA: 0s - loss: 0.2662\n",
      "Epoch 00028: val_loss did not improve from 0.17883\n",
      "4162/4162 [==============================] - 1s 174us/sample - loss: 0.2671 - val_loss: 0.2632\n",
      "Epoch 29/60\n",
      "3968/4162 [===========================>..] - ETA: 0s - loss: 0.2752\n",
      "Epoch 00029: val_loss did not improve from 0.17883\n",
      "4162/4162 [==============================] - 1s 173us/sample - loss: 0.2748 - val_loss: 0.2554\n",
      "Epoch 30/60\n",
      "3968/4162 [===========================>..] - ETA: 0s - loss: 0.2603\n",
      "Epoch 00030: val_loss did not improve from 0.17883\n",
      "4162/4162 [==============================] - 1s 174us/sample - loss: 0.2603 - val_loss: 0.2391\n",
      "Epoch 31/60\n",
      "3968/4162 [===========================>..] - ETA: 0s - loss: 0.2460\n",
      "Epoch 00031: val_loss did not improve from 0.17883\n",
      "4162/4162 [==============================] - 1s 175us/sample - loss: 0.2465 - val_loss: 0.2228\n",
      "Epoch 32/60\n",
      "3968/4162 [===========================>..] - ETA: 0s - loss: 0.2299\n",
      "Epoch 00032: val_loss did not improve from 0.17883\n",
      "4162/4162 [==============================] - 1s 173us/sample - loss: 0.2315 - val_loss: 0.2047\n",
      "Epoch 33/60\n",
      "3968/4162 [===========================>..] - ETA: 0s - loss: 0.2185\n",
      "Epoch 00033: val_loss did not improve from 0.17883\n",
      "4162/4162 [==============================] - 1s 173us/sample - loss: 0.2207 - val_loss: 0.1957\n",
      "Epoch 34/60\n",
      "3968/4162 [===========================>..] - ETA: 0s - loss: 0.2158\n",
      "Epoch 00034: val_loss did not improve from 0.17883\n",
      "4162/4162 [==============================] - 1s 174us/sample - loss: 0.2149 - val_loss: 0.1922\n",
      "Epoch 35/60\n",
      "3968/4162 [===========================>..] - ETA: 0s - loss: 0.2109\n",
      "Epoch 00035: val_loss did not improve from 0.17883\n",
      "4162/4162 [==============================] - 1s 173us/sample - loss: 0.2111 - val_loss: 0.1846\n",
      "Epoch 36/60\n",
      "3968/4162 [===========================>..] - ETA: 0s - loss: 0.2084\n",
      "Epoch 00036: val_loss did not improve from 0.17883\n",
      "4162/4162 [==============================] - 1s 174us/sample - loss: 0.2082 - val_loss: 0.1834\n",
      "Epoch 37/60\n",
      "3968/4162 [===========================>..] - ETA: 0s - loss: 0.2079\n",
      "Epoch 00037: val_loss did not improve from 0.17883\n",
      "4162/4162 [==============================] - 1s 173us/sample - loss: 0.2070 - val_loss: 0.1818\n",
      "Epoch 38/60\n",
      "3968/4162 [===========================>..] - ETA: 0s - loss: 0.2056\n",
      "Epoch 00038: val_loss did not improve from 0.17883\n",
      "4162/4162 [==============================] - 1s 175us/sample - loss: 0.2050 - val_loss: 0.1804\n",
      "Epoch 39/60\n",
      "3968/4162 [===========================>..] - ETA: 0s - loss: 0.2041\n",
      "Epoch 00039: val_loss did not improve from 0.17883\n",
      "4162/4162 [==============================] - 1s 175us/sample - loss: 0.2043 - val_loss: 0.1792\n",
      "Epoch 40/60\n",
      "3968/4162 [===========================>..] - ETA: 0s - loss: 0.2036\n",
      "Epoch 00040: val_loss did not improve from 0.17883\n",
      "4162/4162 [==============================] - 1s 174us/sample - loss: 0.2035 - val_loss: 0.1789\n",
      "Epoch 41/60\n",
      "3968/4162 [===========================>..] - ETA: 0s - loss: 0.2029\n",
      "Epoch 00041: val_loss did not improve from 0.17883\n",
      "4162/4162 [==============================] - 1s 173us/sample - loss: 0.2034 - val_loss: 0.1808\n",
      "Epoch 42/60\n",
      "3968/4162 [===========================>..] - ETA: 0s - loss: 0.2044\n",
      "Epoch 00042: val_loss did not improve from 0.17883\n",
      "4162/4162 [==============================] - 1s 175us/sample - loss: 0.2046 - val_loss: 0.1825\n",
      "Epoch 43/60\n",
      "3968/4162 [===========================>..] - ETA: 0s - loss: 0.2071\n",
      "Epoch 00043: val_loss did not improve from 0.17883\n",
      "4162/4162 [==============================] - 1s 174us/sample - loss: 0.2073 - val_loss: 0.1828\n",
      "Epoch 44/60\n",
      "3968/4162 [===========================>..] - ETA: 0s - loss: 0.2048\n",
      "Epoch 00044: val_loss did not improve from 0.17883\n",
      "4162/4162 [==============================] - 1s 173us/sample - loss: 0.2056 - val_loss: 0.1811\n",
      "Epoch 00044: early stopping\n"
     ]
    }
   ],
   "source": [
    "# train the model\n",
    "\n",
    "# number of training epochs - feel free to change\n",
    "#\n",
    "# Time: 60 epochs ~= 3 minutes for LSTM\n",
    "\n",
    "epochs_num = 60\n",
    "history = autoencoder.fit(x_train, x_train, batch_size=128, epochs=epochs_num, validation_split=0.1, verbose=1, callbacks=callbacks_list) \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's look at the loss function to see how accurate our training was.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "./data/best_model_ae-lstm.h5\n"
     ]
    }
   ],
   "source": [
    "# load weights from best model\n",
    "print(data_dir+\"best_model_ae-lstm.h5\")\n",
    "autoencoder.load_weights(data_dir+\"best_model_ae-lstm.h5\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# show plot accuracy changes during training\n",
    "\n",
    "plt.plot(history.history['loss'],'g')\n",
    "plt.plot(history.history['val_loss'],'r')\n",
    "plt.title('LSTM Loss across epochs')\n",
    "plt.ylabel('Loss')\n",
    "plt.xlabel('# epoch')\n",
    "plt.legend(['train', 'test'], loc='lower right')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The model is starting to get trained, but at this point, the accuracy may not be that great.  In the real world, we train on even larger data sets for longer periods of time until the accurracy met our business requirements.  We also review the model and can increase it's complexity."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's go ahead and use our model to make predictions on the test data.  Remember, this is a reconstruction model, so the predictions, ideally, should look similar to the original data for normal disks."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Use model to predict on test data set\n",
    "y_pred = autoencoder.predict(x_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The next cell will plot a comparison of the original data to the predicted data for a random record. It compares the features for the first day in the sequence. Run it a few times to see how well your model came out."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "This record is from an anomalous disk.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# create horizontal positions for scatter plot\n",
    "x_pos = np.arange(x_test.shape[2])\n",
    "\n",
    "# select random record\n",
    "rec_num = random.randint(0, y_pred.shape[0])\n",
    "\n",
    "# plot predicted points\n",
    "plt.scatter(x_pos, x_test[rec_num,0,:], c=\"#ff0000\")\n",
    "plt.scatter(x_pos, y_pred[rec_num,0,:], c=\"#0000ff\")\n",
    "\n",
    "# label graph\n",
    "plt.xlabel(\"Feature\")\n",
    "plt.ylabel(\"Value\")\n",
    "plt.xticks(range(x_test.shape[2]))\n",
    "plt.title(\"Compare original to reconstruction for day 0 of seq num \" + str(rec_num))\n",
    "\n",
    "if y_labels_seq[rec_num] == 0:\n",
    "    print(\"This record is from a normal disk.\")\n",
    "else:\n",
    "    print(\"This record is from an anomalous disk.\")    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You'll see that once you have a trained model, you need to determine a \"threshold\" of difference in detecting an anomaly. In our case, we know the number of normal disks and the number of anomalous disks. We'll calculate the MSE for each reconstruction and set our initial threshold based on that. This should be a good point to start the threshold at. In real world usage, you'll adjust the model and threshold to obtain accuracy to meet your business needs. In our case we'd like the recall to be as high as possible to identify as many failing disks as possible."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Suggested Threshold: 0.15566227473163186\n"
     ]
    }
   ],
   "source": [
    "# Calcuate MSE for all sequences and predictions\n",
    "import statistics\n",
    "\n",
    "# determine median mse to split data into normal vs anomaly\n",
    "pred_mse = []  # calculated mse for test data\n",
    "pred_res = []  # result: 0 normal, 1 anomaly\n",
    "\n",
    "for i in range(len(x_test)):\n",
    "    pred_mse.append(mean_squared_error(y_pred[i,:,:], x_test[i,:,:]))\n",
    "\n",
    "# Find number of disks that we know are normal and use that value as splitting point for data\n",
    "num_normal_disks = list(y_labels_seq).count(0)\n",
    "\n",
    "sort_mse = pred_mse.copy()\n",
    "sort_mse.sort()\n",
    "suggested_threshold = sort_mse[num_normal_disks]\n",
    "print(\"Suggested Threshold:\", suggested_threshold)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Used threshold: 0.15566227473163186\n",
      "Resulting in the following classification of predicted data:\n",
      "- Normal Disks:    601\n",
      "- Anomalous Disks: 233\n"
     ]
    }
   ],
   "source": [
    "# Routine to specify whether or not a sequence is anomalous\n",
    "\n",
    "# specify override threshold or leave 0 to use suggested threshold\n",
    "override_threshold = 0\n",
    "\n",
    "if override_threshold == 0:\n",
    "    threshold = suggested_threshold\n",
    "else:\n",
    "    threshold = override_threshold\n",
    "\n",
    "for mse in pred_mse:\n",
    "    if (mse <= threshold):\n",
    "        pred_res.append(0)\n",
    "    else:\n",
    "        pred_res.append(1)\n",
    "        \n",
    "print(\"Used threshold:\", threshold)\n",
    "print(\"Resulting in the following classification of predicted data:\")\n",
    "print(\"- Normal Disks:   \", pred_res.count(0))\n",
    "print(\"- Anomalous Disks:\", pred_res.count(1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a name=\"3b\"></a>\n",
    "## Training the 1D Convolutional model\n",
    "Now that we've tested our LSTM model, let's create and train a 1D-Convolutional model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "checkpoint2 = ModelCheckpoint(data_dir+\"best_model_ae-conv1d.h5\", monitor='val_loss', verbose=1, save_best_only=True, mode='min')\n",
    "es2 = EarlyStopping(patience=20, verbose=1)\n",
    "\n",
    "callbacks_list2 = [es2, checkpoint2]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's build out a 1D Convolution Model:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model_1\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "input_2 (InputLayer)         [(None, 16, 21)]          0         \n",
      "_________________________________________________________________\n",
      "conv1d (Conv1D)              (None, 16, 160)           20320     \n",
      "_________________________________________________________________\n",
      "max_pooling1d (MaxPooling1D) (None, 8, 160)            0         \n",
      "_________________________________________________________________\n",
      "dropout (Dropout)            (None, 8, 160)            0         \n",
      "_________________________________________________________________\n",
      "conv1d_1 (Conv1D)            (None, 8, 80)             76880     \n",
      "_________________________________________________________________\n",
      "max_pooling1d_1 (MaxPooling1 (None, 4, 80)             0         \n",
      "_________________________________________________________________\n",
      "dropout_1 (Dropout)          (None, 4, 80)             0         \n",
      "_________________________________________________________________\n",
      "conv1d_2 (Conv1D)            (None, 4, 4)              1924      \n",
      "_________________________________________________________________\n",
      "dropout_2 (Dropout)          (None, 4, 4)              0         \n",
      "_________________________________________________________________\n",
      "up_sampling1d (UpSampling1D) (None, 8, 4)              0         \n",
      "_________________________________________________________________\n",
      "conv1d_3 (Conv1D)            (None, 8, 80)             2000      \n",
      "_________________________________________________________________\n",
      "dropout_3 (Dropout)          (None, 8, 80)             0         \n",
      "_________________________________________________________________\n",
      "up_sampling1d_1 (UpSampling1 (None, 16, 80)            0         \n",
      "_________________________________________________________________\n",
      "conv1d_4 (Conv1D)            (None, 16, 160)           76960     \n",
      "_________________________________________________________________\n",
      "dropout_4 (Dropout)          (None, 16, 160)           0         \n",
      "_________________________________________________________________\n",
      "conv1d_5 (Conv1D)            (None, 16, 21)            3381      \n",
      "=================================================================\n",
      "Total params: 181,465\n",
      "Trainable params: 181,465\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "# Traditional Conv1D model\n",
    "\n",
    "# Model parameter settings\n",
    "sample_size = x_train.shape[1]\n",
    "num_signals = x_train.shape[2]\n",
    "dropout_rate = 0.2\n",
    "outside_filters = 160\n",
    "inside_filters = 80\n",
    "ksize=6\n",
    "\n",
    "# TTraditional 1D-Convolution Autoencoder\n",
    "\n",
    "input_data = Input(shape=(sample_size, num_signals))\n",
    "# --- Encode ---\n",
    "encoded = Conv1D(filters=outside_filters, kernel_size=ksize, activation=\"relu\", padding=\"same\")(input_data)\n",
    "encoded = MaxPooling1D(pool_size=2)(encoded)\n",
    "encoded = Dropout(rate=dropout_rate)(encoded)\n",
    "encoded = Conv1D(filters=inside_filters, kernel_size=ksize, activation=\"relu\", padding=\"same\")(encoded)\n",
    "encoded = MaxPooling1D(pool_size=2)(encoded)\n",
    "encoded = Dropout(rate=dropout_rate)(encoded)\n",
    "# --- Encodings ---\n",
    "encoded = Conv1D(filters=4, kernel_size=ksize, activation=\"relu\", padding=\"same\")(encoded)\n",
    "encoded = Dropout(rate=dropout_rate)(encoded)\n",
    "# --- Decode ---\n",
    "decoded = UpSampling1D(size=2)(encoded)\n",
    "decoded = Conv1D(filters=inside_filters, kernel_size=ksize, activation=\"relu\", padding=\"same\")(decoded)\n",
    "decoded = Dropout(rate=dropout_rate)(decoded)\n",
    "decoded = UpSampling1D(size=2)(decoded)\n",
    "decoded = Conv1D(filters=outside_filters, kernel_size=ksize, activation=\"relu\", padding=\"same\")(decoded)\n",
    "decoded = Dropout(rate=dropout_rate)(decoded)\n",
    "# --- Output ---\n",
    "output_layer = Conv1D(filters=num_signals, kernel_size=1, activation=\"linear\", padding=\"same\")(decoded)\n",
    "autoencoder2 = Model(input_data, output_layer)\n",
    "lr = 1e-3 # learning rate\n",
    "autoencoder2.compile(optimizer = \"adam\", loss = \"mse\", metrics = [\"accuracy\"])\n",
    "autoencoder2.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train on 4162 samples, validate on 463 samples\n",
      "Epoch 1/100\n",
      "3328/4162 [======================>.......] - ETA: 0s - loss: 0.9072 - accuracy: 0.2404\n",
      "Epoch 00001: val_loss improved from inf to 0.50471, saving model to ./data/best_model_ae-conv1d.h5\n",
      "4162/4162 [==============================] - 1s 335us/sample - loss: 0.8872 - accuracy: 0.2447 - val_loss: 0.5047 - val_accuracy: 0.2701\n",
      "Epoch 2/100\n",
      "3456/4162 [=======================>......] - ETA: 0s - loss: 0.6621 - accuracy: 0.2668\n",
      "Epoch 00002: val_loss improved from 0.50471 to 0.43963, saving model to ./data/best_model_ae-conv1d.h5\n",
      "4162/4162 [==============================] - 0s 70us/sample - loss: 0.7884 - accuracy: 0.2691 - val_loss: 0.4396 - val_accuracy: 0.2430\n",
      "Epoch 3/100\n",
      "3456/4162 [=======================>......] - ETA: 0s - loss: 0.7593 - accuracy: 0.2860\n",
      "Epoch 00003: val_loss improved from 0.43963 to 0.41485, saving model to ./data/best_model_ae-conv1d.h5\n",
      "4162/4162 [==============================] - 0s 58us/sample - loss: 0.7395 - accuracy: 0.2866 - val_loss: 0.4149 - val_accuracy: 0.3078\n",
      "Epoch 4/100\n",
      "3456/4162 [=======================>......] - ETA: 0s - loss: 0.7108 - accuracy: 0.2924\n",
      "Epoch 00004: val_loss improved from 0.41485 to 0.37541, saving model to ./data/best_model_ae-conv1d.h5\n",
      "4162/4162 [==============================] - 0s 59us/sample - loss: 0.6992 - accuracy: 0.2925 - val_loss: 0.3754 - val_accuracy: 0.3152\n",
      "Epoch 5/100\n",
      "3456/4162 [=======================>......] - ETA: 0s - loss: 0.7015 - accuracy: 0.2917\n",
      "Epoch 00005: val_loss did not improve from 0.37541\n",
      "4162/4162 [==============================] - 0s 35us/sample - loss: 0.6889 - accuracy: 0.2842 - val_loss: 0.3836 - val_accuracy: 0.2858\n",
      "Epoch 6/100\n",
      "3456/4162 [=======================>......] - ETA: 0s - loss: 0.6711 - accuracy: 0.2501\n",
      "Epoch 00006: val_loss improved from 0.37541 to 0.34818, saving model to ./data/best_model_ae-conv1d.h5\n",
      "4162/4162 [==============================] - 0s 58us/sample - loss: 0.6391 - accuracy: 0.2460 - val_loss: 0.3482 - val_accuracy: 0.2535\n",
      "Epoch 7/100\n",
      "3456/4162 [=======================>......] - ETA: 0s - loss: 0.5572 - accuracy: 0.2257\n",
      "Epoch 00007: val_loss improved from 0.34818 to 0.31276, saving model to ./data/best_model_ae-conv1d.h5\n",
      "4162/4162 [==============================] - 0s 58us/sample - loss: 0.6200 - accuracy: 0.2302 - val_loss: 0.3128 - val_accuracy: 0.2785\n",
      "Epoch 8/100\n",
      "3456/4162 [=======================>......] - ETA: 0s - loss: 0.6252 - accuracy: 0.2910\n",
      "Epoch 00008: val_loss improved from 0.31276 to 0.29877, saving model to ./data/best_model_ae-conv1d.h5\n",
      "4162/4162 [==============================] - 0s 60us/sample - loss: 0.5932 - accuracy: 0.2946 - val_loss: 0.2988 - val_accuracy: 0.3278\n",
      "Epoch 9/100\n",
      "3456/4162 [=======================>......] - ETA: 0s - loss: 0.5989 - accuracy: 0.3640\n",
      "Epoch 00009: val_loss improved from 0.29877 to 0.29237, saving model to ./data/best_model_ae-conv1d.h5\n",
      "4162/4162 [==============================] - 0s 58us/sample - loss: 0.5756 - accuracy: 0.3677 - val_loss: 0.2924 - val_accuracy: 0.3785\n",
      "Epoch 10/100\n",
      "3584/4162 [========================>.....] - ETA: 0s - loss: 0.4748 - accuracy: 0.4028\n",
      "Epoch 00010: val_loss improved from 0.29237 to 0.29220, saving model to ./data/best_model_ae-conv1d.h5\n",
      "4162/4162 [==============================] - 0s 59us/sample - loss: 0.5299 - accuracy: 0.4064 - val_loss: 0.2922 - val_accuracy: 0.3747\n",
      "Epoch 11/100\n",
      "3456/4162 [=======================>......] - ETA: 0s - loss: 0.5096 - accuracy: 0.4349\n",
      "Epoch 00011: val_loss improved from 0.29220 to 0.28479, saving model to ./data/best_model_ae-conv1d.h5\n",
      "4162/4162 [==============================] - 0s 58us/sample - loss: 0.5232 - accuracy: 0.4372 - val_loss: 0.2848 - val_accuracy: 0.4569\n",
      "Epoch 12/100\n",
      "3456/4162 [=======================>......] - ETA: 0s - loss: 0.4307 - accuracy: 0.4252\n",
      "Epoch 00012: val_loss improved from 0.28479 to 0.26213, saving model to ./data/best_model_ae-conv1d.h5\n",
      "4162/4162 [==============================] - 0s 58us/sample - loss: 0.5232 - accuracy: 0.4323 - val_loss: 0.2621 - val_accuracy: 0.3952\n",
      "Epoch 13/100\n",
      "3456/4162 [=======================>......] - ETA: 0s - loss: 0.5703 - accuracy: 0.4497\n",
      "Epoch 00013: val_loss did not improve from 0.26213\n",
      "4162/4162 [==============================] - 0s 35us/sample - loss: 0.5475 - accuracy: 0.4486 - val_loss: 0.2641 - val_accuracy: 0.3962\n",
      "Epoch 14/100\n",
      "3456/4162 [=======================>......] - ETA: 0s - loss: 0.3735 - accuracy: 0.46 - ETA: 0s - loss: 0.4598 - accuracy: 0.4622\n",
      "Epoch 00014: val_loss did not improve from 0.26213\n",
      "4162/4162 [==============================] - 0s 36us/sample - loss: 0.5163 - accuracy: 0.4601 - val_loss: 0.2764 - val_accuracy: 0.3865\n",
      "Epoch 15/100\n",
      "3456/4162 [=======================>......] - ETA: 0s - loss: 0.5359 - accuracy: 0.4652\n",
      "Epoch 00015: val_loss improved from 0.26213 to 0.26106, saving model to ./data/best_model_ae-conv1d.h5\n",
      "4162/4162 [==============================] - 0s 59us/sample - loss: 0.5220 - accuracy: 0.4644 - val_loss: 0.2611 - val_accuracy: 0.4258\n",
      "Epoch 16/100\n",
      "3456/4162 [=======================>......] - ETA: 0s - loss: 0.4967 - accuracy: 0.4616\n",
      "Epoch 00016: val_loss improved from 0.26106 to 0.24333, saving model to ./data/best_model_ae-conv1d.h5\n",
      "4162/4162 [==============================] - 0s 58us/sample - loss: 0.4650 - accuracy: 0.4586 - val_loss: 0.2433 - val_accuracy: 0.3931\n",
      "Epoch 17/100\n",
      "3456/4162 [=======================>......] - ETA: 0s - loss: 0.4879 - accuracy: 0.4642\n",
      "Epoch 00017: val_loss did not improve from 0.24333\n",
      "4162/4162 [==============================] - 0s 35us/sample - loss: 0.4865 - accuracy: 0.4638 - val_loss: 0.2509 - val_accuracy: 0.4137\n",
      "Epoch 18/100\n",
      "3584/4162 [========================>.....] - ETA: 0s - loss: 0.3936 - accuracy: 0.4731\n",
      "Epoch 00018: val_loss improved from 0.24333 to 0.22929, saving model to ./data/best_model_ae-conv1d.h5\n",
      "4162/4162 [==============================] - 0s 57us/sample - loss: 0.4558 - accuracy: 0.4722 - val_loss: 0.2293 - val_accuracy: 0.4590\n",
      "Epoch 19/100\n",
      "3456/4162 [=======================>......] - ETA: 0s - loss: 0.4359 - accuracy: 0.4732\n",
      "Epoch 00019: val_loss improved from 0.22929 to 0.22803, saving model to ./data/best_model_ae-conv1d.h5\n",
      "4162/4162 [==============================] - 0s 58us/sample - loss: 0.4426 - accuracy: 0.4731 - val_loss: 0.2280 - val_accuracy: 0.4266\n",
      "Epoch 20/100\n",
      "3456/4162 [=======================>......] - ETA: 0s - loss: 0.4505 - accuracy: 0.4890\n",
      "Epoch 00020: val_loss improved from 0.22803 to 0.21854, saving model to ./data/best_model_ae-conv1d.h5\n",
      "4162/4162 [==============================] - 0s 59us/sample - loss: 0.4156 - accuracy: 0.4881 - val_loss: 0.2185 - val_accuracy: 0.4715\n",
      "Epoch 21/100\n",
      "3456/4162 [=======================>......] - ETA: 0s - loss: 0.4483 - accuracy: 0.4933\n",
      "Epoch 00021: val_loss did not improve from 0.21854\n",
      "4162/4162 [==============================] - 0s 36us/sample - loss: 0.4343 - accuracy: 0.4930 - val_loss: 0.2396 - val_accuracy: 0.4420\n",
      "Epoch 22/100\n",
      "3456/4162 [=======================>......] - ETA: 0s - loss: 0.5584 - accuracy: 0.4897\n",
      "Epoch 00022: val_loss improved from 0.21854 to 0.21356, saving model to ./data/best_model_ae-conv1d.h5\n",
      "4162/4162 [==============================] - 0s 60us/sample - loss: 0.5208 - accuracy: 0.4876 - val_loss: 0.2136 - val_accuracy: 0.4980\n",
      "Epoch 23/100\n",
      "3456/4162 [=======================>......] - ETA: 0s - loss: 0.5024 - accuracy: 0.4896\n",
      "Epoch 00023: val_loss improved from 0.21356 to 0.21217, saving model to ./data/best_model_ae-conv1d.h5\n",
      "4162/4162 [==============================] - 0s 59us/sample - loss: 0.4726 - accuracy: 0.4890 - val_loss: 0.2122 - val_accuracy: 0.4341\n",
      "Epoch 24/100\n",
      "3456/4162 [=======================>......] - ETA: 0s - loss: 0.3306 - accuracy: 0.5003\n",
      "Epoch 00024: val_loss improved from 0.21217 to 0.20432, saving model to ./data/best_model_ae-conv1d.h5\n",
      "4162/4162 [==============================] - 0s 59us/sample - loss: 0.4528 - accuracy: 0.5016 - val_loss: 0.2043 - val_accuracy: 0.4906\n",
      "Epoch 25/100\n",
      "3584/4162 [========================>.....] - ETA: 0s - loss: 0.5146 - accuracy: 0.4761\n",
      "Epoch 00025: val_loss did not improve from 0.20432\n",
      "4162/4162 [==============================] - 0s 35us/sample - loss: 0.4810 - accuracy: 0.4753 - val_loss: 0.2268 - val_accuracy: 0.4843\n",
      "Epoch 26/100\n",
      "3456/4162 [=======================>......] - ETA: 0s - loss: 0.5065 - accuracy: 0.4725\n",
      "Epoch 00026: val_loss did not improve from 0.20432\n",
      "4162/4162 [==============================] - 0s 36us/sample - loss: 0.4699 - accuracy: 0.4705 - val_loss: 0.2045 - val_accuracy: 0.4241\n",
      "Epoch 27/100\n",
      "3456/4162 [=======================>......] - ETA: 0s - loss: 0.4660 - accuracy: 0.4979\n",
      "Epoch 00027: val_loss improved from 0.20432 to 0.19155, saving model to ./data/best_model_ae-conv1d.h5\n",
      "4162/4162 [==============================] - 0s 58us/sample - loss: 0.4489 - accuracy: 0.5011 - val_loss: 0.1916 - val_accuracy: 0.4897\n",
      "Epoch 28/100\n",
      "3456/4162 [=======================>......] - ETA: 0s - loss: 0.4585 - accuracy: 0.5102\n",
      "Epoch 00028: val_loss improved from 0.19155 to 0.18925, saving model to ./data/best_model_ae-conv1d.h5\n",
      "4162/4162 [==============================] - 0s 58us/sample - loss: 0.4530 - accuracy: 0.5092 - val_loss: 0.1892 - val_accuracy: 0.5386\n",
      "Epoch 29/100\n",
      "3456/4162 [=======================>......] - ETA: 0s - loss: 0.4400 - accuracy: 0.5085\n",
      "Epoch 00029: val_loss did not improve from 0.18925\n",
      "4162/4162 [==============================] - 0s 35us/sample - loss: 0.4121 - accuracy: 0.5052 - val_loss: 0.1965 - val_accuracy: 0.5175\n",
      "Epoch 30/100\n",
      "3456/4162 [=======================>......] - ETA: 0s - loss: 0.3708 - accuracy: 0.5356\n",
      "Epoch 00030: val_loss did not improve from 0.18925\n",
      "4162/4162 [==============================] - 0s 35us/sample - loss: 0.3859 - accuracy: 0.5338 - val_loss: 0.1979 - val_accuracy: 0.5432\n",
      "Epoch 31/100\n",
      "3456/4162 [=======================>......] - ETA: 0s - loss: 0.4754 - accuracy: 0.5087\n",
      "Epoch 00031: val_loss did not improve from 0.18925\n",
      "4162/4162 [==============================] - 0s 35us/sample - loss: 0.4388 - accuracy: 0.5101 - val_loss: 0.2011 - val_accuracy: 0.5126\n",
      "Epoch 32/100\n",
      "3584/4162 [========================>.....] - ETA: 0s - loss: 0.3822 - accuracy: 0.5308\n",
      "Epoch 00032: val_loss improved from 0.18925 to 0.17712, saving model to ./data/best_model_ae-conv1d.h5\n",
      "4162/4162 [==============================] - 0s 61us/sample - loss: 0.3852 - accuracy: 0.5333 - val_loss: 0.1771 - val_accuracy: 0.6010\n",
      "Epoch 33/100\n",
      "3456/4162 [=======================>......] - ETA: 0s - loss: 0.4147 - accuracy: 0.5279\n",
      "Epoch 00033: val_loss did not improve from 0.17712\n",
      "4162/4162 [==============================] - 0s 36us/sample - loss: 0.4433 - accuracy: 0.5290 - val_loss: 0.2027 - val_accuracy: 0.5749\n",
      "Epoch 34/100\n",
      "3712/4162 [=========================>....] - ETA: 0s - loss: 0.4212 - accuracy: 0.5140\n",
      "Epoch 00034: val_loss did not improve from 0.17712\n",
      "4162/4162 [==============================] - 0s 35us/sample - loss: 0.4048 - accuracy: 0.5150 - val_loss: 0.2027 - val_accuracy: 0.5329\n",
      "Epoch 35/100\n",
      "3456/4162 [=======================>......] - ETA: 0s - loss: 0.3657 - accuracy: 0.5310\n",
      "Epoch 00035: val_loss did not improve from 0.17712\n",
      "4162/4162 [==============================] - 0s 36us/sample - loss: 0.3866 - accuracy: 0.5360 - val_loss: 0.1922 - val_accuracy: 0.6118\n",
      "Epoch 36/100\n",
      "3456/4162 [=======================>......] - ETA: 0s - loss: 0.4125 - accuracy: 0.5380\n",
      "Epoch 00036: val_loss did not improve from 0.17712\n",
      "4162/4162 [==============================] - 0s 35us/sample - loss: 0.4015 - accuracy: 0.5352 - val_loss: 0.1836 - val_accuracy: 0.4776\n",
      "Epoch 37/100\n",
      "3456/4162 [=======================>......] - ETA: 0s - loss: 0.4363 - accuracy: 0.5189\n",
      "Epoch 00037: val_loss did not improve from 0.17712\n",
      "4162/4162 [==============================] - 0s 35us/sample - loss: 0.4094 - accuracy: 0.5195 - val_loss: 0.1878 - val_accuracy: 0.4966\n",
      "Epoch 38/100\n",
      "3456/4162 [=======================>......] - ETA: 0s - loss: 0.3617 - accuracy: 0.5349\n",
      "Epoch 00038: val_loss improved from 0.17712 to 0.17592, saving model to ./data/best_model_ae-conv1d.h5\n",
      "4162/4162 [==============================] - 0s 58us/sample - loss: 0.3976 - accuracy: 0.5330 - val_loss: 0.1759 - val_accuracy: 0.5477\n",
      "Epoch 39/100\n",
      "2688/4162 [==================>...........] - ETA: 0s - loss: 0.4416 - accuracy: 0.5367\n",
      "Epoch 00039: val_loss did not improve from 0.17592\n",
      "4162/4162 [==============================] - 1s 294us/sample - loss: 0.4052 - accuracy: 0.5433 - val_loss: 0.1866 - val_accuracy: 0.5544\n",
      "Epoch 40/100\n",
      "3456/4162 [=======================>......] - ETA: 0s - loss: 0.3795 - accuracy: 0.5415\n",
      "Epoch 00040: val_loss did not improve from 0.17592\n",
      "4162/4162 [==============================] - 0s 36us/sample - loss: 0.3648 - accuracy: 0.5454 - val_loss: 0.1812 - val_accuracy: 0.5506\n",
      "Epoch 41/100\n",
      "3456/4162 [=======================>......] - ETA: 0s - loss: 0.3974 - accuracy: 0.5285\n",
      "Epoch 00041: val_loss did not improve from 0.17592\n",
      "4162/4162 [==============================] - 0s 36us/sample - loss: 0.3947 - accuracy: 0.5303 - val_loss: 0.2059 - val_accuracy: 0.4872\n",
      "Epoch 42/100\n",
      "3456/4162 [=======================>......] - ETA: 0s - loss: 0.4802 - accuracy: 0.5249\n",
      "Epoch 00042: val_loss did not improve from 0.17592\n",
      "4162/4162 [==============================] - 0s 35us/sample - loss: 0.4444 - accuracy: 0.5302 - val_loss: 0.2294 - val_accuracy: 0.4965\n",
      "Epoch 43/100\n",
      "3456/4162 [=======================>......] - ETA: 0s - loss: 0.4610 - accuracy: 0.5241\n",
      "Epoch 00043: val_loss did not improve from 0.17592\n",
      "4162/4162 [==============================] - 0s 36us/sample - loss: 0.4311 - accuracy: 0.5231 - val_loss: 0.1939 - val_accuracy: 0.5601\n",
      "Epoch 44/100\n",
      "3712/4162 [=========================>....] - ETA: 0s - loss: 0.4045 - accuracy: 0.5552\n",
      "Epoch 00044: val_loss improved from 0.17592 to 0.17319, saving model to ./data/best_model_ae-conv1d.h5\n",
      "4162/4162 [==============================] - 0s 57us/sample - loss: 0.3882 - accuracy: 0.5533 - val_loss: 0.1732 - val_accuracy: 0.5705\n",
      "Epoch 45/100\n",
      "3712/4162 [=========================>....] - ETA: 0s - loss: 0.3606 - accuracy: 0.5593\n",
      "Epoch 00045: val_loss did not improve from 0.17319\n",
      "4162/4162 [==============================] - 0s 35us/sample - loss: 0.3746 - accuracy: 0.5550 - val_loss: 0.1789 - val_accuracy: 0.5378\n",
      "Epoch 46/100\n",
      "3456/4162 [=======================>......] - ETA: 0s - loss: 0.3239 - accuracy: 0.5528\n",
      "Epoch 00046: val_loss did not improve from 0.17319\n",
      "4162/4162 [==============================] - 0s 36us/sample - loss: 0.3666 - accuracy: 0.5465 - val_loss: 0.2070 - val_accuracy: 0.5202\n",
      "Epoch 47/100\n",
      "3584/4162 [========================>.....] - ETA: 0s - loss: 0.3908 - accuracy: 0.5111\n",
      "Epoch 00047: val_loss did not improve from 0.17319\n",
      "4162/4162 [==============================] - 0s 35us/sample - loss: 0.3735 - accuracy: 0.5166 - val_loss: 0.1955 - val_accuracy: 0.5269\n",
      "Epoch 48/100\n",
      "3456/4162 [=======================>......] - ETA: 0s - loss: 0.3602 - accuracy: 0.5487\n",
      "Epoch 00048: val_loss did not improve from 0.17319\n",
      "4162/4162 [==============================] - 0s 35us/sample - loss: 0.3805 - accuracy: 0.5510 - val_loss: 0.1800 - val_accuracy: 0.5567\n",
      "Epoch 49/100\n",
      "3456/4162 [=======================>......] - ETA: 0s - loss: 0.3834 - accuracy: 0.5465\n",
      "Epoch 00049: val_loss did not improve from 0.17319\n",
      "4162/4162 [==============================] - 0s 35us/sample - loss: 0.3647 - accuracy: 0.5429 - val_loss: 0.1896 - val_accuracy: 0.5776\n",
      "Epoch 50/100\n",
      "3456/4162 [=======================>......] - ETA: 0s - loss: 0.3840 - accuracy: 0.5586\n",
      "Epoch 00050: val_loss improved from 0.17319 to 0.16467, saving model to ./data/best_model_ae-conv1d.h5\n",
      "4162/4162 [==============================] - 0s 57us/sample - loss: 0.3744 - accuracy: 0.5570 - val_loss: 0.1647 - val_accuracy: 0.5821\n",
      "Epoch 51/100\n",
      "3456/4162 [=======================>......] - ETA: 0s - loss: 0.3330 - accuracy: 0.5657\n",
      "Epoch 00051: val_loss did not improve from 0.16467\n",
      "4162/4162 [==============================] - 0s 36us/sample - loss: 0.3620 - accuracy: 0.5650 - val_loss: 0.1663 - val_accuracy: 0.5940\n",
      "Epoch 52/100\n",
      "3456/4162 [=======================>......] - ETA: 0s - loss: 0.3310 - accuracy: 0.5813\n",
      "Epoch 00052: val_loss did not improve from 0.16467\n",
      "4162/4162 [==============================] - 0s 36us/sample - loss: 0.3153 - accuracy: 0.5831 - val_loss: 0.1805 - val_accuracy: 0.5934\n",
      "Epoch 53/100\n",
      "3456/4162 [=======================>......] - ETA: 0s - loss: 0.3136 - accuracy: 0.5808\n",
      "Epoch 00053: val_loss improved from 0.16467 to 0.15717, saving model to ./data/best_model_ae-conv1d.h5\n",
      "4162/4162 [==============================] - 0s 58us/sample - loss: 0.3208 - accuracy: 0.5751 - val_loss: 0.1572 - val_accuracy: 0.5448\n",
      "Epoch 54/100\n",
      "3456/4162 [=======================>......] - ETA: 0s - loss: 0.3412 - accuracy: 0.5727\n",
      "Epoch 00054: val_loss did not improve from 0.15717\n",
      "4162/4162 [==============================] - 0s 36us/sample - loss: 0.3311 - accuracy: 0.5636 - val_loss: 0.2015 - val_accuracy: 0.4741\n",
      "Epoch 55/100\n",
      "3456/4162 [=======================>......] - ETA: 0s - loss: 0.4017 - accuracy: 0.5426\n",
      "Epoch 00055: val_loss did not improve from 0.15717\n",
      "4162/4162 [==============================] - 0s 35us/sample - loss: 0.4208 - accuracy: 0.5465 - val_loss: 0.1648 - val_accuracy: 0.5632\n",
      "Epoch 56/100\n",
      "3456/4162 [=======================>......] - ETA: 0s - loss: 0.4341 - accuracy: 0.5524\n",
      "Epoch 00056: val_loss did not improve from 0.15717\n",
      "4162/4162 [==============================] - 0s 36us/sample - loss: 0.3975 - accuracy: 0.5526 - val_loss: 0.1877 - val_accuracy: 0.5632\n",
      "Epoch 57/100\n",
      "3456/4162 [=======================>......] - ETA: 0s - loss: 0.3448 - accuracy: 0.5638\n",
      "Epoch 00057: val_loss did not improve from 0.15717\n",
      "4162/4162 [==============================] - 0s 35us/sample - loss: 0.3229 - accuracy: 0.5681 - val_loss: 0.1702 - val_accuracy: 0.6106\n",
      "Epoch 58/100\n",
      "3456/4162 [=======================>......] - ETA: 0s - loss: 0.3173 - accuracy: 0.5742\n",
      "Epoch 00058: val_loss did not improve from 0.15717\n",
      "4162/4162 [==============================] - 0s 35us/sample - loss: 0.3062 - accuracy: 0.5752 - val_loss: 0.1598 - val_accuracy: 0.5942\n",
      "Epoch 59/100\n",
      "3456/4162 [=======================>......] - ETA: 0s - loss: 0.2778 - accuracy: 0.5645\n",
      "Epoch 00059: val_loss did not improve from 0.15717\n",
      "4162/4162 [==============================] - 0s 35us/sample - loss: 0.2809 - accuracy: 0.5658 - val_loss: 0.1580 - val_accuracy: 0.5850\n",
      "Epoch 60/100\n",
      "3456/4162 [=======================>......] - ETA: 0s - loss: 0.3216 - accuracy: 0.5806\n",
      "Epoch 00060: val_loss did not improve from 0.15717\n",
      "4162/4162 [==============================] - 0s 35us/sample - loss: 0.2989 - accuracy: 0.5798 - val_loss: 0.1671 - val_accuracy: 0.5879\n",
      "Epoch 61/100\n",
      "3456/4162 [=======================>......] - ETA: 0s - loss: 0.3412 - accuracy: 0.5739\n",
      "Epoch 00061: val_loss did not improve from 0.15717\n",
      "4162/4162 [==============================] - 0s 35us/sample - loss: 0.3196 - accuracy: 0.5761 - val_loss: 0.1695 - val_accuracy: 0.6065\n",
      "Epoch 62/100\n",
      "3456/4162 [=======================>......] - ETA: 0s - loss: 0.3388 - accuracy: 0.5751\n",
      "Epoch 00062: val_loss did not improve from 0.15717\n",
      "4162/4162 [==============================] - 0s 36us/sample - loss: 0.3267 - accuracy: 0.5759 - val_loss: 0.1666 - val_accuracy: 0.5626\n",
      "Epoch 63/100\n",
      "3456/4162 [=======================>......] - ETA: 0s - loss: 0.3070 - accuracy: 0.5799\n",
      "Epoch 00063: val_loss did not improve from 0.15717\n",
      "4162/4162 [==============================] - 0s 36us/sample - loss: 0.3170 - accuracy: 0.5788 - val_loss: 0.1581 - val_accuracy: 0.5938\n",
      "Epoch 64/100\n",
      "3456/4162 [=======================>......] - ETA: 0s - loss: 0.2923 - accuracy: 0.5809\n",
      "Epoch 00064: val_loss did not improve from 0.15717\n",
      "4162/4162 [==============================] - 0s 35us/sample - loss: 0.2930 - accuracy: 0.5819 - val_loss: 0.1601 - val_accuracy: 0.5571\n",
      "Epoch 65/100\n",
      "3456/4162 [=======================>......] - ETA: 0s - loss: 0.2734 - accuracy: 0.5797\n",
      "Epoch 00065: val_loss improved from 0.15717 to 0.14857, saving model to ./data/best_model_ae-conv1d.h5\n",
      "4162/4162 [==============================] - 0s 58us/sample - loss: 0.2752 - accuracy: 0.5745 - val_loss: 0.1486 - val_accuracy: 0.5525\n",
      "Epoch 66/100\n",
      "3456/4162 [=======================>......] - ETA: 0s - loss: 0.2987 - accuracy: 0.5639\n",
      "Epoch 00066: val_loss did not improve from 0.14857\n",
      "4162/4162 [==============================] - 0s 36us/sample - loss: 0.2857 - accuracy: 0.5614 - val_loss: 0.1807 - val_accuracy: 0.5416\n",
      "Epoch 67/100\n",
      "3456/4162 [=======================>......] - ETA: 0s - loss: 0.3236 - accuracy: 0.5679\n",
      "Epoch 00067: val_loss did not improve from 0.14857\n",
      "4162/4162 [==============================] - 0s 35us/sample - loss: 0.3221 - accuracy: 0.5675 - val_loss: 0.1517 - val_accuracy: 0.6042\n",
      "Epoch 68/100\n",
      "3456/4162 [=======================>......] - ETA: 0s - loss: 0.3012 - accuracy: 0.5705\n",
      "Epoch 00068: val_loss did not improve from 0.14857\n",
      "4162/4162 [==============================] - 0s 36us/sample - loss: 0.3341 - accuracy: 0.5660 - val_loss: 0.2004 - val_accuracy: 0.5317\n",
      "Epoch 69/100\n",
      "3456/4162 [=======================>......] - ETA: 0s - loss: 0.3158 - accuracy: 0.5539\n",
      "Epoch 00069: val_loss did not improve from 0.14857\n",
      "4162/4162 [==============================] - 0s 36us/sample - loss: 0.3319 - accuracy: 0.5564 - val_loss: 0.1885 - val_accuracy: 0.5360\n",
      "Epoch 70/100\n",
      "3456/4162 [=======================>......] - ETA: 0s - loss: 0.3632 - accuracy: 0.5637\n",
      "Epoch 00070: val_loss did not improve from 0.14857\n",
      "4162/4162 [==============================] - 0s 35us/sample - loss: 0.3406 - accuracy: 0.5654 - val_loss: 0.1817 - val_accuracy: 0.5755\n",
      "Epoch 71/100\n",
      "3456/4162 [=======================>......] - ETA: 0s - loss: 0.3326 - accuracy: 0.5849\n",
      "Epoch 00071: val_loss did not improve from 0.14857\n",
      "4162/4162 [==============================] - 0s 36us/sample - loss: 0.3068 - accuracy: 0.5858 - val_loss: 0.1835 - val_accuracy: 0.5818\n",
      "Epoch 72/100\n",
      "3456/4162 [=======================>......] - ETA: 0s - loss: 0.3011 - accuracy: 0.5679\n",
      "Epoch 00072: val_loss did not improve from 0.14857\n",
      "4162/4162 [==============================] - 0s 36us/sample - loss: 0.3079 - accuracy: 0.5698 - val_loss: 0.1787 - val_accuracy: 0.5876\n",
      "Epoch 73/100\n",
      "3456/4162 [=======================>......] - ETA: 0s - loss: 0.2841 - accuracy: 0.5839\n",
      "Epoch 00073: val_loss did not improve from 0.14857\n",
      "4162/4162 [==============================] - 0s 36us/sample - loss: 0.2801 - accuracy: 0.5859 - val_loss: 0.1793 - val_accuracy: 0.6119\n",
      "Epoch 74/100\n",
      "3456/4162 [=======================>......] - ETA: 0s - loss: 0.3204 - accuracy: 0.5757\n",
      "Epoch 00074: val_loss did not improve from 0.14857\n",
      "4162/4162 [==============================] - 0s 35us/sample - loss: 0.3215 - accuracy: 0.5756 - val_loss: 0.1681 - val_accuracy: 0.5899\n",
      "Epoch 75/100\n",
      "3456/4162 [=======================>......] - ETA: 0s - loss: 0.3064 - accuracy: 0.5608\n",
      "Epoch 00075: val_loss did not improve from 0.14857\n",
      "4162/4162 [==============================] - 0s 36us/sample - loss: 0.3173 - accuracy: 0.5602 - val_loss: 0.1785 - val_accuracy: 0.5803\n",
      "Epoch 76/100\n",
      "3456/4162 [=======================>......] - ETA: 0s - loss: 0.3227 - accuracy: 0.5761\n",
      "Epoch 00076: val_loss did not improve from 0.14857\n",
      "4162/4162 [==============================] - 0s 35us/sample - loss: 0.3627 - accuracy: 0.5753 - val_loss: 0.2012 - val_accuracy: 0.5674\n",
      "Epoch 77/100\n",
      "3456/4162 [=======================>......] - ETA: 0s - loss: 0.3348 - accuracy: 0.5589\n",
      "Epoch 00077: val_loss did not improve from 0.14857\n",
      "4162/4162 [==============================] - 0s 35us/sample - loss: 0.3428 - accuracy: 0.5610 - val_loss: 0.1724 - val_accuracy: 0.5389\n",
      "Epoch 78/100\n",
      "3456/4162 [=======================>......] - ETA: 0s - loss: 0.3125 - accuracy: 0.5559\n",
      "Epoch 00078: val_loss did not improve from 0.14857\n",
      "4162/4162 [==============================] - 0s 35us/sample - loss: 0.3420 - accuracy: 0.5572 - val_loss: 0.1863 - val_accuracy: 0.6011\n",
      "Epoch 79/100\n",
      "3456/4162 [=======================>......] - ETA: 0s - loss: 0.3514 - accuracy: 0.5669\n",
      "Epoch 00079: val_loss did not improve from 0.14857\n",
      "4162/4162 [==============================] - 0s 35us/sample - loss: 0.3569 - accuracy: 0.5638 - val_loss: 0.1949 - val_accuracy: 0.5404\n",
      "Epoch 80/100\n",
      "3584/4162 [========================>.....] - ETA: 0s - loss: 0.3562 - accuracy: 0.5631\n",
      "Epoch 00080: val_loss did not improve from 0.14857\n",
      "4162/4162 [==============================] - 0s 35us/sample - loss: 0.3473 - accuracy: 0.5657 - val_loss: 0.1735 - val_accuracy: 0.5989\n",
      "Epoch 81/100\n",
      "3584/4162 [========================>.....] - ETA: 0s - loss: 0.2990 - accuracy: 0.5598\n",
      "Epoch 00081: val_loss did not improve from 0.14857\n",
      "4162/4162 [==============================] - 0s 35us/sample - loss: 0.2875 - accuracy: 0.5657 - val_loss: 0.1571 - val_accuracy: 0.6006\n",
      "Epoch 82/100\n",
      "3456/4162 [=======================>......] - ETA: 0s - loss: 0.2765 - accuracy: 0.5832\n",
      "Epoch 00082: val_loss did not improve from 0.14857\n",
      "4162/4162 [==============================] - 0s 36us/sample - loss: 0.2730 - accuracy: 0.5848 - val_loss: 0.1575 - val_accuracy: 0.6178\n",
      "Epoch 83/100\n",
      "3456/4162 [=======================>......] - ETA: 0s - loss: 0.2832 - accuracy: 0.5916\n",
      "Epoch 00083: val_loss did not improve from 0.14857\n",
      "4162/4162 [==============================] - 0s 36us/sample - loss: 0.2785 - accuracy: 0.5873 - val_loss: 0.1583 - val_accuracy: 0.6127\n",
      "Epoch 84/100\n",
      "3456/4162 [=======================>......] - ETA: 0s - loss: 0.2432 - accuracy: 0.5886\n",
      "Epoch 00084: val_loss improved from 0.14857 to 0.14497, saving model to ./data/best_model_ae-conv1d.h5\n",
      "4162/4162 [==============================] - 0s 58us/sample - loss: 0.2440 - accuracy: 0.5875 - val_loss: 0.1450 - val_accuracy: 0.5922\n",
      "Epoch 85/100\n",
      "3456/4162 [=======================>......] - ETA: 0s - loss: 0.2484 - accuracy: 0.5833\n",
      "Epoch 00085: val_loss did not improve from 0.14497\n",
      "4162/4162 [==============================] - 0s 35us/sample - loss: 0.3076 - accuracy: 0.5847 - val_loss: 0.1456 - val_accuracy: 0.6012\n",
      "Epoch 86/100\n",
      "3456/4162 [=======================>......] - ETA: 0s - loss: 0.2831 - accuracy: 0.5813\n",
      "Epoch 00086: val_loss did not improve from 0.14497\n",
      "4162/4162 [==============================] - 0s 36us/sample - loss: 0.2812 - accuracy: 0.5810 - val_loss: 0.1636 - val_accuracy: 0.5610\n",
      "Epoch 87/100\n",
      "3456/4162 [=======================>......] - ETA: 0s - loss: 0.3177 - accuracy: 0.5846\n",
      "Epoch 00087: val_loss did not improve from 0.14497\n",
      "4162/4162 [==============================] - 0s 35us/sample - loss: 0.3190 - accuracy: 0.5829 - val_loss: 0.1575 - val_accuracy: 0.5827\n",
      "Epoch 88/100\n",
      "3456/4162 [=======================>......] - ETA: 0s - loss: 0.3402 - accuracy: 0.5918\n",
      "Epoch 00088: val_loss did not improve from 0.14497\n",
      "4162/4162 [==============================] - 0s 35us/sample - loss: 0.3218 - accuracy: 0.5926 - val_loss: 0.1532 - val_accuracy: 0.6316\n",
      "Epoch 89/100\n",
      "3456/4162 [=======================>......] - ETA: 0s - loss: 0.2838 - accuracy: 0.5909\n",
      "Epoch 00089: val_loss did not improve from 0.14497\n",
      "4162/4162 [==============================] - 0s 36us/sample - loss: 0.3003 - accuracy: 0.5891 - val_loss: 0.1532 - val_accuracy: 0.5652\n",
      "Epoch 90/100\n",
      "3456/4162 [=======================>......] - ETA: 0s - loss: 0.2888 - accuracy: 0.5802\n",
      "Epoch 00090: val_loss did not improve from 0.14497\n",
      "4162/4162 [==============================] - 0s 35us/sample - loss: 0.2738 - accuracy: 0.5811 - val_loss: 0.1591 - val_accuracy: 0.5962\n",
      "Epoch 91/100\n",
      "3456/4162 [=======================>......] - ETA: 0s - loss: 0.2412 - accuracy: 0.5894\n",
      "Epoch 00091: val_loss did not improve from 0.14497\n",
      "4162/4162 [==============================] - 0s 36us/sample - loss: 0.2425 - accuracy: 0.5898 - val_loss: 0.1470 - val_accuracy: 0.5859\n",
      "Epoch 92/100\n",
      "3456/4162 [=======================>......] - ETA: 0s - loss: 0.2890 - accuracy: 0.5888\n",
      "Epoch 00092: val_loss did not improve from 0.14497\n",
      "4162/4162 [==============================] - 0s 35us/sample - loss: 0.2763 - accuracy: 0.5876 - val_loss: 0.1605 - val_accuracy: 0.6064\n",
      "Epoch 93/100\n",
      "3456/4162 [=======================>......] - ETA: 0s - loss: 0.2909 - accuracy: 0.5897\n",
      "Epoch 00093: val_loss did not improve from 0.14497\n",
      "4162/4162 [==============================] - 0s 36us/sample - loss: 0.2886 - accuracy: 0.5895 - val_loss: 0.1703 - val_accuracy: 0.6192\n",
      "Epoch 94/100\n",
      "3456/4162 [=======================>......] - ETA: 0s - loss: 0.2499 - accuracy: 0.6015\n",
      "Epoch 00094: val_loss did not improve from 0.14497\n",
      "4162/4162 [==============================] - 0s 36us/sample - loss: 0.2658 - accuracy: 0.5955 - val_loss: 0.1477 - val_accuracy: 0.5813\n",
      "Epoch 95/100\n",
      "3456/4162 [=======================>......] - ETA: 0s - loss: 0.2914 - accuracy: 0.5801\n",
      "Epoch 00095: val_loss did not improve from 0.14497\n",
      "4162/4162 [==============================] - 0s 37us/sample - loss: 0.2927 - accuracy: 0.5761 - val_loss: 0.1804 - val_accuracy: 0.5614\n",
      "Epoch 96/100\n",
      "3456/4162 [=======================>......] - ETA: 0s - loss: 0.2679 - accuracy: 0.5972\n",
      "Epoch 00096: val_loss did not improve from 0.14497\n",
      "4162/4162 [==============================] - 0s 35us/sample - loss: 0.2881 - accuracy: 0.5971 - val_loss: 0.1778 - val_accuracy: 0.6110\n",
      "Epoch 97/100\n",
      "3456/4162 [=======================>......] - ETA: 0s - loss: 0.2962 - accuracy: 0.5687\n",
      "Epoch 00097: val_loss did not improve from 0.14497\n",
      "4162/4162 [==============================] - 0s 35us/sample - loss: 0.2935 - accuracy: 0.5675 - val_loss: 0.1782 - val_accuracy: 0.5305\n",
      "Epoch 98/100\n",
      "3456/4162 [=======================>......] - ETA: 0s - loss: 0.3053 - accuracy: 0.5941\n",
      "Epoch 00098: val_loss did not improve from 0.14497\n",
      "4162/4162 [==============================] - 0s 36us/sample - loss: 0.2922 - accuracy: 0.5942 - val_loss: 0.1657 - val_accuracy: 0.5733\n",
      "Epoch 99/100\n",
      "3584/4162 [========================>.....] - ETA: 0s - loss: 0.2968 - accuracy: 0.6052\n",
      "Epoch 00099: val_loss did not improve from 0.14497\n",
      "4162/4162 [==============================] - 0s 35us/sample - loss: 0.2830 - accuracy: 0.6020 - val_loss: 0.1515 - val_accuracy: 0.5782\n",
      "Epoch 100/100\n",
      "3456/4162 [=======================>......] - ETA: 0s - loss: 0.2522 - accuracy: 0.5979\n",
      "Epoch 00100: val_loss did not improve from 0.14497\n",
      "4162/4162 [==============================] - 0s 35us/sample - loss: 0.2590 - accuracy: 0.5980 - val_loss: 0.1615 - val_accuracy: 0.6504\n"
     ]
    }
   ],
   "source": [
    "# train the model\n",
    "\n",
    "# number of training epochs - feel free to change\n",
    "#\n",
    "#       100 epochs ~= 3 minutes for Conv 1D\n",
    "\n",
    "epochs_num = 100\n",
    "history2 = autoencoder2.fit(x_train, x_train, batch_size=128, epochs=epochs_num, validation_split=0.1, verbose=1, callbacks=callbacks_list2)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "./data/best_model_ae-conv1d.h5\n"
     ]
    }
   ],
   "source": [
    "# load weights from best model\n",
    "print(data_dir+\"best_model_ae-conv1d.h5\")\n",
    "autoencoder2.load_weights(data_dir+\"best_model_ae-conv1d.h5\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# show plot accuracy changes during training\n",
    "\n",
    "plt.plot(history2.history['loss'],'g')\n",
    "plt.plot(history2.history['val_loss'],'r')\n",
    "plt.title('1D-Conv Loss across epochs')\n",
    "plt.ylabel('Loss')\n",
    "plt.xlabel('# epoch')\n",
    "plt.legend(['train', 'test'], loc='lower right')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's go ahead and use our model to make predictions on the test data. Remember, this is a reconstruction model, so the predictions, ideally, should look similar to the original data for normal disks."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Use model to predict on test data set\n",
    "y_pred2 = autoencoder2.predict(x_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The next cell will plot a comparison of the original data to the predicted data for a random record. It only shows compares the features for the first day in the sequence. Run it a few times to see how well your model came out."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "This record is from a normal disk.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# create horizontal positions for scatter plot\n",
    "x_pos = np.arange(x_test.shape[2])\n",
    "\n",
    "# select random record\n",
    "rec_num = random.randint(0, y_pred.shape[0])\n",
    "\n",
    "# plot predicted points\n",
    "plt.scatter(x_pos, x_test[rec_num,0,:], c=\"#ff0000\")\n",
    "plt.scatter(x_pos, y_pred2[rec_num,0,:], c=\"#0000ff\")\n",
    "\n",
    "# label graph\n",
    "plt.xlabel(\"Feature\")\n",
    "plt.ylabel(\"Value\")\n",
    "plt.xticks(range(x_test.shape[2]))\n",
    "plt.title(\"Compare original to reconstruction for day 0 of seq num \" + str(rec_num))\n",
    "\n",
    "if y_labels_seq[rec_num] == 0:\n",
    "    print(\"This record is from a normal disk.\")\n",
    "else:\n",
    "    print(\"This record is from an anomalous disk.\") "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Like before, we need to determine a \"threshold\" of difference in detecting an anomaly. We'll use the same technique on the new data to determine our threshold."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Suggested Threshold: 0.1777364960109314\n"
     ]
    }
   ],
   "source": [
    "# Calcuate MSE for all sequences and predictions\n",
    "import statistics\n",
    "\n",
    "# determine median mse to split data into normal vs anomaly\n",
    "pred_mse2 = []  # calculated mse for test data\n",
    "pred_res2 = []  # result: 0 normal, 1 anomaly\n",
    "\n",
    "for i in range(len(x_test)):\n",
    "    pred_mse2.append(mean_squared_error(y_pred2[i,:,:], x_test[i,:,:]))\n",
    "\n",
    "# Find number of disks that we know are normal and use that value as splitting point for data\n",
    "num_normal_disks = list(y_labels_seq).count(0)\n",
    "\n",
    "sort_mse2 = pred_mse2.copy()\n",
    "sort_mse2.sort()\n",
    "suggested_threshold2 = sort_mse2[num_normal_disks]\n",
    "print(\"Suggested Threshold:\", suggested_threshold2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Used threshold: 0.1777364960109314\n",
      "Resulting in the following classification of predicted data:\n",
      "- Normal Disks:    601\n",
      "- Anomalous Disks: 233\n"
     ]
    }
   ],
   "source": [
    "# Routine to specify whether or not a sequence is anomalous\n",
    "\n",
    "# specify override threshold or leave 0 to use suggested threshold\n",
    "override_threshold2 = 0\n",
    "\n",
    "if override_threshold2 == 0:\n",
    "    threshold2 = suggested_threshold2\n",
    "else:\n",
    "    threshold2 = override_threshold2\n",
    "\n",
    "for mse in pred_mse2:\n",
    "    if (mse <= threshold2):\n",
    "        pred_res2.append(0)\n",
    "    else:\n",
    "        pred_res2.append(1)\n",
    "        \n",
    "print(\"Used threshold:\", threshold2)\n",
    "print(\"Resulting in the following classification of predicted data:\")\n",
    "print(\"- Normal Disks:   \", pred_res2.count(0))\n",
    "print(\"- Anomalous Disks:\", pred_res2.count(1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ROC Curve\n",
    "\n",
    "The ROC curve plots the False Positive rate along the horizontal axis and the True Positive rate along the vertical axis. False Positive rate and True Positive rate will change based on the various threshold values. \n",
    "\n",
    "The threshold is high at the y-intercept, resulting in a 0% False Positive rate and a low True Positive rate. As the threshold decreases, the True Positive rate will increase but so will the False Positive rate. \n",
    "\n",
    "Perfect classification occurs if the distribution of anomalous disks and normal disks are completely separated. This would yield a point in the upper left corner of the ROC space, where the False Positive rate is 0% and the True Positive rate is 100%. \n",
    "\n",
    "https://en.wikipedia.org/wiki/Receiver_operating_characteristic\n",
    "\n",
    "Check the ROC curve of the LSTM model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import roc_curve, auc\n",
    "\n",
    "\n",
    "def roc_auc_show(pred_y, y_label, figure_label_name='LSTM'):\n",
    "    fpr, tpr, _ = roc_curve(y_label, pred_y)\n",
    "    roc_auc = auc(fpr, tpr)\n",
    "\n",
    "    plt.figure()\n",
    "    lw = 2\n",
    "    plt.plot(fpr, tpr, color='darkorange',\n",
    "             lw=lw, label=figure_label_name + ' ROC curve (area = %0.2f)' % roc_auc)\n",
    "    plt.plot([0, 1], [0, 1], color='navy', lw=lw, linestyle='--')\n",
    "    plt.xlim([0.0, 1.0])\n",
    "    plt.ylim([0.0, 1.05])\n",
    "    plt.xlabel('False Positive Rate')\n",
    "    plt.ylabel('True Positive Rate')\n",
    "    plt.title('Receiver operating characteristic example')\n",
    "    plt.legend(loc=\"lower right\")\n",
    "    plt.show()\n",
    "\n",
    "roc_auc_show(pred_mse, y_labels_seq)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check the ROC curve of the 1D-Conv model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "roc_auc_show(pred_mse2, y_labels_seq, figure_label_name='CNN LSTM')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Performance comparison of LSTM and 1D-Conv models"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that you've tested both LSTM and the 1D Convultional anomaly detectors, how do you think their accuracy and performance compared?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "For LSTM AE\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.81      0.81      0.81       600\n",
      "           1       0.51      0.50      0.51       234\n",
      "\n",
      "    accuracy                           0.72       834\n",
      "   macro avg       0.66      0.66      0.66       834\n",
      "weighted avg       0.72      0.72      0.72       834\n",
      "\n",
      "For 1D Conv AE\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.81      0.81      0.81       600\n",
      "           1       0.51      0.50      0.51       234\n",
      "\n",
      "    accuracy                           0.72       834\n",
      "   macro avg       0.66      0.66      0.66       834\n",
      "weighted avg       0.72      0.72      0.72       834\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "\n",
    "y_test = []\n",
    "\n",
    "for i in np.arange(0, len(y_labels), sequence_length):\n",
    "    y_test.append(max(y_labels[i:i+sequence_length]))\n",
    "\n",
    "print('For LSTM AE')\n",
    "print(classification_report(y_test, pred_res))\n",
    "print('For 1D Conv AE')\n",
    "print(classification_report(y_test, pred_res2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## STOP HERE -- END OF SESSION 3\n",
    "We will complete Exercise 3 as part of next session of DLI."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a name=\"e4\"></a>\n",
    "### Exercise 3: Experimenting with Hyperparameters\n",
    "\n",
    "Now that you've worked your through the entire notebook, feel free to go back and adjust the models to see if you can improve accuracy.  You can add layers, increase filters, and change hyperparameters.  This is where the real art of Deep Learning happens...\n",
    "\n",
    "However, it is possible to take a systematic approach to hyperparameter optimization by sweeping the space of all parameter values and finding those which maximize some metric you're interested in.  We can then define a function to construct our model based on a given set of parameters, as well as define a scoring function which creates and trains the model and evaluates it's performance. There are many approaches to perform hyperparameter optimization using Deep Learning models. We will explore one concept called Bayesian Optimization which is a fairly common approach for this type of optimization.  \n",
    "\n",
    "In this example, we'll continue with the LSTM model designed previously and set up the parameter search space to include adjusting the dropout rate and LSTM width along with a factor describing how much of the suggested threshold to use when classifying a defective disk.  In more complicated models, other hyperparameters such as the number of stacked LSTM layers or convolution kernel width could be included in the search space.  Just keep in mind that each new parameter will expand the search space and require more time to maximize.\n",
    "\n",
    "![Bayesian Optimization](https://raw.githubusercontent.com/fmfn/BayesianOptimization/master/examples/bayesian_optimization.gif)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Import the python package for optimizing the architecture for the LSTM autoencoder "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: bayesian-optimization in /opt/conda/envs/rapids/lib/python3.6/site-packages (1.2.0)\r\n",
      "Requirement already satisfied: numpy>=1.9.0 in /opt/conda/envs/rapids/lib/python3.6/site-packages (from bayesian-optimization) (1.18.5)\r\n",
      "Requirement already satisfied: scipy>=0.14.0 in /opt/conda/envs/rapids/lib/python3.6/site-packages (from bayesian-optimization) (1.4.1)\r\n",
      "Requirement already satisfied: scikit-learn>=0.18.0 in /opt/conda/envs/rapids/lib/python3.6/site-packages (from bayesian-optimization) (0.23.0)\r\n",
      "Requirement already satisfied: joblib>=0.11 in /opt/conda/envs/rapids/lib/python3.6/site-packages (from scikit-learn>=0.18.0->bayesian-optimization) (0.16.0)\r\n",
      "Requirement already satisfied: threadpoolctl>=2.0.0 in /opt/conda/envs/rapids/lib/python3.6/site-packages (from scikit-learn>=0.18.0->bayesian-optimization) (2.1.0)\r\n"
     ]
    }
   ],
   "source": [
    "!pip install bayesian-optimization\n",
    "from bayes_opt import BayesianOptimization"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can create a class **Hyperparameter** to create inputs for the optimization and provide a mapping between unscaled parameters required by optimizater and scaled parameters used in the method **lstm_autoencoder** defined below:   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Hyperparameter: \n",
    "    \n",
    "    def __init__(self,inputs=None):\n",
    "        \n",
    "        _defaults = {\"min\": 0, \"max\": 1.0, \"count\": 11, \"scale\" : \"linear\", \"type\" : \"float\" }\n",
    "        \n",
    "        for key in sorted(_defaults.keys()): \n",
    "            try:\n",
    "                _defaults[key] = inputs[key]\n",
    "            except:\n",
    "                pass\n",
    "        \n",
    "        if inputs is not None: \n",
    "            for key in sorted(_defaults.keys()):\n",
    "                try:\n",
    "                    eval(\"self.set_\"+key)(_defaults[key])\n",
    "                except:\n",
    "                    print(key + \"not defined for Hyperparameter \" )\n",
    "            self.update()\n",
    "            \n",
    "        print(self)\n",
    "            \n",
    "    def set_min(self,val=None):\n",
    "        self.min = val \n",
    "        \n",
    "    def set_max(self,val=None):\n",
    "        self.max = val \n",
    "        \n",
    "    def set_count(self,val=None):\n",
    "        self.count = val \n",
    "        \n",
    "    def set_step(self,val=None):\n",
    "        self.step = val \n",
    "                     \n",
    "    def set_type(self,val=None):\n",
    "        self.type = val \n",
    "\n",
    "    def set_scale(self,val=None):\n",
    "        self.scale = val \n",
    "        \n",
    "    def update(self):\n",
    "        if self.scale == \"log\":\n",
    "             self.step = (np.log(self.max) - np.log(self.min))/(self.count-1)\n",
    "        else:\n",
    "             self.step = (self.max - self.min)/(self.count-1)\n",
    "        if self.type == \"int\": \n",
    "            self.step = int(self.step)\n",
    "        \n",
    "    def from_unscaled(self,val=None): \n",
    "        if self.scale == \"log\":\n",
    "            _tval =  np.exp(np.log(self.min) + self.step*val)\n",
    "        else:\n",
    "            _tval =  self.min + self.step*val\n",
    "        \n",
    "        if self.type == \"int\":\n",
    "            _tval = int(_tval)\n",
    "        \n",
    "        return _tval \n",
    "    \n",
    "    def to_unscaled(self,val=None): \n",
    "        if self.scale == \"log\":\n",
    "            _tval =  (np.log(val) - np.log(self.min))/self.step\n",
    "        else:\n",
    "            _tval =  (val - self.min)/self.step\n",
    "        \n",
    "        if self.type == \"int\":\n",
    "            _tval = int(_tval)\n",
    "        \n",
    "        return _tval \n",
    "    \n",
    "    def __repr__(self):\n",
    "        \n",
    "        _cur_str = ' Hyperparameter (min: {}, max: {}, scale: {}, step: {}, count: {}, type: {})'.format(self.min,\n",
    "                                         self.max,\n",
    "                                         self.scale,\n",
    "                                         self.step, \n",
    "                                         self.count, \n",
    "                                         self.type )\n",
    "        return  _cur_str \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Method to plot the training history of each architecture "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_history(history,result,lw,lr):\n",
    "    plt.figure()\n",
    "    plt.plot(history['loss'],'g')\n",
    "    plt.plot(history['val_loss'],'r')\n",
    "\n",
    "    plt.ylabel('Loss')\n",
    "    plt.xlabel('# epoch')\n",
    "    plt.legend(['train', 'test'], loc='lower right')\n",
    "    plt.ylim([0.15,0.55])\n",
    "    plt.title(\"LSTM Loss across epochs \\n lstm width {:7d} learning rate {:7.4f} objective {:7.4f}\" .format(lw,lr,result))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>\n",
    "Next define a function to train the model and return the accuracy:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Model #1 - Wide LSTM-based Encoder-Decoder\n",
    "data_shape = x_train.shape[1:]  \n",
    "\n",
    "dp_lvl = 0.2\n",
    "\n",
    "def lstm_autoencoder(data_shape, params_list):\n",
    "    \n",
    "    \n",
    "    mod_params = {} \n",
    "    mod_params['lstm_width'] = lstm_width.from_unscaled(int(np.round(params_list[0])))\n",
    "    mod_params['learning_rate'] = learning_rate.from_unscaled(params_list[1])\n",
    "    mod_params['num_epochs'] = 20\n",
    "    mod_params['batch_size'] = 128 \n",
    "    \n",
    "    inputs = Input(shape=(data_shape))\n",
    "    encoded = LSTM(int(mod_params[\"lstm_width\"]), dropout = dp_lvl, recurrent_dropout = dp_lvl, return_sequences =  False, activation='relu')(inputs)\n",
    "    decoded = RepeatVector(data_shape[0])(encoded)\n",
    "    decoded = LSTM(int(mod_params[\"lstm_width\"]), return_sequences=True, activation='relu')(decoded)\n",
    "    decoded = LSTM(data_shape[1], activation='tanh', return_sequences=True)(decoded)\n",
    "\n",
    "    # Build entire model\n",
    "    autoencoder = Model(inputs, decoded)\n",
    "    \n",
    "    filename = \"case_%.3e_%d_best_model_ae-lstm.h5\" %  \\\n",
    "                                 (mod_params[\"learning_rate\"],mod_params[\"lstm_width\"])\n",
    "    if print_flag == 1:\n",
    "        print(autoencoder.summary())\n",
    "    return autoencoder, filename, mod_params"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Utility methods for plotting and reporting resutls "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "def print_history(num_step, mod_params, loss, result, num_train, num_non_train, run_time):\n",
    "\n",
    "    if num_step == 0: \n",
    "        print(\"\\n| {:>9s} | {:>10s} | {:>13s} | {:>6s} | {:>9s} | {:>9s} | {:>13s} | {:>15s} | \\n|\".format(\"Iteration\", \"lstm width\",\n",
    "                                                                         \"learning rate\",\n",
    "                                                                         \"loss\",\"objective\", \"trainable\", \"non trainable\",\n",
    "                                                                         \"run time (mins)\")+'-'*107+\"|\")\n",
    "        \n",
    "    print( \"| {:>9d} | {:>10d} | {:>13.6f} | {:>6.4f} | {:>9.4f} | {:>9d} | {:>13d} | {:>15.4f} |\".format( num_step + 1,\n",
    "        mod_params['lstm_width'],mod_params['learning_rate'],loss, result, num_train, num_non_train, run_time))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_model_loss(idx_list,header): \n",
    "    \n",
    "    print(\" \"*20 + header)\n",
    "    for idx in idx_list:\n",
    "        cur_iter = iter_history[idx]\n",
    "        iter_history[idx][\"model\"].summary()\n",
    "        plot_history(cur_iter[\"history\"],cur_iter['objective'],\n",
    "                     cur_iter['params'][\"lstm_width\"],cur_iter['params'][\"learning_rate\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_loss_map(results_list):\n",
    "    results_list_shifted = [r - 0.9*min_res for r in results_list]\n",
    "    plt.figure(figsize=(6,4))\n",
    "    plt.scatter([c['params']['lstm_width'] for c in iter_history],\n",
    "                [c['params']['learning_rate'] for c in iter_history],\n",
    "                s=[r*500 for r in results_list_shifted],\n",
    "                c=[r for r in results_list_shifted ])\n",
    "\n",
    "    for i,c in enumerate(iter_history):\n",
    "        plt.text(c['params']['lstm_width'], c['params']['learning_rate'], r'%d'%(i),fontsize = 14)\n",
    "    plt.xlabel('lstm_width',fontsize=14)\n",
    "    plt.ylabel('learning_rate',fontsize=14)\n",
    "    plt.tick_params(labelsize=12)\n",
    "    plt.yscale('log')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>\n",
    "\n",
    "### Main method for building the autoencoder\n",
    "This method builds the autoencoder and trains using provided data using \n",
    "* method **lstm_autoencoder**\n",
    "* print history of training with the inputs to the architecture of the LSTM model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "import statistics\n",
    "from tensorflow.keras.callbacks import ModelCheckpoint, EarlyStopping\n",
    "\n",
    "print_flag = 0 \n",
    "iter_history = []\n",
    "\n",
    "def count_params(model, param_type = \"train\"): # Compute number of params in a model (the actual number of floats)\n",
    "    if param_type == \"train\":\n",
    "        return sum([np.prod(K.get_value(w).shape) for w in model.trainable_weights])\n",
    "    else: \n",
    "        return sum([np.prod(K.get_value(w).shape) for w in model.non_trainable_weights])\n",
    "        \n",
    "def train_and_fit_AE(param1, param2):\n",
    "    \n",
    "    time_start = time.time()\n",
    "    autoencoder, filename, params  = lstm_autoencoder(data_shape, [param1, param2])\n",
    "   \n",
    "    # checkpoint\n",
    "    checkpoint = ModelCheckpoint(data_dir+filename, monitor='val_loss', \n",
    "                                 verbose=print_flag, save_best_only=True, mode='min')\n",
    "    es = EarlyStopping(patience=15, verbose=print_flag)\n",
    "\n",
    "    optimizer = Adam(lr=params['learning_rate'])\n",
    "    autoencoder.compile(optimizer=optimizer, loss='mae')\n",
    "\n",
    "    history = autoencoder.fit(x_train, x_train, batch_size=params['batch_size'], epochs=params['num_epochs'], \n",
    "                              validation_split=0.1, verbose=print_flag, callbacks=[es, checkpoint])\n",
    "    loss = history.history[\"val_loss\"][-1]\n",
    "    if np.isnan(loss) : loss = 1e8\n",
    "    result = np.exp(-20*loss**2) \n",
    "\n",
    "    print_history(len(iter_history), params, loss, result, \n",
    "                  count_params(autoencoder,\"train\"), \n",
    "                  count_params(autoencoder,\"non_train\"),\n",
    "                  (time.time()-time_start)/60.0)\n",
    "    iter_history.append({'objective':result, 'params': params, 'history':history.history, \"model\" : autoencoder})\n",
    "    \n",
    "    return result\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's define the parameter space:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " Hyperparameter (min: 32, max: 128, scale: linear, step: 6, count: 15, type: int)\n",
      " Hyperparameter (min: 0.0005, max: 0.1, scale: log, step: 0.37845124046771694, count: 15, type: float)\n"
     ]
    }
   ],
   "source": [
    "lstm_width = Hyperparameter({\"min\": 32, \"max\": 128, \"count\": 15, \"type\" : \"int\" })\n",
    "learning_rate = Hyperparameter({\"min\": 0.0005, \"max\": 0.1, \"count\": 15, \"scale\" : \"log\"})\n",
    "\n",
    "pbounds = {\"param1\"       : (0,lstm_width.count-1),\n",
    "           \"param2\"    : (0,learning_rate.count-1)}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's run one test of our \"train_and_fit\" function to ensure it runs correctly."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model_2\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "input_3 (InputLayer)         [(None, 16, 21)]          0         \n",
      "_________________________________________________________________\n",
      "lstm_3 (LSTM)                (None, 80)                32640     \n",
      "_________________________________________________________________\n",
      "repeat_vector_1 (RepeatVecto (None, 16, 80)            0         \n",
      "_________________________________________________________________\n",
      "lstm_4 (LSTM)                (None, 16, 80)            51520     \n",
      "_________________________________________________________________\n",
      "lstm_5 (LSTM)                (None, 16, 21)            8568      \n",
      "=================================================================\n",
      "Total params: 92,728\n",
      "Trainable params: 92,728\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "None\n",
      "Train on 4162 samples, validate on 463 samples\n",
      "Epoch 1/20\n",
      "3968/4162 [===========================>..] - ETA: 0s - loss: 0.3451\n",
      "Epoch 00001: val_loss improved from inf to 0.26597, saving model to ./data/case_3.317e-03_80_best_model_ae-lstm.h5\n",
      "4162/4162 [==============================] - 5s 1ms/sample - loss: 0.3431 - val_loss: 0.2660\n",
      "Epoch 2/20\n",
      "3968/4162 [===========================>..] - ETA: 0s - loss: 0.2618\n",
      "Epoch 00002: val_loss improved from 0.26597 to 0.23573, saving model to ./data/case_3.317e-03_80_best_model_ae-lstm.h5\n",
      "4162/4162 [==============================] - 1s 191us/sample - loss: 0.2605 - val_loss: 0.2357\n",
      "Epoch 3/20\n",
      "3968/4162 [===========================>..] - ETA: 0s - loss: 0.2437\n",
      "Epoch 00003: val_loss improved from 0.23573 to 0.22023, saving model to ./data/case_3.317e-03_80_best_model_ae-lstm.h5\n",
      "4162/4162 [==============================] - 1s 190us/sample - loss: 0.2428 - val_loss: 0.2202\n",
      "Epoch 4/20\n",
      "3968/4162 [===========================>..] - ETA: 0s - loss: 0.2306\n",
      "Epoch 00004: val_loss improved from 0.22023 to 0.20998, saving model to ./data/case_3.317e-03_80_best_model_ae-lstm.h5\n",
      "4162/4162 [==============================] - 1s 193us/sample - loss: 0.2300 - val_loss: 0.2100\n",
      "Epoch 5/20\n",
      "3968/4162 [===========================>..] - ETA: 0s - loss: 0.2199\n",
      "Epoch 00005: val_loss improved from 0.20998 to 0.19864, saving model to ./data/case_3.317e-03_80_best_model_ae-lstm.h5\n",
      "4162/4162 [==============================] - 1s 191us/sample - loss: 0.2196 - val_loss: 0.1986\n",
      "Epoch 6/20\n",
      "3968/4162 [===========================>..] - ETA: 0s - loss: 0.2166\n",
      "Epoch 00006: val_loss improved from 0.19864 to 0.19634, saving model to ./data/case_3.317e-03_80_best_model_ae-lstm.h5\n",
      "4162/4162 [==============================] - 1s 193us/sample - loss: 0.2166 - val_loss: 0.1963\n",
      "Epoch 7/20\n",
      "3968/4162 [===========================>..] - ETA: 0s - loss: 0.2127\n",
      "Epoch 00007: val_loss improved from 0.19634 to 0.19102, saving model to ./data/case_3.317e-03_80_best_model_ae-lstm.h5\n",
      "4162/4162 [==============================] - 1s 191us/sample - loss: 0.2124 - val_loss: 0.1910\n",
      "Epoch 8/20\n",
      "3968/4162 [===========================>..] - ETA: 0s - loss: 0.2060\n",
      "Epoch 00008: val_loss improved from 0.19102 to 0.18644, saving model to ./data/case_3.317e-03_80_best_model_ae-lstm.h5\n",
      "4162/4162 [==============================] - 1s 190us/sample - loss: 0.2073 - val_loss: 0.1864\n",
      "Epoch 9/20\n",
      "3968/4162 [===========================>..] - ETA: 0s - loss: 0.2055\n",
      "Epoch 00009: val_loss improved from 0.18644 to 0.18413, saving model to ./data/case_3.317e-03_80_best_model_ae-lstm.h5\n",
      "4162/4162 [==============================] - 1s 190us/sample - loss: 0.2051 - val_loss: 0.1841\n",
      "Epoch 10/20\n",
      "3968/4162 [===========================>..] - ETA: 0s - loss: 0.2028\n",
      "Epoch 00010: val_loss did not improve from 0.18413\n",
      "4162/4162 [==============================] - 1s 172us/sample - loss: 0.2033 - val_loss: 0.1848\n",
      "Epoch 11/20\n",
      "3968/4162 [===========================>..] - ETA: 0s - loss: 0.2000\n",
      "Epoch 00011: val_loss improved from 0.18413 to 0.18236, saving model to ./data/case_3.317e-03_80_best_model_ae-lstm.h5\n",
      "4162/4162 [==============================] - 1s 192us/sample - loss: 0.2001 - val_loss: 0.1824\n",
      "Epoch 12/20\n",
      "3968/4162 [===========================>..] - ETA: 0s - loss: 0.1987\n",
      "Epoch 00012: val_loss improved from 0.18236 to 0.18187, saving model to ./data/case_3.317e-03_80_best_model_ae-lstm.h5\n",
      "4162/4162 [==============================] - 1s 193us/sample - loss: 0.1992 - val_loss: 0.1819\n",
      "Epoch 13/20\n",
      "3968/4162 [===========================>..] - ETA: 0s - loss: 0.1986\n",
      "Epoch 00013: val_loss improved from 0.18187 to 0.17984, saving model to ./data/case_3.317e-03_80_best_model_ae-lstm.h5\n",
      "4162/4162 [==============================] - 1s 189us/sample - loss: 0.1981 - val_loss: 0.1798\n",
      "Epoch 14/20\n",
      "3968/4162 [===========================>..] - ETA: 0s - loss: 0.1975\n",
      "Epoch 00014: val_loss improved from 0.17984 to 0.17900, saving model to ./data/case_3.317e-03_80_best_model_ae-lstm.h5\n",
      "4162/4162 [==============================] - 1s 191us/sample - loss: 0.1980 - val_loss: 0.1790\n",
      "Epoch 15/20\n",
      "3968/4162 [===========================>..] - ETA: 0s - loss: 0.1952\n",
      "Epoch 00015: val_loss did not improve from 0.17900\n",
      "4162/4162 [==============================] - 1s 175us/sample - loss: 0.1956 - val_loss: 0.1803\n",
      "Epoch 16/20\n",
      "3968/4162 [===========================>..] - ETA: 0s - loss: 0.1947\n",
      "Epoch 00016: val_loss did not improve from 0.17900\n",
      "4162/4162 [==============================] - 1s 172us/sample - loss: 0.1956 - val_loss: 0.1790\n",
      "Epoch 17/20\n",
      "3968/4162 [===========================>..] - ETA: 0s - loss: 0.1953\n",
      "Epoch 00017: val_loss improved from 0.17900 to 0.17831, saving model to ./data/case_3.317e-03_80_best_model_ae-lstm.h5\n",
      "4162/4162 [==============================] - 1s 191us/sample - loss: 0.1953 - val_loss: 0.1783\n",
      "Epoch 18/20\n",
      "3968/4162 [===========================>..] - ETA: 0s - loss: 0.1948\n",
      "Epoch 00018: val_loss improved from 0.17831 to 0.17644, saving model to ./data/case_3.317e-03_80_best_model_ae-lstm.h5\n",
      "4162/4162 [==============================] - 1s 193us/sample - loss: 0.1950 - val_loss: 0.1764\n",
      "Epoch 19/20\n",
      "3968/4162 [===========================>..] - ETA: 0s - loss: 0.1945\n",
      "Epoch 00019: val_loss did not improve from 0.17644\n",
      "4162/4162 [==============================] - 1s 173us/sample - loss: 0.1941 - val_loss: 0.1793\n",
      "Epoch 20/20\n",
      "3968/4162 [===========================>..] - ETA: 0s - loss: 0.1938\n",
      "Epoch 00020: val_loss did not improve from 0.17644\n",
      "4162/4162 [==============================] - 1s 172us/sample - loss: 0.1931 - val_loss: 0.1769\n",
      "\n",
      "| Iteration | lstm width | learning rate |   loss | objective | trainable | non trainable | run time (mins) | \n",
      "|-----------------------------------------------------------------------------------------------------------|\n",
      "|         1 |         80 |      0.003317 | 0.1769 |    0.5348 |     92728 |             0 |          0.3350 |\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.5347939402453705"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print_flag = 1\n",
    "train_and_fit_AE(lstm_width.to_unscaled(80),learning_rate.to_unscaled(0.003317))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, let's the define Bayesian Optimization environment using the our model function:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "optimizer = BayesianOptimization(\n",
    "    f=train_and_fit_AE,\n",
    "    pbounds=pbounds,\n",
    "    verbose=0, # verbose = 1 prints only when a maximum is observed, verbose = 0 is silent\n",
    "    random_state=1,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Main call to optimizer\n",
    "Run the optimizer to explore the parameter space. Each call to the method can take a few minutes. So the total time is almost 15-20 minutes. How can this be improved ? \n",
    "\n",
    "Suggestions for  experimentation (outside of current DLI): \n",
    "* **init_points** specifies the initial number of calls to the **lstm_autoencoder** using random parameters. This is used to get initial values to populate our search space. This can be done in parallel. \n",
    "* **n_iter** specifies the subsequent number of steps to leverage the prior results to search through new architectures using the most optimal possible parameters.  This is where we leverage Bayesian Optimization to optimize our model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "| Iteration | lstm width | learning rate |   loss | objective | trainable | non trainable | run time (mins) | \n",
      "|-----------------------------------------------------------------------------------------------------------|\n",
      "|         1 |         68 |      0.022723 | 0.4968 |    0.0072 |     69304 |             0 |          0.3109 |\n",
      "|         2 |         32 |      0.002481 | 0.1817 |    0.5168 |     19768 |             0 |          0.3327 |\n",
      "|         3 |         44 |      0.000816 | 0.1932 |    0.4740 |     32824 |             0 |          0.3375 |\n",
      "|         4 |         50 |      0.003120 | 0.1829 |    0.5124 |     40648 |             0 |          0.3391 |\n",
      "|         5 |         68 |      0.008686 | 0.1973 |    0.4590 |     69304 |             0 |          0.3261 |\n",
      "|         6 |         68 |      0.018866 | 0.5182 |    0.0047 |     69304 |             0 |          0.3005 |\n",
      "|         7 |         50 |      0.052426 | 0.5337 |    0.0034 |     40648 |             0 |          0.3286 |\n",
      "|         8 |         32 |      0.017448 | 0.2064 |    0.4267 |     19768 |             0 |          0.3380 |\n",
      "|         9 |         68 |      0.003292 | 0.1809 |    0.5197 |     69304 |             0 |          0.3270 |\n",
      "|        10 |         32 |      0.006380 | 0.1900 |    0.4859 |     19768 |             0 |          0.3301 |\n",
      "|        11 |         68 |      0.000690 | 0.2007 |    0.4468 |     69304 |             0 |          0.3465 |\n",
      "|        12 |         86 |      0.002958 | 0.1786 |    0.5285 |    105736 |             0 |          0.3282 |\n",
      "|        13 |         98 |      0.000892 | 0.1793 |    0.5256 |    134344 |             0 |          0.3347 |\n",
      "|        14 |        110 |      0.002463 | 0.1794 |    0.5252 |    166408 |             0 |          0.3442 |\n",
      "|        15 |        116 |      0.000603 | 0.1887 |    0.4904 |    183736 |             0 |          0.3354 |\n",
      "|        16 |        116 |      0.010096 | 0.5133 |    0.0051 |    183736 |             0 |          0.3200 |\n"
     ]
    }
   ],
   "source": [
    "print_flag = 0\n",
    "iter_history = []\n",
    "\n",
    "init_points = 8 \n",
    "num_steps = 8 \n",
    "\n",
    "optimizer.maximize(\n",
    "    init_points=init_points,\n",
    "    n_iter=num_steps,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br> \n",
    "\n",
    "### Example output from optimization \n",
    "\n",
    "<img src=\"./images/optresult.png\" alt=\"optresult\"/>\n",
    "\n",
    "<!-- ![image.png](attachment:image.png) -->"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br> \n",
    "\n",
    "### Processing the results\n",
    "Bayesian optimization has provided several options for improved architectures. Some of the architectures are similar while there are quite a few which are different. It is useful to sort the architectures and take a look at the best performing ones. The next few steps sort and plot the best architectures. \n",
    "#### Sort the architectures with loss \n",
    "<a name=\"a2\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "results_list = [cur_iter['objective'] for cur_iter in iter_history]\n",
    "\n",
    "idx_sorted = sorted(range(len(results_list)), key=results_list.__getitem__)\n",
    "min_res = results_list[idx_sorted[0]]\n",
    "max_res = results_list[idx_sorted[-1]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a name=\"a3\"></a>\n",
    "#### Plot the loss per epoch for the best three cases "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                    Best cases\n",
      "Model: \"model_14\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "input_15 (InputLayer)        [(None, 16, 21)]          0         \n",
      "_________________________________________________________________\n",
      "lstm_39 (LSTM)               (None, 86)                37152     \n",
      "_________________________________________________________________\n",
      "repeat_vector_13 (RepeatVect (None, 16, 86)            0         \n",
      "_________________________________________________________________\n",
      "lstm_40 (LSTM)               (None, 16, 86)            59512     \n",
      "_________________________________________________________________\n",
      "lstm_41 (LSTM)               (None, 16, 21)            9072      \n",
      "=================================================================\n",
      "Total params: 105,736\n",
      "Trainable params: 105,736\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "Model: \"model_15\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "input_16 (InputLayer)        [(None, 16, 21)]          0         \n",
      "_________________________________________________________________\n",
      "lstm_42 (LSTM)               (None, 98)                47040     \n",
      "_________________________________________________________________\n",
      "repeat_vector_14 (RepeatVect (None, 16, 98)            0         \n",
      "_________________________________________________________________\n",
      "lstm_43 (LSTM)               (None, 16, 98)            77224     \n",
      "_________________________________________________________________\n",
      "lstm_44 (LSTM)               (None, 16, 21)            10080     \n",
      "=================================================================\n",
      "Total params: 134,344\n",
      "Trainable params: 134,344\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "Model: \"model_16\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "input_17 (InputLayer)        [(None, 16, 21)]          0         \n",
      "_________________________________________________________________\n",
      "lstm_45 (LSTM)               (None, 110)               58080     \n",
      "_________________________________________________________________\n",
      "repeat_vector_15 (RepeatVect (None, 16, 110)           0         \n",
      "_________________________________________________________________\n",
      "lstm_46 (LSTM)               (None, 16, 110)           97240     \n",
      "_________________________________________________________________\n",
      "lstm_47 (LSTM)               (None, 16, 21)            11088     \n",
      "=================================================================\n",
      "Total params: 166,408\n",
      "Trainable params: 166,408\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_model_loss([idx_sorted[-1-i] for i in range(3)],\"Best cases\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br> \n",
    "**An example loss curve for a good architecture**\n",
    "![Loss example 1](./images/loss_crv_ex1.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a name=\"a4\"></a>\n",
    "#### Plot the loss per epoch for the worst three cases "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                    Worst cases\n",
      "Model: \"model_9\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "input_10 (InputLayer)        [(None, 16, 21)]          0         \n",
      "_________________________________________________________________\n",
      "lstm_24 (LSTM)               (None, 50)                14400     \n",
      "_________________________________________________________________\n",
      "repeat_vector_8 (RepeatVecto (None, 16, 50)            0         \n",
      "_________________________________________________________________\n",
      "lstm_25 (LSTM)               (None, 16, 50)            20200     \n",
      "_________________________________________________________________\n",
      "lstm_26 (LSTM)               (None, 16, 21)            6048      \n",
      "=================================================================\n",
      "Total params: 40,648\n",
      "Trainable params: 40,648\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "Model: \"model_8\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "input_9 (InputLayer)         [(None, 16, 21)]          0         \n",
      "_________________________________________________________________\n",
      "lstm_21 (LSTM)               (None, 68)                24480     \n",
      "_________________________________________________________________\n",
      "repeat_vector_7 (RepeatVecto (None, 16, 68)            0         \n",
      "_________________________________________________________________\n",
      "lstm_22 (LSTM)               (None, 16, 68)            37264     \n",
      "_________________________________________________________________\n",
      "lstm_23 (LSTM)               (None, 16, 21)            7560      \n",
      "=================================================================\n",
      "Total params: 69,304\n",
      "Trainable params: 69,304\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "Model: \"model_18\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "input_19 (InputLayer)        [(None, 16, 21)]          0         \n",
      "_________________________________________________________________\n",
      "lstm_51 (LSTM)               (None, 116)               64032     \n",
      "_________________________________________________________________\n",
      "repeat_vector_17 (RepeatVect (None, 16, 116)           0         \n",
      "_________________________________________________________________\n",
      "lstm_52 (LSTM)               (None, 16, 116)           108112    \n",
      "_________________________________________________________________\n",
      "lstm_53 (LSTM)               (None, 16, 21)            11592     \n",
      "=================================================================\n",
      "Total params: 183,736\n",
      "Trainable params: 183,736\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_model_loss([idx_sorted[i] for i in range(3)],\"Worst cases\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>\n",
    "\n",
    "**A typical loss curve for a bad architecture**\n",
    "![Loss example 1](./images/loss_crv_ex2.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a name=\"a5\"></a>\n",
    "#### Plot different architectures in a 2D space representating the different architectures with the minimum loss as the size "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_loss_map(results_list)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>\n",
    "\n",
    "**An example map for the loss with the inputs for the lstm autoencoder** \n",
    "![Loss example 1](./images/loss_map.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a name=\"e5\"></a>\n",
    "### Exercise 4: Update code to use specified validation data\n",
    "\n",
    "The test data can also be specified as a separate dataset rather than being specified as a fraction of the dataset. This is useful especially in situations where it is important to have a certain structure in the test data representative of the training dataset, rather than a randomly sampled section of the main dataset. **train_and_fit_AE** method needs to be specified. \n",
    "\n",
    "As an exercise, please update the <<< TO DO >>> below with the appropriate validation data using the test data set as the features and label.\n",
    "\n",
    "<a name=\"q1\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "import statistics\n",
    "from tensorflow.keras.callbacks import ModelCheckpoint, EarlyStopping\n",
    "\n",
    "print_flag = 0 \n",
    "iter_history = []\n",
    "\n",
    "def train_and_fit_AE(param1, param2):\n",
    "    \n",
    "    time_start = time.time()\n",
    "    autoencoder, filename, params  = lstm_autoencoder(data_shape, [param1, param2])\n",
    "   \n",
    "    # checkpoint\n",
    "    checkpoint = ModelCheckpoint(data_dir+filename, monitor='val_loss', \n",
    "                                 verbose=print_flag, save_best_only=True, mode='min')\n",
    "    es = EarlyStopping(patience=15, verbose=print_flag)\n",
    "\n",
    "    optimizer = Adam(lr=params['learning_rate'])\n",
    "    autoencoder.compile(optimizer=optimizer, loss='mae')\n",
    "\n",
    "    history = autoencoder.fit(x_train, x_train, batch_size=params['batch_size'], epochs=params['num_epochs'], \n",
    "                              validation_data=(x_test, x_test), verbose=print_flag, callbacks=[es, checkpoint])\n",
    "    \n",
    "    loss = history.history[\"val_loss\"][-1]\n",
    "    if np.isnan(loss) : loss = 1e8\n",
    "    result = np.exp(-20*loss**2) \n",
    "\n",
    "    print_history(len(iter_history), params, loss, result, \n",
    "                  count_params(autoencoder,\"train\"), \n",
    "                  count_params(autoencoder,\"non_train\"),\n",
    "                  (time.time()-time_start)/60.0)\n",
    "    iter_history.append({'objective':result, 'params': params, 'history':history.history, \"model\" : autoencoder})\n",
    "        \n",
    "    return result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "| Iteration | lstm width | learning rate |   loss | objective | trainable | non trainable | run time (mins) | \n",
      "|-----------------------------------------------------------------------------------------------------------|\n",
      "|         1 |         68 |      0.022723 | 0.5225 |    0.0042 |     69304 |             0 |          0.3797 |\n",
      "|         2 |         32 |      0.002481 | 0.2142 |    0.3996 |     19768 |             0 |          0.3635 |\n",
      "|         3 |         44 |      0.000816 | 0.2108 |    0.4113 |     32824 |             0 |          0.3703 |\n",
      "|         4 |         50 |      0.003120 | 0.2134 |    0.4023 |     40648 |             0 |          0.3632 |\n",
      "|         5 |         68 |      0.008686 | 0.2239 |    0.3669 |     69304 |             0 |          0.4071 |\n",
      "|         6 |         68 |      0.018866 | 0.5075 |    0.0058 |     69304 |             0 |          0.3472 |\n",
      "|         7 |         50 |      0.052426 | 0.5235 |    0.0042 |     40648 |             0 |          0.3381 |\n",
      "|         8 |         32 |      0.017448 | 0.5112 |    0.0054 |     19768 |             0 |          0.3800 |\n",
      "|         9 |         68 |      0.003076 | 0.2107 |    0.4116 |     69304 |             0 |          0.3582 |\n",
      "|        10 |         62 |      0.000693 | 0.2156 |    0.3947 |     58888 |             0 |          0.3644 |\n",
      "|        11 |         86 |      0.001175 | 0.2132 |    0.4029 |    105736 |             0 |          0.3631 |\n",
      "|        12 |         92 |      0.004267 | 0.2114 |    0.4090 |    119608 |             0 |          0.3882 |\n",
      "|        13 |        110 |      0.002021 | 0.2087 |    0.4184 |    166408 |             0 |          0.3577 |\n",
      "|        14 |        110 |      0.000500 | 0.2219 |    0.3737 |    166408 |             0 |          0.3695 |\n",
      "|        15 |        116 |      0.008993 | 0.3075 |    0.1509 |    183736 |             0 |          0.3552 |\n",
      "|        16 |         32 |      0.000500 | 0.2252 |    0.3626 |     19768 |             0 |          0.4009 |\n"
     ]
    }
   ],
   "source": [
    "optimizer = BayesianOptimization(\n",
    "    f=train_and_fit_AE,\n",
    "    pbounds=pbounds,\n",
    "    verbose=0, # verbose = 1 prints only when a maximum is observed, verbose = 0 is silent\n",
    "    random_state=1,\n",
    ")\n",
    "\n",
    "print_flag = 0\n",
    "iter_history = []\n",
    "\n",
    "init_points = 8 \n",
    "num_steps = 8 \n",
    "\n",
    "optimizer.maximize(\n",
    "    init_points=init_points,\n",
    "    n_iter=num_steps,\n",
    ")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a name=\"e6\"></a>\n",
    "### Exercise 5: Create HPO result plots \n",
    "Now the results of this new hyperparameter optimization can be analyzed in a similar way as the previous particular dataset.\n",
    "* sort the architectures \n",
    "* loss vs epoch for best three architectures \n",
    "* loss vs epoch for worst three architectures \n",
    "* map of training and test loss with key parameters of the architectures - learning rate and lstm width \n",
    "\n",
    "The code required below is used from the plotting above. <br>\n",
    "Use the hyperlinks to go to the appropriate session and scroll down to this section to complete the exercise. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Sort the architectures with loss   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "results_list = [cur_iter['objective'] for cur_iter in iter_history]\n",
    "\n",
    "idx_sorted = sorted(range(len(results_list)), key=results_list.__getitem__)\n",
    "min_res = results_list[idx_sorted[0]]\n",
    "max_res = results_list[idx_sorted[-1]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Plot the loss per epoch for the best three cases**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                    Best cases\n",
      "Model: \"model_31\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "input_32 (InputLayer)        [(None, 16, 21)]          0         \n",
      "_________________________________________________________________\n",
      "lstm_90 (LSTM)               (None, 110)               58080     \n",
      "_________________________________________________________________\n",
      "repeat_vector_30 (RepeatVect (None, 16, 110)           0         \n",
      "_________________________________________________________________\n",
      "lstm_91 (LSTM)               (None, 16, 110)           97240     \n",
      "_________________________________________________________________\n",
      "lstm_92 (LSTM)               (None, 16, 21)            11088     \n",
      "=================================================================\n",
      "Total params: 166,408\n",
      "Trainable params: 166,408\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "Model: \"model_27\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "input_28 (InputLayer)        [(None, 16, 21)]          0         \n",
      "_________________________________________________________________\n",
      "lstm_78 (LSTM)               (None, 68)                24480     \n",
      "_________________________________________________________________\n",
      "repeat_vector_26 (RepeatVect (None, 16, 68)            0         \n",
      "_________________________________________________________________\n",
      "lstm_79 (LSTM)               (None, 16, 68)            37264     \n",
      "_________________________________________________________________\n",
      "lstm_80 (LSTM)               (None, 16, 21)            7560      \n",
      "=================================================================\n",
      "Total params: 69,304\n",
      "Trainable params: 69,304\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "Model: \"model_21\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "input_22 (InputLayer)        [(None, 16, 21)]          0         \n",
      "_________________________________________________________________\n",
      "lstm_60 (LSTM)               (None, 44)                11616     \n",
      "_________________________________________________________________\n",
      "repeat_vector_20 (RepeatVect (None, 16, 44)            0         \n",
      "_________________________________________________________________\n",
      "lstm_61 (LSTM)               (None, 16, 44)            15664     \n",
      "_________________________________________________________________\n",
      "lstm_62 (LSTM)               (None, 16, 21)            5544      \n",
      "=================================================================\n",
      "Total params: 32,824\n",
      "Trainable params: 32,824\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_model_loss([idx_sorted[-1-i] for i in range(3)],\"Best cases\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Plot the loss per epoch for the worst three cases**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                    Worst cases\n",
      "Model: \"model_25\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "input_26 (InputLayer)        [(None, 16, 21)]          0         \n",
      "_________________________________________________________________\n",
      "lstm_72 (LSTM)               (None, 50)                14400     \n",
      "_________________________________________________________________\n",
      "repeat_vector_24 (RepeatVect (None, 16, 50)            0         \n",
      "_________________________________________________________________\n",
      "lstm_73 (LSTM)               (None, 16, 50)            20200     \n",
      "_________________________________________________________________\n",
      "lstm_74 (LSTM)               (None, 16, 21)            6048      \n",
      "=================================================================\n",
      "Total params: 40,648\n",
      "Trainable params: 40,648\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "Model: \"model_19\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "input_20 (InputLayer)        [(None, 16, 21)]          0         \n",
      "_________________________________________________________________\n",
      "lstm_54 (LSTM)               (None, 68)                24480     \n",
      "_________________________________________________________________\n",
      "repeat_vector_18 (RepeatVect (None, 16, 68)            0         \n",
      "_________________________________________________________________\n",
      "lstm_55 (LSTM)               (None, 16, 68)            37264     \n",
      "_________________________________________________________________\n",
      "lstm_56 (LSTM)               (None, 16, 21)            7560      \n",
      "=================================================================\n",
      "Total params: 69,304\n",
      "Trainable params: 69,304\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "Model: \"model_26\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "input_27 (InputLayer)        [(None, 16, 21)]          0         \n",
      "_________________________________________________________________\n",
      "lstm_75 (LSTM)               (None, 32)                6912      \n",
      "_________________________________________________________________\n",
      "repeat_vector_25 (RepeatVect (None, 16, 32)            0         \n",
      "_________________________________________________________________\n",
      "lstm_76 (LSTM)               (None, 16, 32)            8320      \n",
      "_________________________________________________________________\n",
      "lstm_77 (LSTM)               (None, 16, 21)            4536      \n",
      "=================================================================\n",
      "Total params: 19,768\n",
      "Trainable params: 19,768\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    },
    {
     "data": {
      "image/png": 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nfXIF8E5J2/8YKL3297+BL0REqVcR7HNBQx03Gb0UGK3CO467wS9Jd6wMjohW0r/Ds0in+P9TmTmfwfwncLWkIZKmAB+syLaUVK/d6/Itt5OUTASuYec7Zr4L/I1Ss9pHka4BfafErB8AVku6UtJgpXdqHy3pDTl9GGnfrc3z/fNuW6kOVNk/R7Hj9bNlDSMdyFaQ/pF+qSK9cv92ti0605V98D3gHZJOU3pvx98B/xkRawAkXShpqFLT9meSWs69I6cNJ1X3/L+IqHw51SrS2cPU3LUHvBOo3jT+XcCr8+2//SW9m1QF9eNCnvdJmiJpSC7nLVFxO2xEvEg6uH9V0vBc7sMlvSln+TZwhaQT8nf4CO14P3qHv7N8JnoL6cVcB5CuoRHpxV7/Anw9n3UgabzKv2zsu8BH8rqNItWKfKdG3ntJx69PKN1afWke3/5q347mdQMpYLbvkxmkWoK3tZc5z+e6iJhRsuz73jUN0oXEn5IuqrXf/XJqId9M0o95JTvunrqxkP4WCvXBpKqKACZ0sOwXKdQ/Ay3sevGqeM1lLOnHscvdUzn9kjzPlaSX/JzOrtdiFlLjlavdvF0vJ/37W0/61/ZP5DttcvrAvE1Xk36El3cwr53WI2//75Oqv14hvd71LTntjaQzjbWkQPx3dH7dp+Y2osrdUx3kHUv6cbV/f75Mfv1plXU6lIq6e1I11+2kKoTnSEGnuP+PJNX9ryRVeXS4LUrsow73Qd6GpxWG30O67reOwl1JOe1XpACwmnSR9aJC2gfZ+c6+9m5Sme1SJc+ppCrHVfmz+Du9lx13T60mXWgeU23epLO5b5HuiFtF+rNWLPclpNfvriU1C39cHn8eO5rxv6LGvjwtj7uuouyDSH8Gnsnle4yu3fF2ed5Xq0m3uQ4spP0E+KvC8HF5+2wAHmwvf5l5VeS7mp2Pdf87r1txX67trOxue8qsE5K+DBwUEZVnhNYLcpXaU6SDuw9gPWyfq54y21OSjlJ6054kTSM9gzGrt8tl2x1Nqg1wwOgFdQ0aks6S9ISkBZIq60Db68tXSZqXu88X0hZK+n0e31LPcppVGEa6rrGOdBPFV6ny5LP1PKUmUm4gPQVtvaBu1VP5tsMnSfeMt5Lqhi+OiPmFPKcDV0TE26tMvxBojojKuyXMzKyX1PNMYxqwICKeiYjNpCdsz6vj8szMrM7q+XDfeHZ+OKeV9ORrpZMlPUS6b/uKiGi//ziA2UrvL/7niLih2kIkTSe150NTU9MJRx11VHeV38xsnzd37tyXImJs2fz1DBrVHgmtrAt7kNQOzFpJ55AexDsyp50SEYvzfdA/k/R4RNy3ywxTMLkBoLm5OVpafPnDzKwsSWVapNiuntVTrez8ROcE0tnEdhGxOiLW5v67gEZJY/Lw4vy5jHTnyrQ6ltXMzEqoZ9CYAxwpaXJu+/0i8pOl7SQd1P54fL61sR+wQlKTpGF5fBOpeeBH6lhWMzMroW7VU5EaLbuU1OxAAzAzIh6VdElOn0FqwvnPJW0lPe14UUSEpHHArBxP+pPa//9pvcpqZmbl7FNPhPuahplZ10iaGxHNZfP7iXAzMyvNQcPMzEpz0DAzs9IcNMzMrDQHDTMzK81Bw8zMSqtnMyL7h23b4Ikn4KGH4PWvh9e9rvNpzOopArZuhc2bU7dp047+fegW+z3Wrx+MHJm6hobeLs1ew0Gjq154AR54YEc3Zw6sWbMjfcoUeNe7Uvfa1/ZeOa33RMBzz8HDD+/oVqzY83m2te0cACq7Ypp1zYgRMGoUHHBA1z6HDgVVa2Zv3+WH+zqyahW0tOwcJBbn5rMaG+HYY2HatNS9/vXwu9/BD34A992XfuSvf/2OAPLqV3dfuazvWLcOHnkknWk+/PCOz9Wrd+Q5/HA46KA9P7j07w8DBqRu4MAd/cWu1vj2rp9rpLdra4OVK+GVV+Dll2t/btlSex5S3wga48btODZ1UVcf7nPQaLdpU/rBF88gHn98R/qrX70jQEyblgLGoEHV5/Xii3DrrfDDH8Kvf50CyLHH7gggRxwBwLbYxpK1S3jmlWd49pVnWbxmMSdOOJHTJp1GQz+fLvcp7WcPlcFhwYIdVT7DhsExx6Tu2GPT59FHp/G2d4pIfwxqBZTin4PeNHQoXHnlbk3qoNHVoLF5M5x2GvzP/+z4RzFuHJx44o4A0dycTkV3w6qn57Pmpu8w6Lb/YsyDKQgtOGQ4tx8zgG8fsZrHR+xalXBg04H80VF/xIVTLuRNh76J/v1ci9gjNm6ERYvg+edT197/xBO7nj0cccTOweHYY+GQQ+ryT75tWxtrNq9hc9tmIoIgdvuzqPjbL6ZVHhM6SutuqvKvXVXfssBO67Uttu2yrtXGReTxeZ0a1ED/fv1p6Jc/awzXytOghqpl3ps4aOzOmcb73gfjx+8IEhMmdOmUMyL49fO/5pFlj/Dsymd5duWz288eXtn4yvZ8E1bB+58cxEXzGzjm2XUALHvtJF5+x1sZcNF7GHnUVO555h5ueewWfvzkj1m/ZT1jhozhgqMu4MIpF3LGoWfQ2NDY9fWzdMPC0qU7AkIxKLR3y5fvOt1BB6XqpWJwOPro9M+uhE1bN/HCmhd4af1LrNm0htWbVrN602rWbE7928dtXl0zfd2Wdd28Maw7Deo/aKducP/BOw83FoYbdh03oGFAzcBYVtOAJv7iDX+xW9M6aPRgg4Ubtmzgxodv5NrfXcv85enV5wMaBjB55GQmj5rMYSMPY/KoyUweOZnDRqX+kYNGpomfew5uuSVVYT3wQBp3wgnpIAW0RRvL1i3nxTUvsmzdUrZua6OxoZGDhh7EwUMPZsyQ0fST66c7tXZtCgitrbvWTQ8dms4OJk6ESZN2dO3D48enawQ1tG1rY8naJTy/6nkWrV7EolWL0mfuf37V8yxdt7TD4jX2a2T4wOEMHzicYQOH7egfsHP/sIHDGNgwEEkI7dFnUfFgVUyrPIh1lNZdKs+EoPaZTRDb16ef+u2yjtXGiTw+90P6nbVta6Mt2ti6bStt2/JnyeEtbVvY1LaJDVs2sHHrRja2bdzRn7sNWyuGc/qGrRvYFtu6ZduNaxrHkiuW7Na0Dho9EDSWrF3C9XOu51st3+Kl9S9x3EHHcdlJl/HmyW/m4GEHd/1g/uyz8KMfwZ13pvrTCtsiWL1pFS9vfIVVG1fStm0bDf0aGDloJKMGjWL4wOH068VT5LbYxpa2LWzdtoUt27awpS39qPr3609jv0YaG/rTv18jjf3693ygGzJk50BQDAwjRmw/o4wINrdtZv2W9azbsi59bk6fL61/iUWrF+0SHBavWczWbVt3WtzQAUOZOHwiE0dMTJ+5/8CmA7cHgWJQGNi/dlCyfd+Wti1sbuueu92aBjTt1nQOGnUMGg8teYiv3/91vv/I99nStoV3vOYdXH7S5bzxkDf2WL3mpq2b+NkzP+OW+bdw+xO3s3LjSoYPHM65rzmXC197IW874m0M6l/jAn0XbNy6kaVrl7J03VKWrF3CkrVLWLo296/bebgr1SfDBgxj3NBxHNh0IOOaxqVuaPo8sOnA7f3jho5j2IBhbG7bzIatG3b6d9aV/vVb1u8cCAoBoTI4tEVbh2Uf0DCACcMnMGnEpJ0CQvvnpBGTGDFwxF5fx237FweNbg4a22Ibdz11F1+//+v8/NmfM6RxCB+e+mE+ceInOHL0kZ3PoI42t21O10Dm38JtT9zGyxteZmDDQIY0Dul02o4ObFu3bWX1pup3hRww+AAOGnoQBw09iHFN46r35wP+ig0rtgeepWuXsmzdstS/LvfntBXrV1StmthTAxsGMrhxME2NTTQNaGJI4xCaGvPngKYd/YVx1fKMGjyKicMnMrZprKsEbZ/joNFNQWPd5nV896Hvcu3vruXJFU8yYfgEPj7t4/zZ8X/GqMG7dydVPW1p28K9C+9l9tOz2dS2qWa+Mvu7n/pxYNOB2wNAezA4sOlABjQM6M5iAylIvbT+pV0CzOpNq3e6mNh+gbGyv/3iY7F/YP+BPsCbleCgsYdB44XVL3DdnOuY0TKDVza+QvOrmrn8pMu5cMqFvnPJzPY5XQ0adX0AQNJZwDdI7wj/dkRcU5F+OnA78Gwe9Z8R8Xdlpu1uD774IF+//+vc/MjNbIttnH/U+XzqpE9xysRTXEdtZpbVLWhIagCuA94KtAJzJN0REfMrsv4qIt6+m9PusdWbVvOO77+D+567j6EDhnLpGy7lEyd+gsmjJnf3oszM9nr1PNOYBiyIiGcAJN0MnAeUOfDvybRdMmzAMMY1jeOrZ36Vjxz3EUYMGtHdizAz22fUM2iMBxYVhluBE6vkO1nSQ8Bi4IqIeLQL0+4xSfzwj39Yj1mbme1z6hk0ql0IqLzq/iBwSESslXQOcBtwZMlp00Kk6cB0gEmTJu1+ac3MrFP1vCexFZhYGJ5AOpvYLiJWR8Ta3H8X0ChpTJlpC/O4ISKaI6J57Nix3Vl+MzOrUM+gMQc4UtJkSQOAi4A7ihkkHaR8a5Kkabk8K8pMa2ZmPa9u1VMRsVXSpcDdpNtmZ0bEo5IuyekzgAuBP5e0FdgAXBTpwZGq09arrGZmVo4f7jMz24919eE+t7NgZmalOWiYmVlpDhpmZlaag4aZmZXmoGFmZqU5aJiZWWkOGmZmVpqDhpmZleagYWZmpTlomJlZaQ4aZmZWmoOGmZmV5qBhZmalOWiYmVlpDhpmZlaag4aZmZXmoGFmZqU5aJiZWWkOGmZmVlpdg4aksyQ9IWmBpKs6yPcGSW2SLiyMWyjp95LmSfKLv83M+oD+9ZqxpAbgOuCtQCswR9IdETG/Sr4vA3dXmc0ZEfFSvcpoZmZdU88zjWnAgoh4JiI2AzcD51XJ93HgVmBZHctiZmbdoJ5BYzywqDDcmsdtJ2k8cAEwo8r0AcyWNFfS9FoLkTRdUoukluXLl3dDsc3MrJZ6Bg1VGRcVw9cCV0ZEW5W8p0TE8cDZwMckvbHaQiLihohojojmsWPH7lmJzcysQ3W7pkE6s5hYGJ4ALK7I0wzcLAlgDHCOpK0RcVtELAaIiGWSZpGqu+6rY3nNzKwT9TzTmAMcKWmypAHARcAdxQwRMTkiDo2IQ4FbgL+IiNskNUkaBiCpCTgTeKSOZTUzsxLqdqYREVslXUq6K6oBmBkRj0q6JKdXu47RbhwwK5+B9Aduioif1qusZmZWjiIqLzPsvZqbm6OlxY90mJmVJWluRDSXze8nws3MrDQHDTMzK81Bw8zMSnPQMDOz0hw0zMysNAcNMzMrzUHDzMxKc9AwM7PSHDTMzKw0Bw0zMyvNQcPMzEpz0DAzs9IcNMzMrDQHDTMzK81Bw8zMSnPQMDOz0hw0zMysNAcNMzMrra5BQ9JZkp6QtEDSVR3ke4OkNkkXdnVaMzPrOXULGpIagOuAs4EpwMWSptTI92Xg7q5Oa2ZmPaueZxrTgAUR8UxEbAZuBs6rku/jwK3Ast2Y1szMelA9g8Z4YFFhuDWP207SeOACYEZXpy3MY7qkFkkty5cv3+NCm5lZbfUMGqoyLiqGrwWujIi23Zg2jYy4ISKaI6J57Nixu1FMMzMrq38d590KTCwMTwAWV+RpBm6WBDAGOEfS1pLTmplZD6tn0JgDHClpMvACcBHwnmKGiJjc3i/pO8CPI+I2Sf07m9bMzHpe3YJGRGyVdCnprqgGYGZEPCrpkpxeeR2j02nrVVYzMytHEVUvFeyVmpubo6WlpbeLYWa215A0NyKay+b3E+FmZlaag4aZmZXmoGFmZqU5aJiZWWkOGmZmVpqDhpmZleagYWZmpTlomJlZaQ4aZmZWmoOGmZmV5qBhZmalOWiYmVlpDhpmZlZaqaAhqUlSv9z/aknnSmqsb9HMzKyvKXumcR8wKL/T+x7gT4Dv1KtQZmbWN5UNGoqI9cAfAf8UERcAU+pXLDMz64tKBw1JJwPvBe7M4+r5qlgzM+uDygaNy4DPArPyK1sPA35Rv2KZmVlfVCpoRMQvI+LciPhyviD+UkR8orPpJJ0l6QlJCyRdVSX9PEkPS5onqUXSqYW0hZJ+357WpbUyM7O6KHv31E2ShktqAuYDTyRgMpIAABKpSURBVEj6dCfTNADXAWeTrn9cLKnyOsg9wLERMRX4MPDtivQzImJqV95fa2Zm9VO2empKRKwGzgfuAiYB7+9kmmnAgoh4JiI2AzcD5xUzRMTaiIg82AQEZmbWZ5UNGo35uYzzgdsjYgudH+DHA4sKw6153E4kXSDpcdIF9g8XkgKYLWmupOm1FiJpeq7aalm+fHnJ1TEzs91RNmj8M7CQdDZwn6RDgNWdTKMq43YJNBExKyKOIgWkLxSSTomI40nVWx+T9MZqC4mIGyKiOSKax44d2/mamJnZbit7IfybETE+Is6J5DngjE4mawUmFoYnAIs7WMZ9wOGSxuThxflzGTCLVN1lZma9qOyF8BGSvtZeDSTpq6Szjo7MAY6UNFnSAOAi4I6K+R4hSbn/eGAAsCI3WzIsj28CzgQe6dKamZlZtyv7gN5M0kH7XXn4/cC/kZ4Qryoitkq6FLgbaABm5mc8LsnpM4B3Ah+QtAXYALw7IkLSOGBWjif9gZsi4qddXjszM+tW2nHzUgeZpHn5ttgOx/W25ubmaGnxIx1mZmVJmtuVxxrKXgjfUPHg3SmkMwMzM9uPlK2eugT4rqQRefgV4IP1KZKZmfVVpYJGRDwEHCtpeB5eLeky4OF6Fs7MzPqWLr25LyJW5yfDAS6vQ3nMzKwP25PXvVZ7eM/MzPZhexI03E6Umdl+psNrGpLWUD04CBhclxKZmVmf1WHQiIhhPVUQMzPr+/akesrMzPYzDhpmZlaag4aZmZXmoGFmZqU5aJiZWWkOGmZmVpqDhpmZleagYWZmpTlomJlZaQ4aZmZWWl2DhqSzJD0haYGkq6qknyfpYUnzJLVUvB2ww2nNzKzn1S1oSGoArgPOBqYAF0uaUpHtHuDY/K7xDwPf7sK0ZmbWw+p5pjENWBARz0TEZuBm4LxihohYGxHtreg2saNF3U6nNTOznlfPoDEeWFQYbs3jdiLpAkmPA3eSzjZKT5unn56rtlqWL1/eLQU3M7Pq6hk0qr3Zb5d3c0TErIg4Cjgf+EJXps3T3xARzRHRPHbs2N0urJmZda6eQaMVmFgYngAsrpU5Iu4DDpc0pqvTmplZz6hn0JgDHClpsqQBwEXAHcUMko6QpNx/PDAAWFFmWjMz63kdvrlvT0TEVkmXAncDDcDMiHhU0iU5fQbwTuADkrYAG4B35wvjVaetV1nNzKwc7bh5ae/X3NwcLS0tvV0MM7O9hqS5EdFcNr+fCDczs9IcNMzMrDQHDTMzK81Bw8zMSnPQMDOz0hw0zMysNAcNMzMrzUHDzMxKc9AwM7PSHDTMzKw0Bw0zMyvNQcPMzEpz0DAzs9IcNMzMrDQHDTMzK81Bw8zMSnPQMDOz0hw0zMystLoGDUlnSXpC0gJJV1VJf6+kh3P3G0nHFtIWSvq9pHmS/A5XM7M+oH+9ZiypAbgOeCvQCsyRdEdEzC9kexZ4U0S8Iuls4AbgxEL6GRHxUr3KaGZmXVPPM41pwIKIeCYiNgM3A+cVM0TEbyLilTx4PzChjuUxM7M9VM+gMR5YVBhuzeNq+Qjwk8JwALMlzZU0vdZEkqZLapHUsnz58j0qsJmZdaxu1VOAqoyLqhmlM0hB49TC6FMiYrGkA4GfSXo8Iu7bZYYRN5CqtWhubq46fzMz6x71PNNoBSYWhicAiyszSToG+DZwXkSsaB8fEYvz5zJgFqm6y8zMelE9g8Yc4EhJkyUNAC4C7ihmkDQJ+E/g/RHxZGF8k6Rh7f3AmcAjdSyrmZmVULfqqYjYKulS4G6gAZgZEY9KuiSnzwA+D4wGrpcEsDUimoFxwKw8rj9wU0T8tF5lNTOzchSx71wGaG5ujpYWP9JhZlaWpLn5z3opfiLczMxKc9AwM7PSHDTMzKw0Bw0zMyvNQcPMzEpz0DAzs9IcNMzMrDQHDTMzK81Bw8zMSnPQMDOz0hw0zMystHq+T8PMrM/bsmULra2tbNy4sbeLUleDBg1iwoQJNDY27tF8HDTMbL/W2trKsGHDOPTQQ8kta+9zIoIVK1bQ2trK5MmT92herp4ys/3axo0bGT169D4bMAAkMXr06G45m3LQMLP93r4cMNp11zo6aJiZWWkOGmZmvWjlypVcf/31XZ7unHPOYeXKlXUoUcccNMzMelGtoNHW1tbhdHfddRcjR46sV7FqquvdU5LOAr5Bekf4tyPimor09wJX5sG1wJ9HxENlpjUz626X/fQy5i2Z163znHrQVK4969qa6VdddRVPP/00U6dOpbGxkaFDh3LwwQczb9485s+fz/nnn8+iRYvYuHEjn/zkJ5k+fToAhx56KC0tLaxdu5azzz6bU089ld/85jeMHz+e22+/ncGDB3frerSr25mGpAbgOuBsYApwsaQpFdmeBd4UEccAXwBu6MK0ZmZ7vWuuuYbDDz+cefPm8ZWvfIUHHniAL37xi8yfPx+AmTNnMnfuXFpaWvjmN7/JihUrdpnHU089xcc+9jEeffRRRo4cya233lq38tbzTGMasCAingGQdDNwHjC/PUNE/KaQ/35gQtlpzcy6W0dnBD1l2rRpOz1L8c1vfpNZs2YBsGjRIp566ilGjx690zSTJ09m6tSpAJxwwgksXLiwbuWr5zWN8cCiwnBrHlfLR4Cf7Oa0Zmb7hKampu399957L//93//Nb3/7Wx566CGOO+64qs9aDBw4cHt/Q0MDW7durVv56nmmUe2m4KiaUTqDFDRO3Y1ppwPTASZNmtT1UpqZ9aJhw4axZs2aqmmrVq1i1KhRDBkyhMcff5z777+/h0u3q3oGjVZgYmF4ArC4MpOkY4BvA2dHxIquTAsQETeQr4U0NzdXDSxmZn3V6NGjOeWUUzj66KMZPHgw48aN25521llnMWPGDI455hhe85rXcNJJJ/ViSRNF1Oc4K6k/8CTwZuAFYA7wnoh4tJBnEvBz4APF6xtlpq2mubk5WlpauntVzGwf9thjj/Ha1762t4vRI6qtq6S5EdFcdh51O9OIiK2SLgXuJt02OzMiHpV0SU6fAXweGA1cnx9x3xoRzbWmrVdZzcysnLo+pxERdwF3VYybUej/U+BPy05rZma9y0+Em5lZaQ4aZmZWmoOGmZmV5qBhZmalOWiYmfWi3W0aHeDaa69l/fr13VyijjlomJn1or0taNT1llszs73KZZfBvO5tGp2pU+Hack2jv/Wtb+XAAw/khz/8IZs2beKCCy7gb//2b1m3bh3vete7aG1tpa2tjc997nMsXbqUxYsXc8YZZzBmzBh+8YtfdG+5a3DQMDPrRddccw2PPPII8+bNY/bs2dxyyy088MADRATnnnsu9913H8uXL+dVr3oVd955J5DapBoxYgRf+9rX+MUvfsGYMWN6rLwOGmZm7To4I+gJs2fPZvbs2Rx33HEArF27lqeeeorTTjuNK664giuvvJK3v/3tnHbaab1WRgcNM7M+IiL47Gc/y0c/+tFd0ubOnctdd93FZz/7Wc4880w+//nP90IJfSHczKxXFZtGf9vb3sbMmTNZu3YtAC+88ALLli1j8eLFDBkyhPe9731cccUVPPjgg7tM21N8pmFm1ouKTaOfffbZvOc97+Hkk08GYOjQodx4440sWLCAT3/60/Tr14/Gxka+9a1vATB9+nTOPvtsDj744B67EF63ptF7g5tGN7OuctPoXWsa3dVTZmZWmoOGmZmV5qBhZvu9famavpbuWkcHDTPbrw0aNIgVK1bs04EjIlixYgWDBg3a43n57ikz269NmDCB1tZWli9f3ttFqatBgwYxYcKEPZ6Pg4aZ7dcaGxuZPHlybxdjr1HX6ilJZ0l6QtICSVdVST9K0m8lbZJ0RUXaQkm/lzRPku+jNTPrA+p2piGpAbgOeCvQCsyRdEdEzC9kexn4BHB+jdmcEREv1auMZmbWNfU805gGLIiIZyJiM3AzcF4xQ0Qsi4g5wJY6lsPMzLpJPa9pjAcWFYZbgRO7MH0AsyUF8M8RcUO1TJKmA9Pz4FpJT+xOYYExwN5wVuNydr+9pawuZ/faW8oJ9S3rIV3JXM+goSrjunJP2ykRsVjSgcDPJD0eEfftMsMUTKoGlK6Q1NKVR+l7i8vZ/faWsrqc3WtvKSf0rbLWs3qqFZhYGJ4ALC47cUQszp/LgFmk6i4zM+tF9Qwac4AjJU2WNAC4CLijzISSmiQNa+8HzgQeqVtJzcyslLpVT0XEVkmXAncDDcDMiHhU0iU5fYakg4AWYDiwTdJlwBRS/d0sSe1lvCkiflqvsmZ7XMXVQ1zO7re3lNXl7F57SzmhD5V1n2oa3czM6sttT5mZWWkOGmZmVtp+FTRKNGsiSd/M6Q9LOr6XyjlR0i8kPSbpUUmfrJLndEmrcjMr8yT1ylvmO2vupS9sU0mvKWyneZJW5+tnxTy9tj0lzZS0TNIjhXEHSPqZpKfy56ga03b4ne6Bcn5F0uN5386SNLLGtD3WLFCNcl4t6YXC/j2nxrQ9tj07KOsPCuVcKGlejWl7p6mliNgvOtLF+KeBw4ABwEPAlIo85wA/IT1jchLwu14q68HA8bl/GPBklbKeDvy4D2zXhcCYDtL7xDat+B4sAQ7pK9sTeCNwPPBIYdw/Alfl/quAL9dYlw6/0z1QzjOB/rn/y9XKWeZ70gPlvBq4osR3o8e2Z62yVqR/Ffh8b2/TYrc/nWl02qxJHv5uJPcDIyUd3NMFjYgXI+LB3L8GeIz0hP3eqE9s04I3A09HxHO9WIadRHpo9eWK0ecB/577/53q7bOV+U7XtZwRMTsitubB+0nPY/WqGtuzjB7dntBxWZVuH30X8P16lqGr9qegUa1Zk8oDcZk8PUrSocBxwO+qJJ8s6SFJP5H0uh4t2A7tzb3MzU26VOpr2/Qiav8I+8L2bDcuIl6E9CcCOLBKnr62bT9MOqusprPvSU+4NFejzaxR3dfXtudpwNKIeKpGeq9s0/0paJRp1mRPmz7pVpKGArcCl0XE6orkB0lVLMcC/wTc1tPly06JiOOBs4GPSXpjRXqf2ab5IdNzgR9VSe4r27Mr+tK2/WtgK/C9Glk6+57U27eAw4GpwIukap9KfWZ7ZhfT8VlGr2zT/SlolGnWZI+aPulOkhpJAeN7EfGflekRsToi1ub+u4BGSWN6uJhE58299JltSvpxPRgRSysT+sr2LFjaXo2XP5dVydMntq2kDwJvB94bubK9UonvSV1FxNKIaIuIbcC/1Fh+n9ieAJL6A38E/KBWnt7apvtT0CjTrMkdwAfyHT8nAavaqwh6Uq7L/FfgsYj4Wo08B+V8SJpG2pcreq6UpZt76RPbNKv5z60vbM8KdwAfzP0fBG6vkme3m+rpLpLOAq4Ezo2I9TXy9HqzQBXX0S6osfxe354FbwEej4jWaom9uk17+sp7b3akO3meJN0h8dd53CXAJblfpBdHPQ38HmjupXKeSjotfhiYl7tzKsp6KfAo6Q6P+4E/6IVyHpaX/1AuS1/epkNIQWBEYVyf2J6kQPYi6b0yrcBHgNHAPcBT+fOAnPdVwF0dfad7uJwLSNcB2r+nMyrLWet70sPl/I/8/XuYFAgO7u3tWausefx32r+bhby9tk2LnZsRMTOz0van6ikzM9tDDhpmZlaag4aZmZXmoGFmZqU5aJiZWWkOGmYdkPQPuQXc83ui1dMaZbhXUnNvLNuskoOGWcdOJLX79SbgV71cFrNe56BhVkV+T8TDwBuA3wJ/CnxLVd6zIWmspFslzcndKXn81ZL+Q9LPld6L8Wd5vPL8H8nvQ3h3YV6fyeMeknRNYTF/LOkBSU9KOq2uK2/Wgf69XQCzvigiPi3pR8D7gcuBeyPilBrZvwF8PSJ+LWkScDfw2px2DOk9Ik3A/0i6EziZ1HDescAYYI6k+/K484ETI2K9pAMKy+gfEdOUXh70v0nNTJj1OAcNs9qOIzWNcRQwv4N8bwGm5KarAIa3twsE3B4RG4ANkn5BalTuVOD7EdFGapjwl6QzmjcB/xa5DaeIKL5nob3RyrnAoXu6Yma7y0HDrIKkqaS2fyYAL5HarVJ+7ebJOQgU9as2PgeRynZ6gupNcJPH12rXZ1P+bMO/W+tFvqZhViEi5kXEVPJrdoGfA2+LiKlVAgbAbFKDh8D2oNPuPEmDJI0mvVJ2DnAf8G5JDZLGkl75+UCez4clDcnzKVZPmfUJDhpmVeSD+SuR3r9wVER0VD31CaA5vxVuPqn13HYPAHeSWs79QqR3IMwitbb6ECkgfSYilkTET0ktsLbks5orun3FzPaQW7k1qxNJVwNrI+L/9HZZzLqLzzTMzKw0n2mYmVlpPtMwM7PSHDTMzKw0Bw0zMyvNQcPMzEpz0DAzs9L+P1dL1iBvO8vXAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_model_loss([idx_sorted[i] for i in range(3)],\"Worst cases\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Plot different architectures in a 2D space representating the different architectures with the minimum loss as the size**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_loss_map(results_list)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "## Results of specifying a different validation dataset\n",
    "It is important to understand the role of data in prediction quality of models. In the first exercise of hyperparameter optimization, a fraction of the main dataset is used which is randomly sampled. In the second exercise of hyperparameter optimization, the test dataset for XGBoost in Lab1 is used. The distribution of the sensor data is different from the main dataset. A histogram of the both datasets is shown below. \n",
    "<br>\n",
    "\n",
    "This leads to the difference in training of the autoencoder models and the loss for each model and, eventually an impact on the direction of hyperparameter optimization. \n",
    "\n",
    "<img src=\"./images/histogram_lab1.png\" alt=\"histogram lab 1\" width=\"700\"/>\n",
    "\n",
    "<br>\n",
    "<br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Optional Exercise\n",
    "\n",
    "Update **train_and_fit_AE** to optimize the autoencoder based on 1D convolutions example discussed in this lab. \n",
    "* What are the key components to perform the hyperparameter optimization ?\n",
    "* What can be inputs to the optimization ? \n",
    "* How should the objective function be structured ?   "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Exercise**: Define the method to create the autoencoder model with input parameters using tf.keras "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Model #2 -Conv1D-based Encoder-Decoder\n",
    "data_shape = x_train.shape[1:]  \n",
    "\n",
    "dp_lvl = 0.2\n",
    "\n",
    "def conv1d_autoencoder(data_shape, params_list):\n",
    "    \n",
    "    \n",
    "    mod_params = {} \n",
    "    mod_params['outside_filters'] = outside_filters.from_unscaled(int(np.round(params_list[0])))\n",
    "    mod_params['inside_filters'] = inside_filters.from_unscaled(params_list[1])\n",
    "    mod_params['learning_rate'] = learning_rate.from_unscaled(params_list[2])\n",
    "    mod_params['dropout_rate'] = dropout_rate.from_unscaled(params_list[3])\n",
    "    mod_params['num_epochs'] = 20\n",
    "    mod_params['batch_size'] = 128 \n",
    "    #convert from unscaled inputs provided by optimizer using params_list \n",
    "    \n",
    "    inputs = Input(shape=(data_shape))\n",
    "    ksize=6\n",
    "\n",
    "    # --- Encode ---\n",
    "    encoded = Conv1D(filters=mod_params['outside_filters'], kernel_size=ksize, activation=\"relu\", padding=\"same\")(inputs)\n",
    "    encoded = MaxPooling1D(pool_size=2)(encoded)\n",
    "    encoded = Dropout(rate=mod_params['dropout_rate'])(encoded)\n",
    "    encoded = Conv1D(filters=mod_params['inside_filters'], kernel_size=ksize, activation=\"relu\", padding=\"same\")(encoded)\n",
    "    encoded = MaxPooling1D(pool_size=2)(encoded)\n",
    "    encoded = Dropout(rate=mod_params['dropout_rate'])(encoded)\n",
    "    # --- Encodings ---\n",
    "    encoded = Conv1D(filters=4, kernel_size=ksize, activation=\"relu\", padding=\"same\")(encoded)\n",
    "    encoded = Dropout(rate=mod_params['dropout_rate'])(encoded)\n",
    "    # --- Decode ---\n",
    "    decoded = UpSampling1D(size=2)(encoded)\n",
    "    decoded = Conv1D(filters=mod_params['inside_filters'], kernel_size=ksize, activation=\"relu\", padding=\"same\")(decoded)\n",
    "    decoded = Dropout(rate=mod_params['dropout_rate'])(decoded)\n",
    "    decoded = UpSampling1D(size=2)(decoded)\n",
    "    decoded = Conv1D(filters=mod_params['outside_filters'], kernel_size=ksize, activation=\"relu\", padding=\"same\")(decoded)\n",
    "    decoded = Dropout(rate=mod_params['dropout_rate'])(decoded)\n",
    "    # --- Output ---\n",
    "    output_layer = Conv1D(filters=data_shape[1], kernel_size=1, activation=\"linear\", padding=\"same\")(decoded)\n",
    "\n",
    "    autoencoder = Model(inputs, output_layer )\n",
    "    autoencoder.compile(optimizer = \"adam\", loss = \"mse\", metrics = [\"accuracy\"])\n",
    "    \n",
    "    # provide an output filename for storing model weights for lowest loss for the current architecture\n",
    "    filename = \"case_%.3e_%.3e_%d_%d_best_model_ae-conv1d.h5\" %  \\\n",
    "                                 (mod_params[\"learning_rate\"],mod_params[\"dropout_rate\"], \\\n",
    "                                  mod_params[\"outside_filters\"],mod_params[\"inside_filters\"])\n",
    "    \n",
    "    if print_flag == 1:\n",
    "        print(autoencoder.summary())\n",
    "    return autoencoder, filename, mod_params"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Exercise**: Define the method to create the autoencoder model with input parameters using tf.keras. No change in needed but the cell below must be run "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "print_flag = 0 \n",
    "iter_history = []\n",
    "\n",
    "def train_and_fit_AE(param1, param2, param3, param4):\n",
    "    \n",
    "    time_start = time.time()\n",
    "    autoencoder, filename, params  = conv1d_autoencoder(data_shape, [param1, param2, param3, param4])\n",
    "   \n",
    "    # checkpoint\n",
    "    checkpoint = ModelCheckpoint(data_dir+filename, monitor='val_loss', \n",
    "                                 verbose=print_flag, save_best_only=True, mode='min')\n",
    "    es = EarlyStopping(patience=15, verbose=print_flag)\n",
    "\n",
    "    optimizer = Adam(lr=params['learning_rate'])\n",
    "    autoencoder.compile(optimizer=optimizer, loss='mse')\n",
    "\n",
    "    history = autoencoder.fit(x_train, x_train, batch_size=params['batch_size'], epochs=params['num_epochs'], \n",
    "                              validation_split=0.1, verbose=print_flag, callbacks=[es, checkpoint])\n",
    "    loss = history.history[\"val_loss\"][-1]\n",
    "    if np.isnan(loss) : loss = 1e8\n",
    "    result = np.exp(-20*loss**2) \n",
    "\n",
    "    print_history(len(iter_history), params, loss, result, \n",
    "                  count_params(autoencoder,\"train\"), \n",
    "                  count_params(autoencoder,\"non_train\"),\n",
    "                  (time.time()-time_start)/60.0)\n",
    "    iter_history.append({'objective':result, 'params': params, 'history':history.history, \"model\" : autoencoder})\n",
    "        \n",
    "    return result\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Exercise**: Define the method to create the autoencoder model with input parameters using tf.keras "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " Hyperparameter (min: 32, max: 196, scale: linear, step: 11, count: 15, type: int)\n",
      " Hyperparameter (min: 32, max: 196, scale: linear, step: 11, count: 15, type: int)\n",
      " Hyperparameter (min: 0.0005, max: 0.1, scale: log, step: 0.37845124046771694, count: 15, type: float)\n",
      " Hyperparameter (min: 0.0005, max: 0.1, scale: log, step: 0.37845124046771694, count: 15, type: float)\n"
     ]
    }
   ],
   "source": [
    "outside_filters = Hyperparameter({\"min\": 32, \"max\": 196, \"count\": 15, \"type\" : \"int\" })\n",
    "inside_filters = Hyperparameter({\"min\": 32, \"max\": 196, \"count\": 15, \"type\" : \"int\" })\n",
    "\n",
    "learning_rate = Hyperparameter({\"min\": 0.0005, \"max\": 0.1, \"count\": 15, \"scale\" : \"log\"})\n",
    "dropout_rate = Hyperparameter({\"min\": 0.0005, \"max\": 0.1, \"count\": 15, \"scale\" : \"log\"})\n",
    "\n",
    "\n",
    "pbounds = {\"param1\" : (0,outside_filters.count-1),\n",
    "           \"param2\"  : (0,inside_filters.count-1), \n",
    "           \"param3\"   : (0,learning_rate.count-1), \n",
    "           \"param4\"    : (0,dropout_rate.count-1), \n",
    "          } "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "def print_history(num_step, mod_params, loss, result, num_train, num_non_train, run_time):\n",
    "\n",
    "    if num_step == 0: \n",
    "        print(\"\\n| {:>4s} | {:>8s} | {:>8s} | {:>13s} | {:>7s} | {:>6s} | {:>9s} | {:>9s} | {:>13s} | {:>7s} | \\n|\".format(\"Iter\",\n",
    "                                                                         \"out filt\", \"in filt\",\n",
    "                                                                         \"learning rate\", \"dropout\",\n",
    "                                                                         \"loss\",\"objective\", \"trainable\", \"non trainable\",\n",
    "                                                                         \"t (min)\")+'-'*113+\"|\")\n",
    "        \n",
    "    print( \"| {:>4d} | {:>8d} | {:>8d} | {:>13.4g} | {:>7.3g} | {:>6.4f} | {:>9.4f} | {:>9d} | {:>13d} | {:>7.2f} |\".format( num_step + 1,\n",
    "        mod_params['outside_filters'],mod_params['inside_filters'],mod_params['learning_rate'],mod_params['dropout_rate'],\n",
    "        loss, result, num_train, num_non_train, run_time))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_history(history,result,oflt,iflt,lr,dr):\n",
    "    plt.figure()\n",
    "    plt.plot(history['loss'],'g')\n",
    "    plt.plot(history['val_loss'],'r')\n",
    "\n",
    "    plt.ylabel('Loss')\n",
    "    plt.xlabel('# epoch')\n",
    "    plt.legend(['train', 'test'], loc='lower right')\n",
    "    plt.ylim([0.1,0.9])\n",
    "    plt.title(\"Conv1D Loss across epochs \\n outside filters {:7d} inside filters {:7d} \\n learning rate {:7.4f} dropout rate {:7.4f}  \\n objective {:7.4f}\" .format(oflt,iflt,lr,dr,result))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_model_loss(idx_list,header): \n",
    "    \n",
    "    print(\" \"*20 + header)\n",
    "    for idx in idx_list:\n",
    "        cur_iter = iter_history[idx]\n",
    "        iter_history[idx][\"model\"].summary()\n",
    "        plot_history(cur_iter[\"history\"],cur_iter['objective'],\n",
    "                     cur_iter['params']['outside_filters'],cur_iter['params']['inside_filters'],\n",
    "                     cur_iter['params']['learning_rate'],cur_iter['params']['dropout_rate'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_loss_map(results_list):\n",
    "    results_list_shifted = [r - 0.9*min_res for r in results_list]\n",
    "    plt.figure(figsize=(6,4))\n",
    "    plt.scatter([c['params']['outside_filters'] for c in iter_history],\n",
    "                [c['params']['learning_rate'] for c in iter_history],\n",
    "                s=[r*500 for r in results_list_shifted],\n",
    "                c=[r for r in results_list_shifted ])\n",
    "\n",
    "    for i,c in enumerate(iter_history):\n",
    "        plt.text(c['params']['outside_filters'], c['params']['learning_rate'], r'%d'%(i),fontsize = 14)\n",
    "    plt.xlabel('outside_filters',fontsize=14)\n",
    "    plt.ylabel('learning_rate',fontsize=14)\n",
    "    plt.tick_params(labelsize=12)\n",
    "    plt.yscale('log')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Exercise**: Define the method to create the autoencoder model with input parameters using tf.keras "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model_35\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "input_36 (InputLayer)        [(None, 16, 21)]          0         \n",
      "_________________________________________________________________\n",
      "conv1d_6 (Conv1D)            (None, 16, 153)           19431     \n",
      "_________________________________________________________________\n",
      "max_pooling1d_2 (MaxPooling1 (None, 8, 153)            0         \n",
      "_________________________________________________________________\n",
      "dropout_5 (Dropout)          (None, 8, 153)            0         \n",
      "_________________________________________________________________\n",
      "conv1d_7 (Conv1D)            (None, 8, 76)             69844     \n",
      "_________________________________________________________________\n",
      "max_pooling1d_3 (MaxPooling1 (None, 4, 76)             0         \n",
      "_________________________________________________________________\n",
      "dropout_6 (Dropout)          (None, 4, 76)             0         \n",
      "_________________________________________________________________\n",
      "conv1d_8 (Conv1D)            (None, 4, 4)              1828      \n",
      "_________________________________________________________________\n",
      "dropout_7 (Dropout)          (None, 4, 4)              0         \n",
      "_________________________________________________________________\n",
      "up_sampling1d_2 (UpSampling1 (None, 8, 4)              0         \n",
      "_________________________________________________________________\n",
      "conv1d_9 (Conv1D)            (None, 8, 76)             1900      \n",
      "_________________________________________________________________\n",
      "dropout_8 (Dropout)          (None, 8, 76)             0         \n",
      "_________________________________________________________________\n",
      "up_sampling1d_3 (UpSampling1 (None, 16, 76)            0         \n",
      "_________________________________________________________________\n",
      "conv1d_10 (Conv1D)           (None, 16, 153)           69921     \n",
      "_________________________________________________________________\n",
      "dropout_9 (Dropout)          (None, 16, 153)           0         \n",
      "_________________________________________________________________\n",
      "conv1d_11 (Conv1D)           (None, 16, 21)            3234      \n",
      "=================================================================\n",
      "Total params: 166,158\n",
      "Trainable params: 166,158\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "None\n",
      "Train on 4162 samples, validate on 463 samples\n",
      "Epoch 1/20\n",
      "2944/4162 [====================>.........] - ETA: 0s - loss: 0.9336\n",
      "Epoch 00001: val_loss improved from inf to 0.49066, saving model to ./data/case_5.000e-03_1.000e-01_153_76_best_model_ae-conv1d.h5\n",
      "4162/4162 [==============================] - 1s 342us/sample - loss: 0.9002 - val_loss: 0.4907\n",
      "Epoch 2/20\n",
      "3712/4162 [=========================>....] - ETA: 0s - loss: 0.8147\n",
      "Epoch 00002: val_loss improved from 0.49066 to 0.43925, saving model to ./data/case_5.000e-03_1.000e-01_153_76_best_model_ae-conv1d.h5\n",
      "4162/4162 [==============================] - 0s 58us/sample - loss: 0.8011 - val_loss: 0.4392\n",
      "Epoch 3/20\n",
      "3584/4162 [========================>.....] - ETA: 0s - loss: 0.8096\n",
      "Epoch 00003: val_loss improved from 0.43925 to 0.43651, saving model to ./data/case_5.000e-03_1.000e-01_153_76_best_model_ae-conv1d.h5\n",
      "4162/4162 [==============================] - 0s 57us/sample - loss: 0.7617 - val_loss: 0.4365\n",
      "Epoch 4/20\n",
      "3712/4162 [=========================>....] - ETA: 0s - loss: 0.7611\n",
      "Epoch 00004: val_loss improved from 0.43651 to 0.40809, saving model to ./data/case_5.000e-03_1.000e-01_153_76_best_model_ae-conv1d.h5\n",
      "4162/4162 [==============================] - 0s 57us/sample - loss: 0.7257 - val_loss: 0.4081\n",
      "Epoch 5/20\n",
      "3584/4162 [========================>.....] - ETA: 0s - loss: 0.6857\n",
      "Epoch 00005: val_loss improved from 0.40809 to 0.39459, saving model to ./data/case_5.000e-03_1.000e-01_153_76_best_model_ae-conv1d.h5\n",
      "4162/4162 [==============================] - 0s 57us/sample - loss: 0.7113 - val_loss: 0.3946\n",
      "Epoch 6/20\n",
      "3712/4162 [=========================>....] - ETA: 0s - loss: 0.7437\n",
      "Epoch 00006: val_loss did not improve from 0.39459\n",
      "4162/4162 [==============================] - 0s 34us/sample - loss: 0.7046 - val_loss: 0.4349\n",
      "Epoch 7/20\n",
      "3456/4162 [=======================>......] - ETA: 0s - loss: 0.5889\n",
      "Epoch 00007: val_loss did not improve from 0.39459\n",
      "4162/4162 [==============================] - 0s 35us/sample - loss: 0.7094 - val_loss: 0.4274\n",
      "Epoch 8/20\n",
      "3712/4162 [=========================>....] - ETA: 0s - loss: 0.6344\n",
      "Epoch 00008: val_loss did not improve from 0.39459\n",
      "4162/4162 [==============================] - 0s 34us/sample - loss: 0.7013 - val_loss: 0.4165\n",
      "Epoch 9/20\n",
      "3456/4162 [=======================>......] - ETA: 0s - loss: 0.7227\n",
      "Epoch 00009: val_loss improved from 0.39459 to 0.39125, saving model to ./data/case_5.000e-03_1.000e-01_153_76_best_model_ae-conv1d.h5\n",
      "4162/4162 [==============================] - 0s 57us/sample - loss: 0.7234 - val_loss: 0.3912\n",
      "Epoch 10/20\n",
      "3456/4162 [=======================>......] - ETA: 0s - loss: 0.7506\n",
      "Epoch 00010: val_loss did not improve from 0.39125\n",
      "4162/4162 [==============================] - 0s 35us/sample - loss: 0.7538 - val_loss: 0.4936\n",
      "Epoch 11/20\n",
      "3584/4162 [========================>.....] - ETA: 0s - loss: 0.7758\n",
      "Epoch 00011: val_loss did not improve from 0.39125\n",
      "4162/4162 [==============================] - 0s 35us/sample - loss: 0.7467 - val_loss: 0.4067\n",
      "Epoch 12/20\n",
      "3712/4162 [=========================>....] - ETA: 0s - loss: 0.6415\n",
      "Epoch 00012: val_loss did not improve from 0.39125\n",
      "4162/4162 [==============================] - 0s 34us/sample - loss: 0.7150 - val_loss: 0.4672\n",
      "Epoch 13/20\n",
      "3584/4162 [========================>.....] - ETA: 0s - loss: 0.6276\n",
      "Epoch 00013: val_loss improved from 0.39125 to 0.39032, saving model to ./data/case_5.000e-03_1.000e-01_153_76_best_model_ae-conv1d.h5\n",
      "4162/4162 [==============================] - 0s 57us/sample - loss: 0.7261 - val_loss: 0.3903\n",
      "Epoch 14/20\n",
      "3712/4162 [=========================>....] - ETA: 0s - loss: 0.7007\n",
      "Epoch 00014: val_loss improved from 0.39032 to 0.38753, saving model to ./data/case_5.000e-03_1.000e-01_153_76_best_model_ae-conv1d.h5\n",
      "4162/4162 [==============================] - 0s 61us/sample - loss: 0.6729 - val_loss: 0.3875\n",
      "Epoch 15/20\n",
      "3712/4162 [=========================>....] - ETA: 0s - loss: 0.6932\n",
      "Epoch 00015: val_loss did not improve from 0.38753\n",
      "4162/4162 [==============================] - 0s 34us/sample - loss: 0.6961 - val_loss: 0.3930\n",
      "Epoch 16/20\n",
      "3456/4162 [=======================>......] - ETA: 0s - loss: 0.7468\n",
      "Epoch 00016: val_loss did not improve from 0.38753\n",
      "4162/4162 [==============================] - 0s 35us/sample - loss: 0.6814 - val_loss: 0.3910\n",
      "Epoch 17/20\n",
      "3712/4162 [=========================>....] - ETA: 0s - loss: 0.6869\n",
      "Epoch 00017: val_loss improved from 0.38753 to 0.35594, saving model to ./data/case_5.000e-03_1.000e-01_153_76_best_model_ae-conv1d.h5\n",
      "4162/4162 [==============================] - 0s 57us/sample - loss: 0.6900 - val_loss: 0.3559\n",
      "Epoch 18/20\n",
      "3584/4162 [========================>.....] - ETA: 0s - loss: 0.6796\n",
      "Epoch 00018: val_loss did not improve from 0.35594\n",
      "4162/4162 [==============================] - 0s 35us/sample - loss: 0.6706 - val_loss: 0.3695\n",
      "Epoch 19/20\n",
      "3712/4162 [=========================>....] - ETA: 0s - loss: 0.7037\n",
      "Epoch 00019: val_loss did not improve from 0.35594\n",
      "4162/4162 [==============================] - 0s 34us/sample - loss: 0.6701 - val_loss: 0.3707\n",
      "Epoch 20/20\n",
      "3584/4162 [========================>.....] - ETA: 0s - loss: 0.6150\n",
      "Epoch 00020: val_loss did not improve from 0.35594\n",
      "4162/4162 [==============================] - 0s 35us/sample - loss: 0.6543 - val_loss: 0.3644\n",
      "\n",
      "| Iter | out filt |  in filt | learning rate | dropout |   loss | objective | trainable | non trainable | t (min) | \n",
      "|-----------------------------------------------------------------------------------------------------------------|\n",
      "|    1 |      153 |       76 |         0.005 |     0.1 | 0.3644 |    0.0702 |    166158 |             0 |    0.09 |\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.07019531763120831"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print_flag = 1\n",
    "train_and_fit_AE(outside_filters.to_unscaled(160),\n",
    "                 inside_filters.to_unscaled(80),\n",
    "                 learning_rate.to_unscaled(.005),\n",
    "                 dropout_rate.to_unscaled(.1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Define the optimizer using the new method to call Conv1d based autoencoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'param1': (0, 14), 'param2': (0, 14), 'param3': (0, 14), 'param4': (0, 14)}"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pbounds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "optimizer = BayesianOptimization(\n",
    "    f=train_and_fit_AE,\n",
    "    pbounds=pbounds,\n",
    "    verbose=0, # verbose = 1 prints only when a maximum is observed, verbose = 0 is silent\n",
    "    random_state=1,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Run optimization "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "| Iter | out filt |  in filt | learning rate | dropout |   loss | objective | trainable | non trainable | t (min) | \n",
      "|-----------------------------------------------------------------------------------------------------------------|\n",
      "|    1 |       98 |      142 |     0.0005003 | 0.00248 | 0.1349 |    0.6950 |    188719 |             0 |    0.10 |\n",
      "|    2 |       54 |       46 |      0.001341 | 0.00312 | 0.1284 |    0.7193 |     40179 |             0 |    0.09 |\n",
      "|    3 |       98 |      114 |      0.004608 |  0.0189 | 0.2267 |    0.3576 |    154391 |             0 |    0.09 |\n",
      "|    4 |       65 |      167 |     0.0005781 |  0.0174 | 0.1675 |    0.5704 |    148320 |             0 |    0.10 |\n",
      "|    5 |       98 |      118 |      0.001052 | 0.00143 | 0.1185 |    0.7551 |    159295 |             0 |    0.09 |\n",
      "|    6 |      153 |      181 |      0.002631 |  0.0196 | 0.1903 |    0.4848 |    364188 |             0 |    0.10 |\n",
      "|    7 |      164 |      169 |     0.0007846 | 0.000615 | 0.1136 |    0.7725 |    365503 |             0 |    0.10 |\n",
      "|    8 |       54 |      167 |     0.0008419 | 0.00466 | 0.1407 |    0.6732 |    124637 |             0 |    0.09 |\n",
      "|    9 |       32 |      105 |        0.0005 |  0.0005 | 0.1864 |    0.4993 |     50363 |             0 |    0.09 |\n",
      "|   10 |      131 |      132 |     0.0009408 |  0.0005 | 0.1176 |    0.7585 |    233648 |             0 |    0.10 |\n",
      "|   11 |      109 |      186 |      0.001916 |  0.0005 | 0.1659 |    0.5765 |    268854 |             0 |    0.09 |\n",
      "|   12 |      186 |      125 |        0.0005 | 0.000764 | 0.1338 |    0.6989 |    312989 |             0 |    0.10 |\n",
      "|   13 |      120 |       73 |     0.0006664 | 0.000947 | 0.1474 |    0.6475 |    126675 |             0 |    0.09 |\n",
      "|   14 |      186 |      145 |      0.003075 |  0.0005 | 0.1954 |    0.4658 |    358629 |             0 |    0.09 |\n",
      "|   15 |      142 |      151 |        0.0005 | 0.000988 | 0.1170 |    0.7605 |    286037 |             0 |    0.10 |\n",
      "|   16 |      109 |      129 |        0.0005 |  0.0005 | 0.1287 |    0.7180 |    191448 |             0 |    0.10 |\n"
     ]
    }
   ],
   "source": [
    "print_flag = 0\n",
    "iter_history = []\n",
    "\n",
    "init_points = 8 \n",
    "num_steps = 8 \n",
    "\n",
    "optimizer.maximize(\n",
    "    init_points=init_points,\n",
    "    n_iter=num_steps,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Sort architectures"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "results_list = [cur_iter['objective'] for cur_iter in iter_history]\n",
    "\n",
    "idx_sorted = sorted(range(len(results_list)), key=results_list.__getitem__)\n",
    "min_res = results_list[idx_sorted[0]]\n",
    "max_res = results_list[idx_sorted[-1]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Plot the loss per epoch for the best three cases**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                    Best cases\n",
      "Model: \"model_42\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "input_43 (InputLayer)        [(None, 16, 21)]          0         \n",
      "_________________________________________________________________\n",
      "conv1d_48 (Conv1D)           (None, 16, 164)           20828     \n",
      "_________________________________________________________________\n",
      "max_pooling1d_16 (MaxPooling (None, 8, 164)            0         \n",
      "_________________________________________________________________\n",
      "dropout_40 (Dropout)         (None, 8, 164)            0         \n",
      "_________________________________________________________________\n",
      "conv1d_49 (Conv1D)           (None, 8, 169)            166465    \n",
      "_________________________________________________________________\n",
      "max_pooling1d_17 (MaxPooling (None, 4, 169)            0         \n",
      "_________________________________________________________________\n",
      "dropout_41 (Dropout)         (None, 4, 169)            0         \n",
      "_________________________________________________________________\n",
      "conv1d_50 (Conv1D)           (None, 4, 4)              4060      \n",
      "_________________________________________________________________\n",
      "dropout_42 (Dropout)         (None, 4, 4)              0         \n",
      "_________________________________________________________________\n",
      "up_sampling1d_16 (UpSampling (None, 8, 4)              0         \n",
      "_________________________________________________________________\n",
      "conv1d_51 (Conv1D)           (None, 8, 169)            4225      \n",
      "_________________________________________________________________\n",
      "dropout_43 (Dropout)         (None, 8, 169)            0         \n",
      "_________________________________________________________________\n",
      "up_sampling1d_17 (UpSampling (None, 16, 169)           0         \n",
      "_________________________________________________________________\n",
      "conv1d_52 (Conv1D)           (None, 16, 164)           166460    \n",
      "_________________________________________________________________\n",
      "dropout_44 (Dropout)         (None, 16, 164)           0         \n",
      "_________________________________________________________________\n",
      "conv1d_53 (Conv1D)           (None, 16, 21)            3465      \n",
      "=================================================================\n",
      "Total params: 365,503\n",
      "Trainable params: 365,503\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "Model: \"model_50\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "input_51 (InputLayer)        [(None, 16, 21)]          0         \n",
      "_________________________________________________________________\n",
      "conv1d_96 (Conv1D)           (None, 16, 142)           18034     \n",
      "_________________________________________________________________\n",
      "max_pooling1d_32 (MaxPooling (None, 8, 142)            0         \n",
      "_________________________________________________________________\n",
      "dropout_80 (Dropout)         (None, 8, 142)            0         \n",
      "_________________________________________________________________\n",
      "conv1d_97 (Conv1D)           (None, 8, 151)            128803    \n",
      "_________________________________________________________________\n",
      "max_pooling1d_33 (MaxPooling (None, 4, 151)            0         \n",
      "_________________________________________________________________\n",
      "dropout_81 (Dropout)         (None, 4, 151)            0         \n",
      "_________________________________________________________________\n",
      "conv1d_98 (Conv1D)           (None, 4, 4)              3628      \n",
      "_________________________________________________________________\n",
      "dropout_82 (Dropout)         (None, 4, 4)              0         \n",
      "_________________________________________________________________\n",
      "up_sampling1d_32 (UpSampling (None, 8, 4)              0         \n",
      "_________________________________________________________________\n",
      "conv1d_99 (Conv1D)           (None, 8, 151)            3775      \n",
      "_________________________________________________________________\n",
      "dropout_83 (Dropout)         (None, 8, 151)            0         \n",
      "_________________________________________________________________\n",
      "up_sampling1d_33 (UpSampling (None, 16, 151)           0         \n",
      "_________________________________________________________________\n",
      "conv1d_100 (Conv1D)          (None, 16, 142)           128794    \n",
      "_________________________________________________________________\n",
      "dropout_84 (Dropout)         (None, 16, 142)           0         \n",
      "_________________________________________________________________\n",
      "conv1d_101 (Conv1D)          (None, 16, 21)            3003      \n",
      "=================================================================\n",
      "Total params: 286,037\n",
      "Trainable params: 286,037\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "Model: \"model_45\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "input_46 (InputLayer)        [(None, 16, 21)]          0         \n",
      "_________________________________________________________________\n",
      "conv1d_66 (Conv1D)           (None, 16, 131)           16637     \n",
      "_________________________________________________________________\n",
      "max_pooling1d_22 (MaxPooling (None, 8, 131)            0         \n",
      "_________________________________________________________________\n",
      "dropout_55 (Dropout)         (None, 8, 131)            0         \n",
      "_________________________________________________________________\n",
      "conv1d_67 (Conv1D)           (None, 8, 132)            103884    \n",
      "_________________________________________________________________\n",
      "max_pooling1d_23 (MaxPooling (None, 4, 132)            0         \n",
      "_________________________________________________________________\n",
      "dropout_56 (Dropout)         (None, 4, 132)            0         \n",
      "_________________________________________________________________\n",
      "conv1d_68 (Conv1D)           (None, 4, 4)              3172      \n",
      "_________________________________________________________________\n",
      "dropout_57 (Dropout)         (None, 4, 4)              0         \n",
      "_________________________________________________________________\n",
      "up_sampling1d_22 (UpSampling (None, 8, 4)              0         \n",
      "_________________________________________________________________\n",
      "conv1d_69 (Conv1D)           (None, 8, 132)            3300      \n",
      "_________________________________________________________________\n",
      "dropout_58 (Dropout)         (None, 8, 132)            0         \n",
      "_________________________________________________________________\n",
      "up_sampling1d_23 (UpSampling (None, 16, 132)           0         \n",
      "_________________________________________________________________\n",
      "conv1d_70 (Conv1D)           (None, 16, 131)           103883    \n",
      "_________________________________________________________________\n",
      "dropout_59 (Dropout)         (None, 16, 131)           0         \n",
      "_________________________________________________________________\n",
      "conv1d_71 (Conv1D)           (None, 16, 21)            2772      \n",
      "=================================================================\n",
      "Total params: 233,648\n",
      "Trainable params: 233,648\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    },
    {
     "data": {
      "image/png": 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jgPea75d5Z9ylgl24wWn6qerv3rXujb4HrjVMuvc8pwe0U3EtVnbjWiTNIfg4EG/hktJC4Bdc4vfXD3emshT33ZqMaziQU76xJbaJiO/eTVa3Fe53OKcuwn2mn+LOmA7gkpbPQFzrp/W4cufjOdaUuALHWrLNxA29OjICMUad1RoyxpgYZ2cExhgT4ywRGGNMjLNEYIwxMc4SgTHGxDhLBLlIXJXQ2lHYbh8R+SLzOU20SAGq2GlijyWCQkBVx6nq5dGOA3I25kEG67xXRDaKKyX8toiEbHYqIs1E5GcR2e/92yzcdXntxj8SV454jYj0Dlj2anFliveIyFIR6ZKb+xkNIvKuiDydy+vM1aTnteN/XkS2eY8XxOvFF2L+NuLKgu8XV4K6RrjrEldO4htv2d8lYAwGEakoIuO9ptw7RGRcbu1nNFkiyOe8L26++JwkCgPPiEhbXBv/NrievKfh+loEmzcRmIprc38SrrbSVF8HojDWNQzX0/QUXAmMN8QrjCciVbz13geUxpU1Hi8iOS7ZEI33NbuiFGt/oAvQFGiC65Nza7AZRaQCrj/FY7iy4cm4TmPhrmsC8Cuu1tMgYLKIVPSbPgXXD6YGroTGv3O2a/lEtMufFqYHx5eALor7kqzF9cQcART3pp2E6724Bdf55hPcgCC+9czG60SG6wBT21v3AOBPb5lhHOsHcj3Hl5fOaN544CVc6d9VuA41R0sXB9mn1bhiW4twZQSK4A6mf+HKJy8FunrzNuD4csY7M3svwnhPxwPP+j1vA2wMMa+vBID4vbYWaJfZuoCSuCRQ12/6GOA57++WwOaA7W0BzgsRSy1cR7A9uMqrQzmxbPdNXnxzcT/KHsXVC9oMjAbKBMzfH9fxaQPwD79tFQVe8aat9/4uGuy74f899dbnX158egbf6zu879Mq77VXcT2odwM/Axd6r2e5THUY34F5QH+/5zcBP4aYtz8wz+95Sdz/ofqZrQuoi/uOJ/lN/xYY4Pf9Wh1u3AXpkS9+aRZSz+O+WM1w/+mqAI970+JwvXxrcKy349CA5fvivtRJHCsm1hHXI7MprvJk2wy2H2reW3CVIpsBZ+F+HWWmF66Call1tWT+Ai7E/ed+AhgrIpVUdRnHlzMuG8Z7kZlGuJ6zPguBUyR4Ce9GwCL1/td6FnmvZ7auukCaqv4RMN23bDKwTESuFJF477LQIULXIxqPO0BWwJVcDja85P/hkmdb3AH7elwFzNNwPcIDvxMX42ocXQ485HfZYhBwLu79bQq0wOsdnRF1vWT9y4tnVA23Cy4ZNvSez/e2V87b1w9EpJhmv0x1RoJ9bieUSw82r7qBdv4i4++A/7SV6nrCB5t+Lq4+0XveZaX5GZXRKEgsEUSAd83xFuBeVd3ufbGexSvLq6rbVPVDVd3vTXsGd1Dw966qLlFXyuCI99pz6qokrsVVQmxGaKHmvRp4VVVTVHUH8FwYu/SaulLHB7z4P1DV9epKRk/C/VJskZ33IgylcOUdfHx/J4Uxr2/+pBDT/deV4bLqykaMxh30Dnn/3qpBRvQSkeq4JPyYqh5S1bnA9CDxDlZXtvkA7lLUf1R1paruBR7G1Z/yvxTzhDf/b7gfEr281/vgauBsVtUtuOTcN8j2cmKI9/n5vgNjve9xqqq+hDsrCTW0ZU5LYgf73EqFuE+Qne+Ab12ZLVsVl8C+wZUBeQl36bFCmPuRbxWYa5MFTEVcNdGf/e9D4S7LICIlcP8R2uEuEwEkiUi8HqtTE6xwWaiyxcGEmjewdHE4BdKOm0dE+uGuldf0XiqF++UbTIbvRRj24q7J+/j+3hPGvL7594SY7r+uDJf1fn2/gCs69wtwNjBNRNqr6oKA5SoDOwKSxBpOrJ7q/74Glipfg/v/eUqI+dfgauiHWrYyuSvwO/AP3C/6yrhLR6UJ/R3wL1Pte803Olg4gn1uewPO/ELN65s/o+/AXlVVr+5RRssewI2r4asCO1FEBuGGspwa5r7kS3ZGEBlbcV+aRqpa1nuUUVXfwfgfuF9PLVW1NMdGIfP/hROpIlAbODYgB4RX2vloLF4LjLdw9xbKe5d/FnMs9sC4M3svMrMEd7nDpymwSV0hv2DzNgn4pdjEez2zdf0BFBGROgHTfcs2A+aqarJ3JjQf+B9uCMRAG4CTxI1Z4FM9yHz+71VgqfLquEspm/xeCyw5vT6DZX3TjitxLiKB402E+z3z/w5ciLtvdDVwkvcd2EXo74B/mWrfd6C0qoa6vBMo2Oe2JJx5vc/gdDL+DvhPO01EkkJMX0Tk/l9GlSWCCFBXpvYt4GVfqxIRqeK1WgF3qnkA2CluGMuQQwNGwPvA3V48ZTk26lK4SuL+M2wBEJEbcKN4+WwCqsqxUr+ZvReZGQ3cJCINxZVafhRXSTWY2bgb1XeJSFERGei97hvmMuS6vF/vU4AnRaSkuJLTnXE3jMFdE79QvOaoInIm7j7JCfcIVHUN7p7CE+JKarci89HoJgD3ikgtESnFsevs/vX9HxNXUrwRbojLSX7LPuo1bayAu//ia765EGgkrlltMdxY0/4CS4aHIwmXpLbgkufjHP9LOsdlqgOMBu7zvjeVcT+k3g0x70e4oSe7efv7OO6+kW9MjpDr8u4PLQD+Ja5cdVfcD4kP/dZ9kohc590n6o673/V9mPuRf0X7bnVhenB8q6FiuP/MKzlWTvgub1pl3EFrL+6X6K2cOLD5zaHW7T1/FzdyFgRvNRRq3iK4y1LbcK2G7sW18JAQ+7QauDTgtWdwZZe34mq0z+HYgOqJuPGZtwNbM3svwnxf78MdXHbjro0X9Zs2E3jE7/mZuJu0B3CXcM7MwrrKAR/jfkWvBXoHLDsQWIG7VLASv5Y7QWI+DdfiZC+hWw0V8Zs/DnfQWoc7wI7F/dr2n9/Xamgj8IDfssVw42Fv8B6vAcX8pg/yPqt1uFLb/t/TOriD307g48y+197zeFwLoN3e9h7w/57gml5+h2ux9ov3WhngDdzoXrtwTTR7hvn5C+6y3Hbv8QLHtwxbAvTxe34pbjCmA7j/SzWzsK6a3jIHcDeGA7/7FwK/eZ9rMl5rqYL+sDLUMU5E2gMjVLVGpjObqBA3StkqIEFzf7QxY+zSUKwRkeIi0kHciFtVcJelsjv4ujGmELBEEHsE17xwB+70fBnht+k3xhRCdmnIGGNinJ0RGGNMjLNEYPI9EZktIkHLEYhIdRHZKyLhdlALd5sRWa8x+ZElAlOgqepadTVt0jKfOzQRWe1XuyfX1hvmtsMqsy0iF3rJyf+hItLNmz4iYNohEfH1jC4qIqPEldfeIyK/ei3GfOuu6a3Lf/nHIr3vJn+wRGBMFEkWymyr6rdeciqlrmd2R1x79s+86QMCpk8APvAWL4LrR/B/uDb9jwHve01T/ZX1W8dTubenJj+zRGDyBRE5X1w1x13ev+cHzHK6iPzkTZ/q9cj2/yVbxHtexvvlu0FE/haRp/0v74jILXL84DJnicgYXFmG6d4v4Qf81ysiPUUkOSDee0Vkmvd3URH5t4isFZFN3i/z4mHu+nXAKHUFBnfgKpVen4VlJ2vwwnclgW64qp+oK/Y2WFVXqyuR8Qmub8LZYW7LFGKWCEzUeQf1GbgeseVxvZVnyPGlpvsBN+J6Zad68wYTstyxiPTAlVjohyuJcCWwTVX74noSd/J+Cb8QsM5pQD05vg5Rb1wFUsi7MttHiStc2N3b32C64Xoozw2x/ClezIE1e9aISIqIvCOFoKqmCY8lApMfXAH8qapj1JU1noArEeBfn2eMqi72fv0+BlwdeCNXMi93fDOu9v58dVaoqwuUIVXdj6su2cvbTh2gPq76aF6W2fbXDVc2Yk6I6dcBozVI+3ARScCNQ/CeHqvBsxVXOrsG7iwhyZvHxAArQ23yg8AyynjPq/g9DyzBnMCJZY8zK3dcDTdISXaMx9WffxJ3NvCxqu4XV0gvr8ps+8voQF8Ndy/gliDT4nCF9A7jaicBoG4MBN/lr03iCvZtEJHSqro7zH0xBZSdEZj8ILCMMrhr9n/7PQ8swXwE9yvWX2bljtfhShIHk1nPyi+ACuKqj/bi2GWhvCyzDRw90LfGVdIMph9uuMaVAcsJrljcKUA3PTbgUTC+9yPkIPGm8LBEYPKDT4G6ItLbuzl7DW5IxE/85rlWXPnoErhf5ZMDm3Zq5uWO/wv8U0TOFqe2uPEVIJNyzF6xt8nAi7gqpV96r+dlmW2fvrgDfaizm34h1vEGbmjMTuqNNOYjIi1FpJ73npXH3YOZraqBI3aZwija5U/tYQ9VBWiFKx+9y/u3ld+02cAQ4Cdc6ePpuF/9EFDSmUzKHePGVF6OuySzGK9MNW7sgbW4csz/DFyvN8+F3mvDAmLPszLb3mu/AzeFWNd5uDLaSQGv1/BiP+jtu+/Rx5veC9eKaB+utPRo4NRofy/skTcPqzVkCjQROQ03ZnIRtS+zMdlil4ZMQdcYN46sJQFjsimiiUBE2onIchFZISIPBZl+koh8JCKLvM5CjYOtx5hgROQ+YCSuZ64xJpsidmnIa+P9B3AZ7nrtfKCXqi71m+dFYK+qPiEi9XHXXttEJCBjjDFBRfKMoAWwQlVXquphYCLuhpy/hsAsAHUdW2p6nYKMMcbkkUh2KKvC8Z2AUoCWAfMsBK4CvhORFriWDVVxLSiOEpH+uIG7KVmy5Nn169ePVMzGGFMo/fzzz1tVtWKwaZFMBME6ogReh3oOeFVEFgC/4Zr6nTA4t6qOxF0Lpnnz5pqcnBw4izHGmAyISMhyKpFMBCkc3xu0Kq4H6VHquq7fAEd7Pa7yHsYYY/JIJO8RzAfqiEgtEUnEFeGa5j+DiJT1poErCDZXra6JMcbkqYidEahqqle46nNcAa63VXWJiAzwpo/AdXcfLSJpwFLgpkjFY4wxJriIVh9V1U9xdWT8Xxvh9/cPQJ3A5YwxxuQd61lsjDExzhKBMcbEOEsExhgT4ywRGGNMjLNEYIwxMc4SgTHGxDhLBMYYE+MsERhjTIyzRGCMMTHOEoExxsQ4SwTGGBPjLBEYY0yMi5lEsGXfFkYkjyBd06MdijHG5CsxkwhmrZrFbTNuY87qOdEOxRhj8pWYSQSd63WmdNHSjFk0JtqhGGNMvhIziaB4QnG6N+jOB0s/YP+R/dEOxxhj8o2YSQQA/Zr2Y+/hvXz8+8fRDsUYY/KNmEoEF9a4kBplatjlIWOM8RPRRCAi7URkuYisEJGHgkwvIyLTRWShiCwRkRsiGU+cxHFtk2v54q8v2LBnQyQ3ZYwxBUbEEoGIxAPDgPZAQ6CXiDQMmO0OYKmqNgVaAy+JSGKkYgLo26Qv6ZrO+N/GR3IzxhhTYETyjKAFsEJVV6rqYWAi0DlgHgWSRESAUsB2IDWCMVGvQj1aVmnJ6EWjI7kZY4wpMCKZCKoA6/yep3iv+RsKNADWA78Bd6tGvsdXv6b9WLRpEQs3Loz0powxJt+LZCKQIK9pwPO2wAKgMtAMGCoipU9YkUh/EUkWkeQtW7bkOLBrGl1DQlyC3TQ2xhgimwhSgGp+z6vifvn7uwGYos4KYBVQP3BFqjpSVZuravOKFSvmOLDyJcpzRd0rGPfbOFLTI3olyhhj8r1IJoL5QB0RqeXdAO4JTAuYZy3QBkBETgHqASsjGNNR/Zr0Y+PejcxaOSsvNmeMMflWxBKBqqYCA4HPgWXA+6q6REQGiMgAb7angPNF5DdgFvCgqm6NVEz+OtTpwEnFTrKbxsaYmFckkitX1U+BTwNeG+H393rg8kjGEErRIkXp2bgn7y54l92HdlO66Am3JowxJibEVM/iQP2a9uNA6gE+XPphtEMxxpioielE0LJKS+qUq2Oth4wxMS2mE4GI0LdJX75Z/Q1rdq6JdjjGGBMVMZ0IAK5tci0A434bF+VIjDEmOmI+EdQ6qRYX1biI0QtHoxrY380YYwq/mE8E4ArRLd+2nOT1ydEOxRhj8pwlAqBHwx4UjS/K6IXWp8AYE3ssEQBlipWhS/0uTFg8gcNph6MdjjHG5ClLBJ5+Tfux7cA2PlvxWbRDMcaYPGWJwHP56ZdzcsmT7fKQMSbmWCLwFIkrQqjP12QAACAASURBVO/GvZn+x3S2H9ge7XCMMSbPWCLw069pPw6nHeb9Je9HOxRjjMkzlgj8NDu1GY0qNrKSE8aYmGKJwI+I0K9pP+atm8eK7SuiHY4xxuQJSwQB+pzRB0EYs9DOCowxscESQYAqpavQ5rQ2jFk0xkpOGGNigiWCIPo16ceqnav4ft330Q7FGGMizhJBEF0bdKVkQknrU2CMiQmWCIIolViKbg278f6S9zmYejDa4RhjTERFNBGISDsRWS4iK0TkoSDT7xeRBd5jsYikiUi5SMYUrr5N+rLr0C6mL58e7VCMMSaiIpYIRCQeGAa0BxoCvUSkof88qvqiqjZT1WbAw8AcVc0X3XovrnkxVZKqMHqRXR4yxhRukTwjaAGsUNWVqnoYmAh0zmD+XsCECMaTJfFx8Vzb5Fpm/jmTzfs2RzscY4yJmEgmgirAOr/nKd5rJxCREkA74MMQ0/uLSLKIJG/ZsiXXAw2lb5O+pGkaExdPzLNtGmNMXotkIpAgr4VqmN8J+D7UZSFVHamqzVW1ecWKFXMtwMw0OrkRZ1U6y1oPGWMKtUgmghSgmt/zqsD6EPP2JB9dFvLXr0k/ft7wM0s2L4l2KMYYExGRTATzgToiUktEEnEH+2mBM4lIGeD/gKkRjCXbep3Ri3iJt0J0xphCK2KJQFVTgYHA58Ay4H1VXSIiA0RkgN+sXYEvVHVfpGLJiZNLnky72u0Y99s40tLToh2OMcbkuoj2I1DVT1W1rqqerqrPeK+NUNURfvO8q6o9IxlHTvVr2o+U3SnMXj072qEYY0yus57FYehUtxNlipaxPgXGmELJEkEYiicUp0fDHny49EP2HNoT7XCMMSZXWSII0y1n38K+I/sY8t2QaIdijDG5yhJBmFpUacF1Ta/j3/P+ze9bf492OMYYk2ssEWTBC5e9QMnEktzx6R02aI0xptCwRJAFJ5c8mWcveZavV33NpCWToh2OMcbkCksEWdT/7P40r9ycez+/l10Hd0U7HGOMyTFLBFkUHxfP8A7D2bR3E/+a/a9oh2OMMTlmiSAbzqlyDreefSuv//Q6CzYuiHY4xhiTI5YIsunZNs9Svnh5bp9xO+maHu1wjDEm2ywRZNNJxU/ixcte5IeUH3h3wbvRDscYY7LNEkEO9Gvaj1bVW/HAlw+wbf+2aIdjjDHZYokgB0SE4R2Gs/PgTh6Z9Ui0wzHGmGyxRJBDZ5xyBne3vJu3fnmLH1N+jHY4xhiTZZYIcsHg1oOplFSJ22fcbmMWGGMKHEsEuSCpaBKvtH2FXzf+yhvJb0Q7HGOMyRJLBLmke8PuXHbaZQz6ehAb926MdjjGGBM2SwS5REQY2mEoB1MPcv+X90c7HGOMCVtEE4GItBOR5SKyQkQeCjFPaxFZICJLRGROJOOJtLrl6/LA+Q8wdtFYG9bSGFNgRCwRiEg8MAxoDzQEeolIw4B5ygLDgStVtRHQI1Lx5JVHLnyEWmVrcfuM2zmcdjja4RhjTKYieUbQAlihqitV9TAwEegcME9vYIqqrgVQ1c0RjCdPFE8ozmvtX2PZ1mW88uMr0Q7HGGMyFclEUAVY5/c8xXvNX13gJBGZLSI/i0i/YCsSkf4ikiwiyVu2bIlQuLmnY92OdK7XmSfmPMHaXWujHY4xxmQokolAgrwWOKxXEeBs4AqgLfCYiNQ9YSHVkaraXFWbV6xYMfcjjYBX272KqnLv5/dGOxRjjMlQJBNBClDN73lVYH2QeT5T1X2quhWYCzSNYEx5pkbZGjx20WNMWTaFmX/OjHY4xhgTUiQTwXygjojUEpFEoCcwLWCeqcCFIlJEREoALYFlEYwpT/3j/H9Qv0J9Bs4cyIEjB6IdjjHGBBWxRKCqqcBA4HPcwf19VV0iIgNEZIA3zzLgM2AR8BPwX1VdHKmY8lpifCLDOgxj5Y6VPP/989EOxxhjghLVwMv2+Vvz5s01OTk52mFkSe8PezNl2RQW376Y2uVqRzscY0wMEpGfVbV5sGnWszgPvHT5SyTGJzLw04EUtMRrjCn8LBHkgUpJlXjq4qf4/K/PGT5/eLTDMcaY41giyCMDWwykU91O3P3Z3Xz515fRDscYY46yRJBH4uPiGXfVOBpWbEiPD3qwfOvyaIdkjDGAJYI8lVQ0iWm9ppEYn0inCZ3YfmB7tEMyxhhLBHmtZtmaTLlmCmt2reHqD67mSNqRaIdkjIlxlgiioFX1VozsOJJZq2Zx92d3RzscY0yMKxLtAGLVdc2uY8mWJbw470UaVWzEHS3uiHZIxpgYZWcEUTSkzRBrSWSMiTpLBFFkLYmMMfmBJYIos5ZExphos0SQD1hLImNMNFkiyCesJZExJlrCajUkIiWBA6qa7o0gVh+Yqar20zUXWUsiY0w0hHtGMBcoJiJVgFnADcC7kQoqlllLImNMXgs3EYiq7geuAl5X1a5Aw8iFFbusJZExJq+FnQhE5DygDzDDe806o0WItSQyxuSlcBPBPcDDwEfecJOnAd9ELixjLYmMMXklrESgqnNU9UpVfV5E4oCtqnpXZsuJSDsRWS4iK0TkoSDTW4vILhFZ4D0ez8Y+FFrWksgYkxfCSgQiMl5ESnuth5YCy0Xk/kyWiQeGAe1x9xN6iUiw+wrfqmoz7/FkFuMv9K5rdh33n38/byS/wbCfhkU7HGNMIRTupaGGqrob6AJ8ClQH+mayTAtghaquVNXDwESgc7YjjWH+LYlmr54d7XCMMYVMuIkgQUQScIlgqtd/ILNR2KsA6/yep3ivBTpPRBaKyEwRaRRsRSLSX0SSRSR5y5YtYYZcePhaEtUpX4eek3uyYc+GaIdkjClEwk0EbwKrgZLAXBGpAezOZBkJ8lpg8vgFqKGqTYHXgY+DrUhVR6pqc1VtXrFixTBDLlySiiYxucdk9hzewzWTryE1PTXaIRljColwbxa/pqpVVLWDOmuAizNZLAWo5ve8KrA+YL27VXWv9/enuDOPCuGHH1sandyIkR1H8u3ab3lk1iPRDscYU0iEe7O4jIj8x3d5RkRewp0dZGQ+UEdEaolIItATmBaw3lNFRLy/W3jxbMvyXsSQPk36cFvz23hx3ot8/HvQEyhjjMmScC8NvQ3sAa72HruBdzJaQFVTgYHA58Ay4H2vD8IAERngzdYdWCwiC4HXgJ6qmtm9h5j3ctuXaV65Odd/fD1/bf8r2uEYYwo4Cee4KyILVLVZZq/lhebNm2tycnJebzbfWb1zNWe9eRY1ytZg3o3zKJ5QPNohGWPyMRH5WVWbB5sW7hnBARFp5bfCC4ADuRGcyZ6aZWsypusYFmxcwJ0z74x2OMaYAizcRDAAGCYiq0VkNTAUuDViUZmwXFH3CgZdOIhRv47inV8zvFJnjDEhhdtqaKHXxLMJ0ERVzwQuiWhkJixPtH6CS2pdwu2f3s7CjQujHY4xpgDK0ghlXnNPX/+B+yIQj8mi+Lh4JnSbQLni5ej+QXd2HdwV7ZCMMQVMToaqDNZhzETBySVPZlL3SazasYobp92INbwyxmRFThKBHW3ykVbVW/H8pc8zZdkUXv7x5WiHY4wpQDIcXEZE9hD8gC+AtVfMZ+477z6+X/c9D3z5AC2qtKBV9VaZL2SMiXkZnhGoapKqlg7ySFJVG6EsnxER3un8DrVOqsXVH1zNpr2boh2SMaYAyMmlIZMPlSlWhsk9JrPj4A56T+lNWnpatEMyxuRzlggKoaanNmV4h+F8vepr/jX7X9EOxxiTz1kiKKRuOPMGbjrzJp759hlm/DEj2uEYY/IxSwSF2OvtX6fZqc3o+1FfVu9cHe1wjDH5lCWCQqx4QnEm95hMuqbT44MeHEo9FO2QjDH5kCWCQu70cqfzbpd3SV6fzF0z72Lv4b3RDskYk8+EVYY6P7Ey1NnzwJcP8OK8FwEolViKykmVqVSq0vH/Jh3/PKloUpSjNsbklozKUFtfgBgxpM0QWlZpyV87/mL9nvVs2LuB9XvW89PfP7F+z3oOpJ5YVbxkQsmjCaJSqUqcdtJp/PP8f1KueLko7IExJlLsjMCgquw+tPtogtiwZ8NxycL376odq2h2ajO+6vcVZYuVjXbYxpgssDMCkyERoUyxMpQpVoYGFRuEnG/GHzPoOqkrbce25Ytrv6BMsTJ5GKUxJlIierNYRNqJyHIRWSEiD2Uw3zkikiYi3SMZj8mZK+peweSrJ/PLhl9oP649ew7tiXZIxphcELFEICLxwDCgPdAQ6CUiDUPM9zxukPvI2rkz4pso7K6sdyWTuk/ip79/osP4DtYKyZhCIJJnBC2AFaq6UlUPAxOBzkHmuxP4ENgcwVhg8mSoUQOWLYvoZmLBVQ2uYny38cxbN4+O4zuy7/C+aIdkjMmBSCaCKsA6v+cp3mtHiUgVoCswIqMViUh/EUkWkeQtW7ZkL5oLLoDEROjVCw5Zx6qcurrR1YzpOoZv137LlROv5MCRE1sdGWMKhkgmgmAjmAU2UXoFeFBVMyyRqaojVbW5qjavWLFi9qKpVAnefhsWLoRBg7K3DnOc3mf05p3O7/DNqm/oMqkLB1MPRjskY0w2RDIRpADV/J5XBdYHzNMcmCgiq4HuwHAR6RKxiDp1gttug5degi+/jNhmYkm/pv3475X/5Yu/vuCqSVdZGQtjCqBIJoL5QB0RqSUiiUBPYJr/DKpaS1VrqmpNYDJwu6p+HMGY4N//hgYN4LrrYOvWiG4qVtx45o282fFNZq6YSY8PenA47XC0QzLGZEHEEoGqpgIDca2BlgHvq+oSERkgIgMitd1MlSgB48fDtm1w881QwDrU5Vf9z+7PsA7DmP7HdK6ZfA1H0o5EOyRjTJgi2o9AVT9V1bqqerqqPuO9NkJVT7g5rKrXq+rkSMZzVLNmMGQITJ0KI0fmySZjwe3n3M6r7V7l498/pveU3qSmp+Z4nWnpaXy96mtumnoTDYc1ZNyicbkQqTHGX+z2LL7nHvjsM7j3Xvi//4P69aMdUaFwV8u7SE1P5R9f/IMicUUY03UMReKy9jVTVRZsXMC438YxYfEE1u9ZT1JiElVLV+Xaj65l3e51PHjBg4gEa49gjMmq2E0EcXHw7rvQpAn07g0//ABFi0Y7qkLhvvPuIzU9lQe/epB4iee9Lu8RHxef6XKrdqxi/G/jGffbOJZtXUZCXALt67Snzxl96FS3E3ESx43TbuThWQ+zdtdaXm//eljrNcZkLHYTAUDlyq5JaefO8Nhj8MIL0Y6o0Hjgggc4knaER795lCJxRXi789vEyYlXIrfu38r7S95n3G/jmLduHgAXVr+QEVeMoEejHidUOh3TdQzVS1fnue+f4+89fzOh2wRKJJTIk30yprCK7UQAcOWVMGAAvPgitG0LbdpEO6JCY9BFg0hNT2XwnMEUiSvCyE4jiZM49h/Zz9TfpzLut3F8/tfnpKan0vjkxgxpM4RejXtRo2yNkOuMkziGXDqEamWqcefMO7nkvUuY3ms6FUtms3+JMcbKUAOwfz+cfTbs3g2LFkH58rm7/himqjz2zWM88+0z9DmjDyLCR8s+Yt+RfVQtXZXejXvTp0kfmpzSJMvrnvr7VHp92Isqpasws89MaperHYE9MKZwyKgMtSUCn19/hZYtoWNH+PBDsBuRuUZVeXjWwzz//fOULVaW7g2606dJHy6qcVHQy0VZ8WPKj3Sa0AmAT3p9QsuqLXMjZGMKHUsE4fr3v+H+++Gtt1wfA5NrVJWFmxbSoEIDihbJ3Zvyf277k3bj2rFhzwYmdp/IlfWuzNX1G1MYZJQIbPB6f/fd5+4R3H03LF8e7WgKFRGh2anNcj0JANQpX4cfbvqBxic3puukroxIzrCGoTEmgCUCf3Fx8N57UKwY9OkDh61UQkFxcsmT+ea6b+hQpwO3zbiNR2Y9QkE72zUmWiwRBKpSBUaNgp9/hscfj3Y0JgtKJpbko2s+ov9Z/Rny3RD6ftTX6h4ZEwZLBMF06QL9+7t+Bd98E+1oTBYUiSvCiI4jeOaSZxj32zjaj2vProO7oh2WMfmaJYJQ/vMfqFsX+vaF7dujHY3JAhHhkQsf4b0u7zF3zVwufOdCUnanRDssY/ItSwShlCzpqpRu3uzODux6c4HTr2k/Pu39Kat3rubc/57Lb5t+i3ZIxuRLlggyctZZ8PTTrl/BO+9EOxqTDZedfhnf3vAtitLqnVaMXTTWbiIbE8ASQWb++U+45BK46y74889oR2OyoempTfnhph9oUKEBfT/qy6VjLmX51rxtHrzjwA5GJI9g877NebpdY8JhiSAzcXEwerQb+L53b9i7N9oRmWyoXqY639/4PW9c8QY/r/+ZJiOa8K9v/hXxcZYPHDnA8989z2mvncZtM27jvFHn8ec2+0Fh8hdLBOHwb1LarBn873/RjshkQ3xcPAOaD+D3gb/To2EPnpz7JI2HN+aLv77I9W2lpqfy1s9vUfv12jw06yHOr3Y+464ax+5Duzn/7fP5X4p9h0z+YYkgXF27wuzZcOQIXHABPPkkpOZ8BC6T904tdSpjrxrLV32/Ik7iaDu2LT0n92TDng05Xreq8uHSD2k8vDH9P+lP9TLVmXP9HGb0nkHvM3oz78Z5lC5amovfu5hP/vgkF/bGmJyLaCIQkXYislxEVojIQ0GmdxaRRSKyQESSRaRVJOPJsYsugoULoWdP+Ne/3POVK6MdlcmmNqe1YdFti3ii9RN8/PvH1B9Wn6E/DSUtPS1b6/tm1TecO+pcun/QnTiJ46NrPmLejfO4qMZFR+epU74O826cR6OTG9F5Ymfe+vmt3NodY7JPVSPyAOKBv4DTgERgIdAwYJ5SHCt81wT4PbP1nn322ZovjB+vWqaMaqlSqu+8o5qeHu2ITA78sfUPvWz0ZcpgtPnI5pr8d3LYy/664VdtO6atMhit+p+qOuqXUXok7UiGy+w5tEfbj22vDEYf//pxTbfvj4kwIFlDHFcjeUbQAlihqitV9TAwEegckIT2egEClAQKTru+Xr3c2cFZZ8ENN8DVV1vHswKsTvk6fH7t50zoNoF1u9bR4r8tuGvmXRn2Sl65YyV9pvThzDfP5Ke/f+LFy17kj4F/cOOZN2Y6TnOpxFJM7TmVG5rdwJNzn+TmaTdzJO1Ibu+WMeEJlSFy+gC6A//1e94XGBpkvq7A78B24LwQ6+oPJAPJ1atXj0y6zK7UVNXnnlNNSFCtXFn1yy+jHZHJoR0HdugdM+5QGSxa6d+VdNLiScf9Yt+4Z6MOnDFQE55M0OJPF9eHv3pYdxzYka1tpaen62NfP6YMRtuPba97Du3Jrd0w5jhkcEYQyUTQI0gieD2D+S8Cvspsvfnm0lCgn39WrVfPvaX33ad68GC0IzI59FPKT3rmiDOVwWjbMW311w2/6uNfP64lnymp8U/E663Tb9W/d/+dK9t6M/lNjXsiTpuPbK6b9m7KlXUa4y+jRBCxgWlE5DxgsKq29Z4/7J2BDMlgmVXAOaq6NdQ8ER2YJqf273cD2wwfDk2awLhx0LhxtKMyOZCansrw+cN59OtH2XN4DwA9Gvbg6Uuepm75urm6rWnLp9Fzck8qJ1Xms2s/s6E3Ta6KyghlIlIE+ANoA/wNzAd6q+oSv3lqA3+pqorIWcB0oKpmEFS+TgQ+M2bAjTfCrl3w/PNw552uY5opsP7e/TejF47mstMvo3nloP+XcsWPKT/ScXxH4iSOT3p/QosqLSK2LRNbojJCmaqmAgOBz4FlwPuqukREBojIAG+2bsBiEVkADAOuySgJFBhXXAG//QaXXgr33APt28OGnLdRN9FTpXQVHr7w4YgmAYBzq57LvJvmUSqxFBe/dzEz/pgR0e0ZAzZmcWSpwptvuiEwS5SAkSPhqquiHZUpADbt3USH8R1YuHEhIzqO4OazbAxtkzM2ZnG0iMCAAfDLL1CjBnTrBuefDx9/DOnp0Y7O5GOnlDqF2dfN5tLTLuWW6bcwePZgq5pqIsYSQV6oXx9++AGGDoWNG125igYN4K234GBki56ZgiupaBLTe03nuqbX8cScJ7hl+i2kpltZE5P7LBHklcREuOMO+OMPmDQJkpLcgDc1a8Kzz8KOHdGO0ORDCfEJvNP5HR698FFG/TqKa6dca8nA5DpLBHmtSBHXC3n+fPj6azjzTBg0CKpVg3vvhbVrox2hyWdEhKcueYoXLn2BSUsmccPUG7JdD8mYYCwRRIsIXHwxzJzpSlVcdZW7dHTaaW6c5EWLoh2hyWfuv+B+nr74acYuGsst028hXe0+k8kdGRdEMXmjSRM3+M3TT8Mrr7h7B2PHQtu28MADLmGIhLcuVdiyBVavPv6xapXr8Pbyy64+kimQBl00iMNph3ly7pMkxCUwouMIJNzvhjEhWPPR/GjHDhgxAl59FTZtcgfuBx5wrY7i42HbtuMP8IEH/f37j19fuXLuXsT69XD4sLsk1bRpHu+UyS2qyqCvBzHkuyEMPGcgr7V/zZKByVRUehZHSkwkAp+DB92ZwYsvupvMFSrAgQOwb9/x85UtC7VquYO9/6NWLddstXRpN9+qVW4MhYMH3SA7jRrl6e6Y3KOq3P/l/bz0w0vce+69vHT5S5YMTIYySgR2aSg/K1YMbr7ZlauYNg0mT3bJwP+gX6OGSwThqFULvvnGJYM2bVwyqF8/gjtgIkVEePGyFzmcdpiXf3yZhLgEnrv0OUsGJlssERQEcXHQpYt75FTt2u7S0P/9H1xyCcyZA3Xq5Hy9Js+JCK+2e5UjaUd4Yd4LFC1SlCcvfjLaYZkCyFoNxaL69WHWLDf+8iWXuEtGpkASEYZdMYybzryJp+Y+xVNznop2SKYAsjOCWNW4MXz1lWuR5DszqF492lGZbIiTOEZ2GsmR9CM8PvtxEuMTebDVg9EOK985cuQIKSkpHCzkvfmLFStG1apVSUhICHsZSwSxrGlT+PJLd7/AlwyqVIl2VCYb4iSOt698myNpR3ho1kMkxidy73n35ni9qsrcNXN5e8HbTP19KudWPZcHLniAi2teXODuR6SkpJCUlETNmjULXOzhUlW2bdtGSkoKtWrVCns5uzQU684+Gz7/HDZvdslg48ZoR2SyKT4untFdR9O9YXfu++I+hv40NNvr+nv33zz77bPUHVqX1u+15qNlH9G+Tnt+3fgrbUa34Zy3zmHS4kkFqtzFwYMHKV++fKFNAuAuFZYvXz7LZz2WCAy0bOl6OP/9tzs72Lw52hGZbCoSV4TxV42nc73O3DnzTt5MfjPsZQ+nHeajZR/RcXxHqr9SnUFfD6JyUmXe6/IeG/+5kQndJrDmnjW82fFN9hzeQ88Pe1L39boM/Wko+w7vy3wD2aSqLN68mNf+9xprdq7J0boKcxLwyc4+Wj8Cc8ycOW4Qndq1XTPT8uWjHZHJpkOph+j2fjdm/DmDUVeO4sYzbww577Ityxj16yhGLxzNlv1bqFSqEtc3u54bmt1AnfLBW5Slpacxbfk0Xpz3Ij+k/ED54uUZ2GIgd5xzBxVLVsxx/IfTDjN3zVymL5/O9D+ms2qna9BQo0wN5lw/hxpla2R5ncuWLaNBgwY5jq0gCLavGfUjiNjg9ZF65NvB6wuLL79ULVpU9cwzVbdvj3Y0JgcOHDmgl4+5XGWw6JiFY46btvvgbn3r57f03P+eqwxGizxZRLtO7KqfLP9Ej6QdydJ2vlvznV454UplMFrs6WJ6+ye364ptK7Ic79Z9W3XMwjHa4/0eWnpI6aPru2LcFTpi/gj97M/PtMyQMnraq6dpyq6ULK9/6dKlWV4mN+3YsUOHDRuW5eXat2+vO3bsyNIywfaVaAxeHyl2RpAHPvsMOnc+djO5TJloR2Sy6cCRA3Sc0JHZq2cz7qpxVC1dlVG/juL9Je+z/8h+GlRowE1n3kTfpn05ueTJOdrWsi3LeOmHlxizaAyp6al0a9CN+8+/n3OqnBNymeVblzNt+TSm/zGd79d9T7qmc0rJU+hUtxOd6nWiTa02lEwseXT+/6X8j8vGXEalpErMuX4Op5Y6Nfz4onxGsHr1ajp27MjixYuPez0tLY34+Phc3VZWzwgimghEpB3wKhAP/FdVnwuY3gfwtXPbC9ymqgszWqclgjwyfbqriHrOOe5mclJStCMy2bTv8D46jO/A3DVzASiVWIqejXpy01k30bJKy1y/br5hzwZe+99rvJH8BrsO7aJ1zdbcf/79tK/dnjRN47u13x295PPn9j8BaHpK06MH/+aVmxMnoW9ffrf2O9qNbUeNsjWYfd3ssC9F+R8c7/nsHhZsXJDznfXT7NRmvNLulZDTe/bsydSpU6lXrx4JCQmUKlWKSpUqsWDBApYuXUqXLl1Yt24dBw8e5O6776Z///4A1KxZk+TkZPbu3Uv79u1p1aoV8+bNo0qVKkydOpXixYtnuK8+UUkEIhIP/AFcBqQA84FeqrrUb57zgWWqukNE2gODVbVlRuu1RJCHpkxxYyecf767mVyyZObL+KjC9u3w11/HHps3uzONSy4Jv5qqyRV7Du3h8W8ep+mpTenesDulEkvlyTbf+uUtXv7xZVJ2p1CnXB227t/KjoM7SIxP5OKaF9Opbic61u2Y5Wv+s1fPpsO4DtQpX4ev+31N+RKZ38+KdiLwPyOYPXs2V1xxBYsXLz7azHP79u2UK1eOAwcOcM455zBnzhzKly9/XCKoXbs2ycnJNGvWjKuvvporr7ySa6+9NsN99YlWraEWwApVXekFMRHoDBxNBKo6z2/+H4GqEYzHZNVVV8G4cdC7N3TqBJ98AiVKHJuenu5aGq1YcfwB3/fYtev49RUrBq+/7jqz3X039OkDQX7NmNyXVDSJl9u9nOfbvO+8+7izxZ1MXDyRtxe8zfnVzqdT3U5cfvrlJBXN/llm65qtmdpzKp0mdOLysZczq98syhYL6AC7mQAAEllJREFUs+YWZHjAzistWrQ4rq3/a6+9xkcffQTAunXr+PPPPykf0GCjVq1aNGvWDICzzz6b1atX50oskUwEVYB1fs9TgIx+7d8EzIxgPCY7rrnGlaLo1w86dHD3DXwH+lWr4NChY/MWKeIK4Z1+Opx7rvv39NNdK6RatVzNpIkTXXntW26Bhx5yw3XefjtUtd8AhVVCfAJ9m/alb9O+ubrey06/jCnXTKHLxC60HduWL/t+SemipXN1G5FU0u8Me/bs2Xz11Vf88MMPlChRgtatWwftC1C0aNGjf8fHx3PgwIFciSWSiSDYuX/Q61AicjEuEbQKMb0/0B+gupVByHvXXuuSwa23QnKyO7g3aAAdO7qDvO+AX62aSwYZuf56uO46+PZbNwjP88+7Mtvdu7uzhHPPzZNdMoVDhzod+KDHB3T/oDsdxnXgs2s/y5PLXtmRlJTEnj17gk7btWsXJ510EiVKlOD333/nxx9/zNPYIpkIUoBqfs+rAusDZxKRJsB/gfaqui3YilR1JDAS3D2C3A/VZOqGG9wlosTEnF/fF3GlsC+6yJ1VDB0Ko0a5s4WWLV1C6N4dslArxcSuzvU7M6HbBHpO7kmnCZ2Y0XsGJRJKZL5gHitfvjwXXHABjRs3pnjx4pxyyilHp7Vr144RI0bQpEkT6tWrx7l5/YMoVLvSnD5wSWYlUAtIBBYCjQLmqQ6sAM4Pd73Wj6CQ2rNHdehQ1Tp1VEG1cmXVZ55R3bIl2pGZAmLconEqg0UvG32ZHjhy4ITp0e5HkJey2o8gYmcEqpoqIgOBz3HNR99W1SUiMsCbPgJ4HCgPDPeasKVqqJ5vpnArVQruuANuu831Y3j1VRg0CJ56yt1UvvtuOOOMvIklPd21eNqyxT02b3b/7tjheltXrXrsUa6ctYDKJ3qf0ZvDaYe5YeoNdHu/G1OunkLRIkUzX9BYhzKTjy1dCq+9BqNHuyE6L7oITjvNtT7yfxQtGt7zhAR3MPc/uPv/7ft32zZISwsvxmLFjk8MwR4VK7ob5SZPjPx5JLd+ciud63Xmgx4fkBDvLjFGu0NZXspPzUeNyZmGDWHECHj2WXjrLdeUddYsN+ay73HkSM62UbYsnHyyO1jXqQMXXOD+9r3m//dJJ7kkkZIS/PHtt645bWpARc6EBFfeu1o1aN3ajTR35pl2JhEh/c/uz5G0IwycOZDeU3ozodsEisTZoS4j9u6Y/K9cOXjwQfcIlJ7umrD6J4fA577XDh92B3Pfwb1ChazfkK5SxT1ahmgJnZ7uziyCJYoVK+CZZ9zlrmrVXOe6zp3dsKF2YzxX3dHiDg6nHea+L+4jMT6R0V1GRzukfM0SgSnY4uJcp7T80jEtLg5OPdU9mgc5C9+yxXXMmzrVtZQaOtTVcrriCpcU2rWD0gWnLXx+du9593Io7RAPz3qYxPhE7q97f7RDyrfswqUxealiRdcU9+OPYetW9+9VV8EXX7jOexUrulLgI0bA+hNaW5sseqjVQzzR+gneXfAu2/dvp6DdE80rlgiMiZYSJdxZwNtvu5Hh5s6FO++EP/90rad8l6CGDHE3zu0gli2PXfQYj7R6hL2H97Jo0yJWbF/Bxr0b2XNoD2npYTYKyAU7d+5k+PDh2Vr2lVdeYf/+/bkc0THWasiY/EbVHfinTnVnDPPnu9dr13YjyF14IbRqBTWyPjhLrFJVkhclU756efYd3sehtGOlUUoklKBkQklKJZaiZGJJisYXjchIZqHKUIfDV3iuQoUKYc1vrYaMKehEoFEj93jkEdcSafp095gwAd70hp+sVs0lBV9iaNjQmqmGIPL/7Z17cFx1Fcc/ZzebF62haUottEIRsMKMhmqBUoIw0EKZWkBGwBEtqHQqMug4lZZhQPAxFh9V7IAOKoIMiDxEGVsljgTLu6UlhZYW2iJOY2sItUKzgSa7e/zjd9e92eymm2zu3k32fGZ+s/fe32/3nj25ud+9v8c5wrjqcRw94WgAkl+9Bn1xI0lNkkwlSWoKUPqABEJEokQjUaISJRqJIDkj5mTR3OzCpuRh+fLl7Ny5k+bmZubOncthhx3GAw88wIEDB7jwwgu5+eabicfjXHzxxXR0dJBMJrnhhhvo7Oxk9+7dnHnmmTQ1NdHW1jYyTvFhQmAY5c4RR8CSJa4kk/Dyy26q6lNPuZSi993n2jU2uumvaWH42MdcSJBiiMczayz27YMpU9w023IZnB8mUYlApIoqqiDqgqClNEUylSSlSZKapDeZmQYckQhRiRKRCCKCECEigkikEIkAYMWKFWzevJn29nZaW1t56KGHWLduHarKwoULWbt2LV1dXRx++OGsXr0acDGIGhoaWLlyJW1tbQU/EQwVEwLDGE1Eo+6XZ3OzG09QhddfzwjDk0+6JwdwN+uTT84Iw+zZrn16IZ2/+BfY+Uuu6JYiLprsjBkDS1PT6FgfkfXLXXDhD/x5whKpBPHeOPG+ON293cR74yR14JhCVaSK6mg1sUiM6v/+k1g0ltmPVhOLxohK/wxkra2ttLa2cuKJJwLQ3d3N9u3baWlpYenSpSxbtowFCxbQ0tIywl88NyYEhjGaEclEf738cness9OJQloYvvtdt75hMOrq+i+gO+GEzH66TJjguqm2bcuUtrb+YtHYmFsgpk8/eGTaMqMqUkVDbQMNtZlUrclUkt5kL33JPnpTvZntZC99qT7ifXESqcSAz4pIhK63uuhN9rJ7/27e63uPa5ddy1VfvmpA2w0bNrBmzRquu+465s2bx4033hjo9wQTAsMYe0yeDBdd5ArA/v3w7LNu0LmmZuANftKkoWWf85NKwa5d/cVh61ZYvdrNhkoTi7kupVmzXPfVnDlOIEbZmEY0EqUuUkddLH/XWEpT/cQhLRapQ1N0d3eze/9uZpwyg9t/cDszz5lJ06FNvN31Ng11DcQkxuRJk7nssssYN24cd911F5AJYW1dQ4ZhDI/x42HePFdGmkjEzV468kg455z+dfv2wauvZgRiyxZYswbuvtvVT5jg0qCmhWHWrFE/9gDu139NVc2AgHfTGqZxRssZXD7vcs6adxaXXHoJixYsIqUp6urr+Naqb7HrjV2s+s4qotEoNbEafrLqJ/Ql+1i8eDHz589nypQpgQwW2/RRwzBKh6pbJ/H005mybZuri8Vg5kwnCqed5kTCF7O/WMo56FwylaSnr4d4X5yevh56+np4L5HJUBaLxDik+hAa6xpprGs86OfZ9FHDMMoXETjuOFeuuMId27sXnnkmIwy33QYrV7q6Y47JPDGceqobjI5G85dR1tWUJhqJMr5mfL88zmlx8AvEgcSBQT5l+JgQGIYRLhMnwic/6Qq4AIEbN2aEwd+dVAjZwpDevv9+Fxk2FnMD1+ni3/dvhywqucQhqB4cEwLDMMqLmho31XX2bFi61HUn7dgBzz/vBr6TyaGXVArq69H6eiSRcGITjzthyHdzjUYHCkYs5tZmVFdntqPRkk2ZLWTF83DEwoTAMIzyRsTNODr22KI+pvYf/2Dv+PFMnDgxc0NVdUKRSLjcFolE7u3eXujpyZ3/IhIZKA4hiYWqsnfvXmpra4f0PhMCwzAqgqlTp9LR0UFXV9fQ35y+2cdimaeMRMK9HjgA3d2Z/VzZ7UTcU4VfDArZzt6vrz/oVN/a2lqmTp06hC9nQmAYRoUQi8WYPn168CdKJGDPHpeMaNeu/smJ3n3XdVOlu6v8XVeDvaa3r7zSdZeNMIEKgYicC9yKW7n9S1VdkVU/A/g1MBO4XlV/GKQ9hmEYgVNV5QICTpvmxjlGAYEJgYhEgduAuUAHsF5EHlXVV3zN/gNcA1wQlB2GYRjG4AQ5P+okYIeqvq6qvcD9wPn+Bqr6pqquB4rMQG4YhmEMlyC7ho4Advn2O4A8Gb8HR0QWA4u93W4ReXWYNjUBbw3zvaWg3O2D8rfR7CsOs684ytm+vJmMghSCXHOlhrUaQlXvAO4ozhwQkRfyLbEuB8rdPih/G82+4jD7iqPc7ctHkF1DHcA03/5UwLJxG4ZhlBlBCsF64FgRmS4i1cClwKMBns8wDMMYBoF1DalqQkSuBh7DTR+9U1W3iMgSr/7nIvJ+4AXgfUBKRL4GHK+q7wRkVtHdSwFT7vZB+dto9hWH2Vcc5W5fTkZdGGrDMAxjZBmdMVsNwzCMEcOEwDAMo8IZk0IgIueKyKsiskNElueoFxH5qVf/kojMLKFt00SkTUS2isgWEflqjjZniMjbItLuleCzV/c//xsi8rJ37gHp4EL234d8fmkXkXe8sSV/m5L7T0TuFJE3RWSz71ijiPxVRLZ7rxPyvHfQ6zVA+34gItu8v+EjInJonvcOej0EaN9NIvIv39/xvDzvDct/v/PZ9oaItOd5b+D+KxpVHVMFNzC9EzgaqAY24Qag/W3OA/6MW+twCvB8Ce2bAsz0tscDr+Ww7wzgTyH68A2gaZD60PyX42/9b+DIsP0HnI6LmbXZd+z7wHJvezlwS57vMOj1GqB984Aqb/uWXPYVcj0EaN9NwNICroFQ/JdV/yPgxrD8V2wZi08EBw1t4e3/Rh3PAYeKyJRSGKeqe1R1o7e9H9iKW4U9mgjNf1mcBexU1X+GcO5+qOpaXOwsP+cD6dRad5M7plYh12sg9qlqq6omvN3ncGt9QiGP/wohNP+lEZfc4GLgtyN93lIxFoUgV2iL7BttIW0CR0SOAk4Ens9RPVtENonIn0XkhJIa5laAt4rIBi+8RzZl4T/c2pR8/3xh+i/NZFXdA+4HAHBYjjbl4ssv4J7ycnGw6yFIrva6ru7M07VWDv5rATpVdXue+jD9VxBjUQgKCW0xYuEvhouIjAMeBr6mA9dNbMR1d3wUWAX8oZS2AXNUdSYwH/iKiJyeVV8O/qsGFgIP5qgO239DoRx8eT2QAO7N0+Rg10NQ/Az4INAM7MF1v2QTuv+AzzD400BY/iuYsSgEhYS2CDX8hYjEcCJwr6r+PrteVd9R1W5vew0QE5GmUtmnqru91zeBR3CP337KIXzIfGCjqnZmV4TtPx+d6S4z7/XNHG3CvhYXAQuAz6rXoZ1NAddDIKhqp6omVTUF/CLPecP2XxXwKeB3+dqE5b+hMBaFoJDQFo8Cn/dmv5wCvJ1+hA8arz/xV8BWVV2Zp837vXaIyEm4v9PeEtl3iIiMT2/jBhQ3ZzULzX8+8v4KC9N/WTwKLPK2FwF/zNEmtFAs4hJHLQMWqmpPnjaFXA9B2ecfd7owz3nDDmVzNrBNVTtyVYbpvyER9mh1EAU3q+U13GyC671jS4Al3rbgkubsBF4GPl5C207DPbq+BLR75bws+64GtuBmQDwHnFpC+472zrvJs6Gs/Oedvx53Y2/wHQvVfzhR2oPLrdEBfBGYCPwN2O69NnptDwfWDHa9lsi+Hbj+9fR1+PNs+/JdDyWy7x7v+noJd3OfUk7+847flb7ufG1L7r9ii4WYMAzDqHDGYteQYRiGMQRMCAzDMCocEwLDMIwKx4TAMAyjwjEhMAzDqHBMCAwDEJHveVFLLwgqgmUBNjwhIqMu8bkx+jEhMAzHybiYT58AngzZFsMoKSYERkXjxeR/CZgFPAt8CfiZ5MhhICKTRORhEVnvlTne8ZtE5B4ReVxc7oErvePiff5mLx79Jb7PutY7tklEVvhO82kRWScir4lIS6Bf3jA8AktebxijAVX9hog8CHwO+DrwhKrOydP8VuDHqvqUiHwAeAz4sFf3EVxuhkOAF0VkNTAbFzDto0ATsF5E1nrHLgBOVtUeEWn0naNKVU8Sl4Tlm7gQBoYRKCYEhuFCgbcDM4BXBml3NnC8F8YI4H3pODLAH1X1XeBdEWnDBRY7DfitqiZxAej+jnvy+ATwa/Xi+6iqP859OgjhBuCoYr+YYRSCCYFRsYhIMy5WzFTgLVwMI/FSDs72bux+IrmOe8KQHatFyR0iGe94vtguB7zXJPb/aZQIGyMwKhZVbVfVZrx0ocDjwDmq2pxDBABacQHtgP8LSZrzRaRWRCbiUmWuB9YCl4hIVEQm4dIdrvM+5wsiUu99jr9ryDBKjgmBUdF4N+h96mLez1DVwbqGrgE+7mXMegUX8TTNOmA1Ltrpt9XFoH8EFzlzE05krlXVf6vqX3DRNF/wnj6WjvgXM4whYNFHDaNIROQmoFtVfxi2LYYxHOyJwDAMo8KxJwLDMIwKx54IDMMwKhwTAsMwjArHhMAwDKPCMSEwDMOocEwIDMMwKpz/AalFSMhH4thnAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_model_loss([idx_sorted[-1-i] for i in range(3)],\"Best cases\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Plot the loss per epoch for the worst three cases**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                    Worst cases\n",
      "Model: \"model_38\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "input_39 (InputLayer)        [(None, 16, 21)]          0         \n",
      "_________________________________________________________________\n",
      "conv1d_24 (Conv1D)           (None, 16, 98)            12446     \n",
      "_________________________________________________________________\n",
      "max_pooling1d_8 (MaxPooling1 (None, 8, 98)             0         \n",
      "_________________________________________________________________\n",
      "dropout_20 (Dropout)         (None, 8, 98)             0         \n",
      "_________________________________________________________________\n",
      "conv1d_25 (Conv1D)           (None, 8, 114)            67146     \n",
      "_________________________________________________________________\n",
      "max_pooling1d_9 (MaxPooling1 (None, 4, 114)            0         \n",
      "_________________________________________________________________\n",
      "dropout_21 (Dropout)         (None, 4, 114)            0         \n",
      "_________________________________________________________________\n",
      "conv1d_26 (Conv1D)           (None, 4, 4)              2740      \n",
      "_________________________________________________________________\n",
      "dropout_22 (Dropout)         (None, 4, 4)              0         \n",
      "_________________________________________________________________\n",
      "up_sampling1d_8 (UpSampling1 (None, 8, 4)              0         \n",
      "_________________________________________________________________\n",
      "conv1d_27 (Conv1D)           (None, 8, 114)            2850      \n",
      "_________________________________________________________________\n",
      "dropout_23 (Dropout)         (None, 8, 114)            0         \n",
      "_________________________________________________________________\n",
      "up_sampling1d_9 (UpSampling1 (None, 16, 114)           0         \n",
      "_________________________________________________________________\n",
      "conv1d_28 (Conv1D)           (None, 16, 98)            67130     \n",
      "_________________________________________________________________\n",
      "dropout_24 (Dropout)         (None, 16, 98)            0         \n",
      "_________________________________________________________________\n",
      "conv1d_29 (Conv1D)           (None, 16, 21)            2079      \n",
      "=================================================================\n",
      "Total params: 154,391\n",
      "Trainable params: 154,391\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "Model: \"model_49\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "input_50 (InputLayer)        [(None, 16, 21)]          0         \n",
      "_________________________________________________________________\n",
      "conv1d_90 (Conv1D)           (None, 16, 186)           23622     \n",
      "_________________________________________________________________\n",
      "max_pooling1d_30 (MaxPooling (None, 8, 186)            0         \n",
      "_________________________________________________________________\n",
      "dropout_75 (Dropout)         (None, 8, 186)            0         \n",
      "_________________________________________________________________\n",
      "conv1d_91 (Conv1D)           (None, 8, 145)            161965    \n",
      "_________________________________________________________________\n",
      "max_pooling1d_31 (MaxPooling (None, 4, 145)            0         \n",
      "_________________________________________________________________\n",
      "dropout_76 (Dropout)         (None, 4, 145)            0         \n",
      "_________________________________________________________________\n",
      "conv1d_92 (Conv1D)           (None, 4, 4)              3484      \n",
      "_________________________________________________________________\n",
      "dropout_77 (Dropout)         (None, 4, 4)              0         \n",
      "_________________________________________________________________\n",
      "up_sampling1d_30 (UpSampling (None, 8, 4)              0         \n",
      "_________________________________________________________________\n",
      "conv1d_93 (Conv1D)           (None, 8, 145)            3625      \n",
      "_________________________________________________________________\n",
      "dropout_78 (Dropout)         (None, 8, 145)            0         \n",
      "_________________________________________________________________\n",
      "up_sampling1d_31 (UpSampling (None, 16, 145)           0         \n",
      "_________________________________________________________________\n",
      "conv1d_94 (Conv1D)           (None, 16, 186)           162006    \n",
      "_________________________________________________________________\n",
      "dropout_79 (Dropout)         (None, 16, 186)           0         \n",
      "_________________________________________________________________\n",
      "conv1d_95 (Conv1D)           (None, 16, 21)            3927      \n",
      "=================================================================\n",
      "Total params: 358,629\n",
      "Trainable params: 358,629\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n",
      "Model: \"model_41\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "input_42 (InputLayer)        [(None, 16, 21)]          0         \n",
      "_________________________________________________________________\n",
      "conv1d_42 (Conv1D)           (None, 16, 153)           19431     \n",
      "_________________________________________________________________\n",
      "max_pooling1d_14 (MaxPooling (None, 8, 153)            0         \n",
      "_________________________________________________________________\n",
      "dropout_35 (Dropout)         (None, 8, 153)            0         \n",
      "_________________________________________________________________\n",
      "conv1d_43 (Conv1D)           (None, 8, 181)            166339    \n",
      "_________________________________________________________________\n",
      "max_pooling1d_15 (MaxPooling (None, 4, 181)            0         \n",
      "_________________________________________________________________\n",
      "dropout_36 (Dropout)         (None, 4, 181)            0         \n",
      "_________________________________________________________________\n",
      "conv1d_44 (Conv1D)           (None, 4, 4)              4348      \n",
      "_________________________________________________________________\n",
      "dropout_37 (Dropout)         (None, 4, 4)              0         \n",
      "_________________________________________________________________\n",
      "up_sampling1d_14 (UpSampling (None, 8, 4)              0         \n",
      "_________________________________________________________________\n",
      "conv1d_45 (Conv1D)           (None, 8, 181)            4525      \n",
      "_________________________________________________________________\n",
      "dropout_38 (Dropout)         (None, 8, 181)            0         \n",
      "_________________________________________________________________\n",
      "up_sampling1d_15 (UpSampling (None, 16, 181)           0         \n",
      "_________________________________________________________________\n",
      "conv1d_46 (Conv1D)           (None, 16, 153)           166311    \n",
      "_________________________________________________________________\n",
      "dropout_39 (Dropout)         (None, 16, 153)           0         \n",
      "_________________________________________________________________\n",
      "conv1d_47 (Conv1D)           (None, 16, 21)            3234      \n",
      "=================================================================\n",
      "Total params: 364,188\n",
      "Trainable params: 364,188\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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Srl3jRVWd5KX/jOtB8x1OTrkmrgH4dKmA6466FzcH8q+EngfifVwIaT7wO87xB3InLqS1BPdsfYGLd58uqXNL7BCR1LabzB4r0mf4dLkEd08n4GpMB3FOC6+X15bUxUvb77W3gGtAnomrsc8AFuE6fOQ5TGvIMAwjn2M1AsMwjHyOOQLDMIx8jjkCwzCMfI45AsMwjHyOOYIsRAJ0frL5uLeJyKTsPq4ROZKLFDuN/Ic5gjyAJ3p2ud92wOnNeZBOmX1EZIs4gbIPRSRst1OvT/9cETng/W0YaVnihN42ixPpWy4BUtre2IYvxAkEqoTR4sltiBP8ez7jnJkqM0udnteP/2VxInw7xI20DjuwS0Taisif3jPwizg9oYjKEicO+Iu375+SVgwyz8pSmyPI4XgPbo64T+LDxDMi0gHXx78tUA03UO25MHnjcQN+RgGlcNpK41IHEEVQ1ktANVUtDlwLPC8ijQLSp+PGGgRKQ5w2flzXU8UnW3sAnYEGOMHCq4H7Q2UUp+D6FW5sTmmccGCghERGZY0B/sBpPQ3AjSEpG5C+yZtXInWJxtwQ2Y+q2pJFC250ZE3vd0HgX8A63EjMYUBhL60UbrTtdtzgm29xE4KkljMFbxAZbpBLTa/snsAKb5+3OTEOpBtuRGugHeHyxgJDcZPMrMYNOlMgLsw5rQGewI2mPYyTJekP/AUk4QYQXeflrYNTOz0K7MNJOKd7LSK4pqNxg7hS19sCW8LkvRzYmHqu3rZ1wBWnUNZ5OAmHLiHSNgCXZmB3ddxAsCSc8upbOEVWcE5IgXs9+6biPsqexukFbcOJ3ZUIyt8DNyBvM/BowLEKAq97aZu83wVDPRuBz6lXXjJwxLtf36TzXD/kPU+rvW1v4KQl9gJzgdbe9iu88pK9Mud720vgJDI2e/foeSA2wmdgBtAjYP1eYFaYvD2AGQHrRXD/Q7UzKgs3yvgwTn8oNX0a0NP7fSlOgdf3d01WLzniSzOP8jInhq/XxAnMDfTSYnCjfM/mxGjHYAncO3APdTFOiIldjRuR2QCnPNkhneOHy3sfTimyIW7kZOeQe6elK05BNVUK+y+c1EIJ3Bf1KBE5S50Wf09gprqvpZIRXIuMqIcbOZvKfKC8iJwZJu8C9f5rPRZ42yMqS0TeEZEDOH2mzbgRqafCaNwLsgzwT9LKe6TSBuc8O+Be2N1wsszn4EaEBz8TlwHn4hxe/4CwxQCcJHZD3P1uijc6Oj1U9T3gf8AQ736lp4bbGSclXddbT/COV9o7189FpJCqfg+8CHzqldnAy/9fnPZVTZxs9uVApLPYhbpv9SLJq04p9i/SfwYC01apGwkf7ljlxM1Et1rc/ApFIjyHHI05gijgxRzvA/qo6k7vwXoRT5ZXVXeo6peqesBLewH3UgjkI1VdrE62N9nbNlhVd6vTu/+FE7LFoQiXtwvwhqpuUNVdOP2UjPi3Oqnjg579n6vqJnWqjJ/ivhSbnsq1iICiwJ6A9dTfxSLIm5q/WJj0k8pS1Qe99da4EMPhCO08johUxTnhZ9RNWDMV+CZE1kHqZJsP4tRYX1XVVeokkZ/E6U8FhmKe8/IvxH1IdPW23wb8Q1W3qep2nHO+I7N2Z8BL3v1LfQZGec9xiqoOxdVKwk1tebqS2KHuW9Ew7QSn8gyklpXRvnlWljrXxCZzGWVxaqJzA9uhcGEZROQM3D/CFbgwEUAxSTtFY7BsL4SXLQ5FuLzB0sWhjhNMmjwicidudq5q3qaiuC/fUKR7LSJgH1A8YD31d1IEeVPzJ4VJD1mWdw+mi8jtwAM4zfrMUBHYpWknMFnLyeqpgdc1WKp8Le7/s3yY/GuB89PZt2Imbc6I4GfgUdwXfUVc6Kg44Z+BQJnq1G0xwWWmQ6j7ti+o5hcub2r+9J6Bfaqqnu5R2H3V6RGl/l+tFpHHcTpTIdsrchNWI4gOf+PCPfVUtaS3lFDV1Jfxo7ivp2bqGiYv8bYHfuFESwRqM2nn1Y1E2vm4LV4PjPdxbQtneuGfRZywPdjujK5FRizGhTtSaQBs1dAzuy0GLgj6UrzA257ZssC9iGuESUuPzUCpoLBB1RD5Aq9VsFR5VVwoZWvAtmDJ6U3p7JualkbiXESC55uI9DkLfAZa49qNugClvGdgD+GfgUCZ6tRnoLiqhgvvBBPqvi2OJK93D2qQ/jMQmHaOiBQLkx5MnpGlNkcQBVT1GO5l+ZqIlAM3EbrXawVcVfMgbh7i0sCz2WjeZ8DDnj0lcf/QmaEI7h9gO4CI3A3UD0jfClRO7akTwbXIiJHAvSJSV5zU8tM4JdVQTME1VPcWkYIi0svb/nNGZYlIORG5RUSKipsLuAMu9JK6L16ZhbzVeHGSxie9CFR1La63ynNet9NWZDwb3Rigj4hUF5GinIizB+r7PyNOUrweTir504B9nxaRsl6vmYGcUCudD9QT1622EE5NNZBgyfBIKIZzUtuBOBEZSNov6dOWqQ5iJNDXe24q4j6kPgqT92ugvojc4J3vQFy7UeqcHGHLUtXlOEn4Z717ex3uQ+JLyOOy1H63VuelhbS9hgrh/plXcUJOuLeXVhH30toHLMdVLY/33PHSuocr21v/CHje+92Nk3sNhcsbhwtL7cD1GuqD6+EhYc5pDdAuaNsLwE7c1/6ruN4x3b20eFx1eSfwd0bXIsLr2hf3ctmLi40XDEibCDwVsH4hrpH2IE6a+cJIysKFsH4FdntpC4H7QlwLDVqqhbH5HFyPk32E7zUUF5A/BvfSWo97wY7CfW0H5k/tNbQFeDxg30K48NVmb/k3UCggfYB3r9bjur8GPqfn4l5+u4GxGT3X3nosrgfQXu94jwc+J7iul9NxPdZ+97aVAN7F9bjag+uieUuE919wM8Xt9JYhpO0Zthi4LWC9HS6efxD3v1QtE2VV8/Y5iJsMqF1AWl9cj6cD3rV8k4AeRrl5MRnqfI6IdASGqerZGWY2fEHcLGWrgQKa9bONGYaFhvIbIlJYRK4UNzNXJVxY6mu/7TIMwz/MEeQ/BNe9cBeuer6UyPv0G4aRB7HQkGEYRj7HagSGYRj5HHMERo5HRKZIgBJoUFpVTwUy0gFqkR4zKuUaRk7EHIGRq1HVdeo0bY5mnDs84uSlj0sOZ1W5ER47YpntgH3uEieH3T1o+zki8q2IJInI3yIyJCBtiogckhMSysuC9u0iIku9fZeISCQ6VEYewByBYfiIZEJmO2CfUjgtosVB2+NxYxZ+BirgRpAHzwvQS09IKJ8XsG8lL29f3OCwfsDo1EGARt7GHIGRIxCRliKS4H0VJ4hIy6AsNURkjpc+zhuRnTqRiIonziYiJUTkA3ETzGwUkecDwzsicl/QV+9FIvIxTpbhG+9L+fHAcr0Rx4lB9vYRkfHe74Ii8i8RWSdOmXKYiBSO8NTvAj5QJzC4C6dU2i2DfV7CDRr7O2h7N5xe/qvqxN0OqeqCCO2ojJMNn6iO73DyFKcisWHkMswRGL7jvdS/w73czsSNVv5O0kpN3wncgxuVnUJ4IbiwcscichNOYuFO3FfvtcAOVb0DNy/ANd6X8pCgMscD54nIuQHbbsXJL0P2yWwjIk2Bxrg5HYJpDqwRkYleWGiKiJwflOclL+03STvLWiKwVESuFSex0RmnDxSpIzFyM34PbbbFFpxk8pygbTOBbt7vKThZ7dS0urjJT2IJkGvAKXUeJmDSG5xe0C/e7x+Ah8PYsIa0cgLHy/XWRwEDvd/n4hQpz8CNy9gP1AjYtwXeBC4RnPtfeBPneOsFCCNd4Z1vItAi4Lp0D0ifhJML6YiT+uiHk/WI99Kb4XSCCuJqIklBdt+Lk8RIwckoXOX3s2FL9ixWIzByAsEyynjrlQLWgyWYC3Cy7HGg3PFuEdkN/AdIjXNXwb14T4XRnND/vxWny3OAtDLbqcf83tseCZmR2X4QJ6A2M0xZB3GaUxNV9QhuVrgzcZPfoKqzVTVJ3RwJ/8XNgHclgNdQPgQ3C1c8bn6M4RI057ORNzFHYOQEgmWUwcXsNwasB0swJ3NyjDwjueP1hI95ZzSychJQxnsxduVEWCg7ZbbbAtd5PYy2AC1xip6pM5ktiOA8AgmUUW4ITFXVRHUTDiUAs3ECbkZex+8qiS224L5ad+O+tOOAm731Ml76FJxqZV3c1/fnwGgvrRppQzjjcPPpFsd96NQA2nhpN+GcQSPcC7AmcLaXNou0c9mmKdfb9i6uV862oO1v4OS9y3nrlYAOEZ77FTg10bq4SYp+JiAMFpS3JK43UOoyA9fLJ3Vu4/NwIZ12uDBSH1wNKN7btwNOqTQON6vZfuA8b982OKfW0Fu/EKdQe7nfz4ct0V98N8AWW1QVoBVOPnqP97dVQNoUXE+ZOTjp428CnESwI0hX7hg3p/IyXEhmEZ5MNdAJ12C8G3gsjCNo7W17O8j2bJPZDtpvCifLlV8PrPTKmoKrqYALVSXgQk67cY6vfdC+vbx9k7xzedTv58KW7FlMa8jI1YjIObg5k+PUHmbDOCWsjcDI7dQH1pgTMIxTJ6qOQESuEJFlIrJSRPqHSC8lIl+LyAJvsFD9UOUYRihEpC/wHm5krmEYp0jUQkPeaM7lQHtcvDYB6KqqSwLyvALsU9XnRKQ2LvbaNioGGYZhGCGJZo2gKbBSVVep69P8Ca5BLpC6wE8A6iaXriYi5aNok2EYhhFEXBTLrkTaQUAbcCMbA5mP6+Uw3Rs6fzZO82RrYCYR6YGbuJsiRYo0ql27drRsNgzDyJPMnTv3b1UNOdAxmo5AQmwLjkMNBt4QkXnAQlxXv5Mm51bV93CxYBo3bqyJiYnBWQzDMIx0EJHg0fvHiaYj2EDa0aCVcSNIj6Oqe4G7AUREgNXeYhiGYWQT0WwjSADOFZHqnk76LTgVx+OISEkvDZxC5FTPORiGYRjZRNRqBKqaIiK9cIqPscCHqrpYRHp66cNwYlgjReQosASnfmgYhmFkI9EMDaGqE4AJQduGBfyeiZP0NQzDMHzCRhYbhmHkc8wRGIZh5HPMERiGYeRzzBEYhmHkc/KVIziccthvEwzDMHIc+cYRjPtzHFVeq8LGvRszzmwYhpGPyDeO4ILyF7Dr0C5enPai36YYhmHkKPKNI6heqjr3NLyH939/n3V71vltjmEYRo4h3zgCgAGXDEBEeGHqC36bYhiGkWPIV46gaomq3HfRfXw470NW7VrltzmGYRg5gnzlCACeav0UsRLL81Of99sUwzCMHEG+cwQVi1XkgcYPMHL+SFbsWOG3OYZhGL6T7xwBwBOtniA+Np5/TP2H36YYhmH4Tr50BBWKVqBX016MXjiapduX+m2OYRiGr+RLRwDQr2U/CscVtlqBYRj5nnzrCMoWKUvvZr35dNGnLNq2yG9zDMMwfCPfOgKAx1o+RtH4ogyaMshvUwzDMHwjXzuC0oVL06d5H75c+iXztszz2xzDMAxfiKojEJErRGSZiKwUkf4h0kuIyDciMl9EFovI3dG0JxR9WvShZKGSPDvl2ew+tGEYRo4gao5ARGKBt4GOQF2gq4jUDcr2ELBEVRsAlwJDRSQ+WjaFomShkjza4lHGLxtP4qbE7Dy0YRhGjiCaNYKmwEpVXaWqR4BPgE5BeRQoJiICFAV2AilRtCkkvZv1pnTh0lYrMAwjXxJNR1AJWB+wvsHbFshbQB1gE7AQeFhVj0XRppAUL1icx1s+zoQVE5i5fmZ2H94wDMNXoukIJMQ2DVrvAMwDKgINgbdEpPhJBYn0EJFEEUncvn171lsKPNT0IcqeUdZqBYZh5Dui6Qg2AFUC1ivjvvwDuRv4Sh0rgdVA7eCCVPU9VW2sqo3Lli0bFWOLxhfliYuf4MdVPzJt7bSoHMMwDCMnEk1HkACcKyLVvQbgW4DxQXnWAW0BRKQ8cB7gmz70A00eoELRCgycMtAvEwzDMLKdqDkCVU0BegE/AEuBz1R1sYj0FJGeXrZ/Ai1FZCHwE/CEqv4dLZsy4owCZ/BkqyeZsmYKv6z+xS8zDMMwshVRDQ7b52waN26siYnR6+Z5KOUQNf9dk+qlqjO121RchybDMIzcjYjMVdXGodLy9cjiUBSKK8SA1gOYvijn2SwAACAASURBVG46P6760W9zDMMwoo45ghDcc+E9VC1RlYG/DCS31ZgMwzAyizmCEBSMK8jTrZ9m9sbZTFw50W9zDMMwooo5gjB0a9iN6iWrW63AMIw8jzmCMBSILcDANgOZu3ku45cF93o1DMPIO5gjSIfbL7idc0ufy7NTnuVY9itfGIZhZAvmCNIhLiaOZ9s8y/yt8/lq6Vd+m2MYhhEVzBFkwC31b6FOmTo8O+VZjh476rc5hmEYWY45ggyIjYll0KWDWLJ9CZ8v+dxvcwzDMLIccwQRcGPdG6lfrj6Dpgwi5Vi2T5dgGIYRVcwRRECMxPDC/73Ash3LuHf8vdZwbBhGnsIcQYRce961/POyfzJy/kh6TehlYwsMw8gzxPltQG5iQOsBJB1OYsiMIRSLL8bgdoNNlM4wjFyPOYJMICIMbjeYfUf2OWdQsBhPX/K032YZhmGcFuYIMomI8OaVb7I/eT/P/PIMReOL8kjzR/w2yzAM45QxR3AKxEgMw68dzr4j++jzQx+Kxhel+0Xd/TbLMAzjlLDG4lMkLiaO0TeMpmPNjvT4pgdjFo7x2yTDMIxTwhzBaRAfG8+XXb6kTbU23PH1HYz7c5zfJhmGYWQacwSnSeEChRl/y3gaV2xMly+68ONfNquZYRi5i6g6AhG5QkSWichKEekfIr2fiMzzlkUiclRESkfTpmhQrGAxJt42kTpl6tDpk05MXzfdb5MMwzAiJmqOQERigbeBjkBdoKuI1A3Mo6qvqGpDVW0IPAn8qqo7o2VTNClVuBST7phE1RJVuWr0VczdNNdvkwzDMCIimjWCpsBKVV2lqkeAT4BO6eTvCuTqFtdyRcox+c7JlC5cmstHXc6ibYv8NskwDCNDoukIKgHrA9Y3eNtOQkTOAK4AvgyT3kNEEkUkcfv27VluaFZSuXhlJt8xmUJxhWj/cXtW7Fjht0mGYRjpEk1HEEp7IZxAzzXAb+HCQqr6nqo2VtXGZcuWzTIDo0WN0jWYfMdkUo6l0O7jdqzbs85vkwzDMMISTUewAagSsF4Z2BQm7y3k8rBQMHXK1mHS7ZPYc2gPbUe2Zcu+LX6bZBiGEZJoOoIE4FwRqS4i8biX/UmzwItICaANkOc64V941oVMvG0im5M20/7j9uw4sMNvkwzDME4iao5AVVOAXsAPwFLgM1VdLCI9RaRnQNbrgEmquj9atvhJiyotGN91PCt2rOCK/13B3wf+9tskwzCMNEhu09Vv3LixJiYm+m1Gpvl2+bfc+NmNlC9ani9u+oImlZr4bZJhGPkIEZmrqo1DpdnI4mzi6lpXM/0eN9Cs1YhWDP99uM8WGYZhOMwRZCONKzZmbo+5tDm7Dfd9cx/3jb+PQymH/DbLMIx8jjmCbKbMGWWYeNtEnmr1FMP/GE7rEa2te6lhGL5ijsAHYmNieaHtC3x989cs37Gci/5zEZNXTfbbLMMw8inmCHykc+3OJNyXQIWiFegwqgODpw8mtzXeG4aR+zFH4DO1zqzFrO6zuKnuTTz505Pc8NkN7D2812+zDMPIR5gjyAEUjS/KmBvG8FqH1xi/bDxN3m/Cku1L/DbLMIx8gjmCHIKI8EjzR/j5rp/Zc2gPTd9vyueLP/fbLMMw8gHmCHIYl5x9CXN7zOWC8hfQ5YsuPDbpMVKOpfhtlmEYeRhzBDmQSsUrMaXbFB5q8hBDZw6l3ch2bN231W+zDMPIo5gjyKHEx8bz1pVvMbLzSOZsnEOj9xoxa8Msv80yDCMPYo4gh3NHgzuYee9MCsYV5OIPL6bFBy146qen+PGvHzmQfMBv8wzDyAOY6FwuYdfBXbw26zUmr5pMwqYEUo6lUCCmAM0qN+OyapdxWbXLaFGlBYXiCvltqmEYOZD0ROfMEeRC9h3Zx/R10/ll9S/8suYX5m6eyzE9RsHYgrSo0uK4Y2haqSkF4wr6ba5hGDkAcwR5nD2H9jBt3bTjjmHelnkoSuG4wlxc9WIuPftSLqt+GU0qNqFAbAG/zTUMwwfMEeQzdh7cydS1U487hoXbFgJQomAJRl0/iqtrXe2zhYZhZDfmCPI52/dv59e1vzJ4+mDmb53PqOtGcXP9m/02yzCMbMQmpsnnlC1Slhvr3sjPd/1Myyot6fplV5sYxzCM40TVEYjIFSKyTERWikj/MHkuFZF5IrJYRH6Npj35neIFizPxtol0qNmB+765j9dmvua3SYZh5ACi5ghEJBZ4G+gI1AW6ikjdoDwlgXeAa1W1HnBTtOwxHGcUOINxt4zjxro30ndSX56b8pxJXxtGPicuimU3BVaq6ioAEfkE6AQEymreCnylqusAVHVbFO0xPOJj4xlzwxiKxhdl0K+D2Ht4L/+6/F+IiN+mGYbhA9F0BJWA9QHrG4BmQXlqAQVEZApQDHhDVUcGFyQiPYAeAFWrVo2KsfmNuJg4Prj2A4rHF+fVWa+y9/Behl09jNiYWL9NMwwjm4mmIwj1eRkcg4gDGgFtgcLATBGZparL0+yk+h7wHrheQ1GwNV8SIzG8fsXrFCtYjBemvUDSkSQ+vu5jG2tgGPmMaDqCDUCVgPXKwKYQef5W1f3AfhGZCjQAlmNkCyLC8//3PMULFueJyU+wP3k/n9/0uUlVGEY+Ipq9hhKAc0WkuojEA7cA44PyjANai0iciJyBCx0tjaJNRhgev/hx3rnyHb5b/h1Xjb6KfUf2+W2SYRjZRNQcgaqmAL2AH3Av989UdbGI9BSRnl6epcD3wAJgDjBcVRdFyyYjfR5o8gAjrxvJr2t+pf3H7dl1cJffJhmGkQ3YyGLjJMb+OZabv7iZ2mVqM+n2SZQvWt5vkwzDOE1sZLGRKTrX7sy3Xb9l5c6VXPLRJazbs85vkwzDiCLmCIyQtK/Rnkm3T2LLvi20HtGaFTtW+G2SYRhRwhyBEZaLq17MlLumcCD5AK1HtGbh1oV+m2QYRhQwR2Cky4VnXcjUblOJjYmlzUdtWLlzpd8mGYaRxZgjMDKkTtk6TLt7GsnHkun3Yz+/zTEMI4sxR2BExDmlzuHJVk8y9s+x/LrGRGIzy44DO2g9ojXT1k7z2xTDOAlzBEbE9GnehyrFq9B3Ul+O6TG/zclVfPDHB0xfN527x93NgeQDfptjGGkwR2BETOEChXmp7Uv8vvl3Ri0Y5bc5uYajx47ybuK7VCtZjb92/cWgKYP8Nskw0mCOwMgUXc/vSpOKTXjqp6fsyzZCJq6cyJrda3il/St0v7A7Q2cO5ffNv/ttlmEcxxyBkSliJIZXO7zKxqSNDJ0x1G9zcgXvJLxDxWIV6XReJ4a0H0K5IuW4d/y9JB9N9ts0wwDMERinQKuqrbihzg0M/m0wm5KCBWWNQP7a+Rffr/yeHhf1oEBsAUoVLsVbHd9i3pZ5vDbLpgo1cgbmCIxT4uV2L5N8NJlnfn7Gb1NyNMMS3WQ/9zW67/i26+tcT+fanXl2yrM2LsPIEZgjME6JGqVr0LtZb0bMG8H8LfP9NidHcjD5IB/O+5Dral9HxWIVj28XEd7q+BbxsfH0+KaHzRlt+E5EjkBEiohIjPe7lohcKyI2jVU+Z0DrAZQqXIq+k/rayywEnyz6hJ0Hd/JQk4dOSqtUvBJD2g3hlzW/MGLeCB+sM4wTRFojmAoUEpFKwE/A3cBH0TLKyB2UKlyKQW0G8fPqn/luxXd+m5PjeCfxHeqVrcclZ18SMv2+RvfRumprHp30KFv2bclm6wzjBJE6AlHVA8D1wJuqeh1QN3pmGbmFno17UuvMWvT7sZ/1gglgzsY5JG5K5MEmDyISavpu1wPr/Wve52DyQXpP7J3NFhrGCSJ2BCLSArgNSP30i+Z8x0YuoUBsAV5p/wp//v0n7819z29zcgzvJLxD0fii3H7B7enmO6/MeQxsM5DPl3zOuD/HZZN1hpGWSB3BI8CTwNfedJPnAL9EzywjN3FNrWu4rNplPDvlWXYf2u23Ob7z94G/+WTRJ9x5wZ0UL1g8w/z9Wvbj/HLn8+CEB9lzaE82WGgYaYnIEajqr6p6raq+7DUa/62qGdZlReQKEVkmIitFpH+I9EtFZI+IzPOWgadwDobPiAhDLx/KzoM7eXHai36b4zsf/vEhh48e5sEmD0aUv0BsAYZfO5wt+7bQf/JJ/yaGEXUi7TU0WkSKi0gRYAmwTETS1SMWkVjgbaAjrj2hq4iEaleYpqoNveUfmbTfyCFceNaF3NXwLt6Y/Qardq3y2xzfOHrsKMMSh9Hm7DbUK1cv4v2aVmrKw80eZtjcYaZQamQ7kYaG6qrqXqAzMAGoCtyRwT5NgZWqukpVjwCfAJ1O2VIjx/P8Zc8TFxOXr79qv1/5Pat3rw7ZZTQj/nnZP6lWshr3fXMfh1IORcE6wwhNpI6ggDduoDMwTlWTgYw6jlcC1gesb/C2BdNCROaLyEQRCfkJJSI9RCRRRBK3b98eoclGdlOpeCX6tezH50s+Z8b6GX6b4wvvJL7DWUXPonPtzpnet0h8EYZdNYxlO5bxwtQXomCdYYQmUkfwH2ANUASYKiJnA3sz2CdUn7lg5/E7cLaqNgDeBMaGKkhV31PVxqrauGzZshGabPhBv5b9OKvoWfT5oU++m7Ng1a5VTFwxkR6NnK7QqdChZgfuuOAOBv822OaINrKNSBuL/62qlVT1SnWsBS7LYLcNQJWA9cpAGoUyVd2rqvu83xNwNY8ykZtv5DSKxBfhxbYvMmfjHD5d9Knf5mQr7ya8S4zE0KNRj9Mq59UOr1KyUEm6f9Odo8eOZpF1hhGeSBuLS4jIq6nhGREZiqsdpEcCcK6IVBeReOAWYHxQuRXEG20jIk09e3Zk+iyMHMWdDe6kYYWG9P+pPweTD/ptTrZwXFeoTlpdoVOhzBlleOOKN5izcQ5vzXkriyw0ciu7D+1m6tqpvDn7TaaunRqVY0Q6KOxDYBHQxVu/AxiBG2kcElVNEZFewA9ALPChNwahp5c+DLgReEBEUoCDwC1qojW5nhiJYejlQ2k7si1vzH6D/q3yfuPxp4s/DasrdCp0rd+VUQtGMeDnAXSq3YlqJatlSblGzuWYHmP1rtXM3zqf+VvmM3/rfOZtmcfaPWuP5+nbvG9YyZLTQSJ574rIPFVtmNG27KBx48aamJiY3Yc1ToFOn3Til9W/sLL3SsoVKee3OVGl6ftN2Z+8n0UPLAorKZFZ1u1ZR92369Kqaism3jYxy8o1Mo+q8m7iu2zYu4FShUpRqnCpkH+LFSxGjGQcaDmQfICFWxemeekv2LqApCNJgPuYqnVmLRqUb+CWCg1oWKEhZxU965SfAxGZq6qNQ6VFWiM4KCKtVHW6V+DFuC94wwjLkHZDqP9ufZ795Vnevfpdv82JGgkbE0jYlMBbHd/K0pd11RJVeantS/T+vjejF47mtgtuy7Kyjczx1py36P19b2IkJt1OEDESQ8lCJY87h+O/C5WiRKESrN2zlvlb5rNi54rj5RSLL0aDCg24s8Gdx1/69cvV54wCZ2TX6UVcI2gAjARKeJt2AXep6oIo2hYSqxHkLnpP7M3bCW+zoOeCTA2wyk10G9uNL5d+yca+GyOSlMgMR48dpdWIVqzYsYKlDy2lbBHrNZfd/LH5D5p/0JzLa1zOuFvGsf/IfnYd2sWug7vC/t19ePdJ23cf2k2lYpVoUKFBmi/9aiWrRVSLOF3SqxFE5AgCCioOrrePiDyiqq9nkY0RY44gd7HjwA5qvlmT5pWbM/G2iX6bk+XsOLCDSq9W4p4L7+Gdq96JyjEWb1vMhf+5kJvr38zH130clWMYoUk6nESj9xqxP3k/83vOp8wZubdTY3qOIFNuyOvumTp+oO9pW2bkec4840yebv0036/8nh9W/uC3OVlOZnWFToV65erxZKsnGbVgFHeNvYvJqyZbt9Js4qEJD/HXrr8Yff3oXO0EMuJ06iPWcmVERK+mvTin1Dk8OulRdh7c6bc5WcYxPca7ie9yydmXUL9c/age66nWT9GzUU++Xvo17T9uT+XXKvPI948wZ+Mcmx0uSoycP5KPF3zMwEsG0qZaG7/NiSqZCg2l2VFknapWzWJ7MuSUQ0ObN8Mzz8C//gUlS2a9YUa6fLX0K2747AYAzi19Ls0qN6N5peY0q9yMC8pfQHxsvM8WZp4JKyZw1eir+PTGT+lSr0vGO2QBB5MPMmHFBEYvGs23y7/lyNEj1Cxdk1vr30rX87tSu0ztLDuWqrJ8x3JmrJ/BjPUzWLBtAW2rt6Vvi755+usYYNnfy2j0XiMaV2zMT3f+RGxMrN8mnTan3EYgIkmE1hQSoLCqZvvkNKfsCL74Arp2hcqV4dNPoWnTrDfOSJdZG2YxZc0UZm2YxeyNs49Pz1gwtiCNKjaiWaVmNKvUjOaVm1O1RNUc313yqtFX8fvm31n3yLpTlpQ4HXYf2s3XS7/mfwv/x8+rf0ZRLjrrIm6tfyu31L+FSsVDSXuFZ/+R/czZOIeZG2YyY/0MZm6YebwGV7JQSWqXqc3sDbMpXKAwDzZ+kEdbPkqFohWicWq+cijlEM2HN2fD3g3M7zk/09cxp5JljcU5gdNqLJ41C26+2dUOXnkFeveGHP6yyauoKuv3rndOYcNsZm+czdzNc4+rbpYvUj5NraFJxSYUK1jMZ6tPsGrXKmr+uybPXPIMz132nN/msDlpM58u/pTRC0eTsCkBQWhTrQ231r+VG+veSKnCpdLkV1XW7ll7/Gt/5oaZzN8yn6Pq2h7qlKlDyyotaVG5BS2rtOS8MucRIzEs3b6UF6a9wJhFY4iPjef+RvfTr2W/PPOyBOg1oRdvJ7zNt12/5apaV/ltTpZhjiCQnTuhWzf45hvo3Bk+/BBKlcpwNyP6JB9NZsHWBczeOPt4rWH5juUACEK9cvW4suaVdKrdieaVm2dLl7twPP7j47w681XWPrI2x70EV+xYwZhFY/jfwv+xfMdyCsQU4Mpzr+SGOjewbf82ZmyYwcz1M9m8bzMAReOL0qxSs+Mv/WaVm1G6cOkMj/HS9JcYOX8ksTGxdL+wO0+0eoKqJbI9WpylfL30a67/7Hr6NO/Dqx1e9ducLMUcQTCq8Prr8PjjUKkSfPaZhYpyKDsP7mTOxjnM3jCbaeum8evaX0k5lkL5IuW5ptY1dKrdiXbntKNQXKFss+lg8kEqv1aZy6pdxhddvsi242YWVeX3zb8zeuFoPln8CZuSnOZjjVI1aFGlBS0rt6RFlRbUL1efuJhTi/Ku3rWawdMHM2LeCAC6NezGk62epHqp6ll2HtnF2t1rafifhtQoVYMZ987Ile1W6WGOIByzZ7tQ0aZNMGQIPPywhYpyOLsP7WbiiomMWzaOCSsmkHQkiSIFitChZgc6n9eZq2pdleHX7Ony33n/pdu4bvx8589cVj0jEd6cwdFjR5m3ZR6Vi1emfNHyWV7+uj3rGPLbEIb/PpyUYync0eAOnmr1FOeeeW6WHysaJB9N5tL/XsrCrQv5/f7fqVm6pt8mZTnmCNJj1y64+24YNw46dYIRIyxUlEs4nHKYKWumMPbPsYxfPp5NSZuIlVguOfsSOp3XKWpibU3fb8q+I/tY/ODiHN+gnd1sStrEK7+9wn/m/ofDRw/TtX5XBrQeQJ2ydfw2LV0G/DSAF6e/yOjrR9P1/K5+mxMVzBFkhCq88YYLFVWs6HoVNWuWtccwosoxPcbcTXMZ++dYxi0bx+LtiwFoUL4BnWt3ptN5nWhYoeFpv7gTNibQdHhT3uz4Jr2a9soK0/MkW/dtZejMobyT8A4Hkg9wU72beLr105xf/ny/TTuJyasmc/nHl3PPhfcw/NrhfpsTNcwRRMqcOS5UtGEDvPwy9OljoaJcysqdKxn35zjGLhvLb+t+Q1GqlqhK66qtaVqpKc0qNaNhhYYUjCuYqXLvHnc3ny/+nE2PbspyXaG8yN8H/ua1ma/x5pw3STqSxK3n38rgtoOpUqJKxjtnA1v3baXBsAaULlyahPsSKBKf0TQruRdzBJlh1y645x4YOxauvdaFikpHN+ZsRJft+7fz7fJv+XbFt2l6y8THxtOwQkOaVmxKs8puDEPN0jXD1hp2HNhB5dcq061BtzytphoNdh3cxdCZQxk6cyiC0K9lPx6/+HFfX7zH9Bgd/9eRqWunMqf7nBxZW8lKzBFkFlV480147DE46ywXKmre/PTLPXoUYnP/CMXczoa9G5i9YbbrjbRxNombEtmfvB+AUoVKHa8xNKvcjKaVmh4fRfuvGf+i34/9WPjAwqhLSuRV1u1ZR//J/RmzaAwVi1XkpbYvcfsFt/vSFfjl6S/T/6f+DLtqGPc3vj/bj5/dmCM4VRISoEsXFyoaPBj69j05VHTwIGzdClu2nPgb+DtwW0oK9O8PAwZAfN7qmpabOXrsKEu2L2H2xtnOQWyaw6Jti47rxZ9T6hyaVWrGtHXTOKfUOfza7VefLc79zFg/gz4/9GHOxjk0rtiY1zu8zsVVL862489cP5PWI1pzXZ3r+OzGz/JFo785gtNh924XKvr6a7j0UihbNu3Lfe/e0PuVKQPly0OFCm4pX945lM8+g/r1Xcipcch7YuQA9h3Zx9xNc4/XGmZvnM2GvRsYe/NYOtXu5Ld5eYJjeowxC8fwxOQn2Ji0kS71uvByu5ejPi3n7kO7aTjMdRz44/4/KFkof2iP+eYIROQK4A3cnMXDVXVwmHxNgFnAzaqa7ggdX+YjSA0VDRkCRYueeLEHvuQDf5crBwXCaM98+y3cf79zJv36wbPPQqHsGwxlnDr7juyjaHxRv83Ic+w/sp9/zfgXQ2YM4eixo/Rt0ZcnWz0ZFUkRVeWmz29i3LJxTLt7Gs0rZ0HIN5eQniNAVaOy4F7+fwHnAPHAfKBumHw/AxOAGzMqt1GjRprr2bVL9d57VUG1dm3VGTP8tsgwfGf9nvV6x1d3KIPQ8q+U1+Fzh2vK0ZQsPca7Ce8qg9CXp7+cpeXmBoBEDfNejWYLTVNgpaquUtUjwCdAqDr1/wO+BLZF0ZacRcmSMHw4/PADHDgAF1/s2h8OHPDbMsPwjcrFKzPyupHM7j6bGqVr0P2b7jR+vzFT1kzJkvIXbF3AI98/QocaHXis5WNZUmZeIWqhIRG5EbhCVbt763cAzVS1V0CeSsBo4P+AD4BvNURoSER6AD0Aqlat2mjt2rVRsdkXkpLgiSfg3XehRg344ANok7cnwTCMjFBVPlv8GY9Pfpx1e9ZxXe3reKX9K9QoXQNV5UDyAfYc3sOeQ3vYe3jv8d97Dnvrgb+9tIXbFhIjMczvOZ9yRcr5fYrZji9tBCJyE9AhyBE0VdX/F5Dnc2Coqs4SkY8I4wgCybNzFk+ZAvfeC6tWwUMPuV5KRS0ebeRvDiYf5NWZr/LS9Jc4fPQwxeKLsffw3uNy2elRLL4YxQsWp0ShEpQoWIJShUvxzCXP5Kt2gUD8cgQtgEGq2sFbfxJAVV8KyLOaE1NelgEOAD1UdWy4cvOsIwDYvx+eftrJXVSt6sJH7dr5bZVh+M7mpM28Put1DiQfoEShEu4FX7BEmt+BL/2i8UXzxKxiWYlfjiAOWA60BTYCCcCtqro4TP6PyM81gkB++811WV2+HLp3d9Nrlijht1WGYeRi0nMEUZtqUlVTRKQX8AOuZ9CHqrpYRHp66cOidexcz8UXw7x5MGiQcwITJ8J778GVV4bOf/Qo7NgB27fDtm3ub+oSuH74MIweDdVzn1a8YRjRwwaU5XQSEpxM9uLFcMMNboxC4Mt92zY361qo+yjidJLKlnVLQgLcdBOMHJn952EYhq/YyOLczuHD8MILMHQonHHGiRd72bLOMYRbP/NMiAuo9D32GLz2mnMqtWv7dz6GYWQ75ggMx/btLix0zTUwZozf1hiGkY2k5wj8m/3byH7KloXevZ2a6qJFfltjGEYOwRxBfuOxx9z4hGef9dsSwzByCOYI8hulS7uZ1776Cv74w29rDMPIAZgjyI/06eP0jqxWYBgG5gjyJyVLuhDRN9+4eZoNw8jXmCPIr/Tu7bqXDhzotyWGYfiMOYL8SrFi8PjjTgr7t9/8tsYwDB8xR5CfeeghNwDNagWGka8xR5CfKVIEnnwSfv7ZyWAbhpEvMUeQ37n/fqhYEZ55JrRekWEYeR5zBPmdwoXhqadg+nSYPNlvawzD8AFzBIab86BKFasVGEY+xRyBAQULOicwezZMmOC3NYZhZDPmCAxHt25OmXTgQKsVGEY+wxyB4ShQwDmB33+HceP8tsYwjGzEHIFxgttvh1q1nEM4dsxvawzDyCai6ghE5AoRWSYiK0Wkf4j0TiKyQETmiUiiiLSKpj1GBsTFOSG6hQvhiy/8tsYwjGwiajOUiUgssBxoD2wAEoCuqrokIE9RYL+qqohcAHymqunOoWgzlEWZo0fhggtcO8HChRAb67dFhmFkAX7NUNYUWKmqq1T1CPAJ0Ckwg6ru0xOeqAhgrZR+ExsLgwbB0qXwySd+W2MYRjYQTUdQCVgfsL7B25YGEblORP4EvgPuCVWQiPTwQkeJ27dvj4qxRgA33OBqBc89BykpfltjGEaUiaYjkBDbTvriV9WvvXBQZ+CfoQpS1fdUtbGqNi5btmwWm2mcREwM/OMfsGIFfPyx39YYhhFloukINgBVAtYrA5vCZVbVqUANESkTRZuMSLn2WmjUyDmE5GS/rTEMI4pE0xEkAOeKSHURiQduAcYHZhCRmiIi3u+LgHhgRxRtMiJFxDmBNWtgxAi/rTEMI4pEzRGoagrQC/gBWIrrEbRYRHqKSE8v2w3AIhGZB7wN3KzR6sZkZJ6OHaF5c3j+eTh82G9rDMOIElHrPhotrPtoNjN5MrRvD2+95SayMQwjV+JX91EjL9C2LVxyCbzwAhw86Lc1hmFEAXMERvqkthVs3gzDhvltjWEYUcAcXjHChwAAFZRJREFUgZExbdq4msHgwbBzp9/WGIaRxZgjMCLjhRecE6hb1+kQ5bK2JcMwwmOOwIiMZs1gzhyoVAluugmuuw42bvTbKsMwsgBzBEbkXHihm8XslVdg0iRXOxg2zCSrDSOXY47AyBxxcfDYY06ZtEkTeOAB14bw559+W2YYxilijsA4NWrUgB9/dKOOFy+GBg3cwLMjR/y2zDCMTGKOwDh1RNxcx0uXwvXXwzPPOH2i2bP9tizrUYW//jJHZ+RJzBEYp0/58jBmDHzzDezeDS1awMMPw759fluWNezcCV27Qs2acM458PLLsGuX31YZRpZhjsDIOq6+2oWJHnoI3nwT6tWDiRP9tur0+PFHOP98+PJL1zZSpw707w9VqkDv3rBqld8WGsZpY1pDRnSYMQO6d3dho1tvhddfh9w0l8SBA+6F/+ab7uU/ahRcdJFLW7AAXn0VRo92U3t27gyPPgotW/prs5EuycnJbNiwgUOHDvltSlQpVKgQlStXpkCBAmm2p6c1hKrmqqVRo0Zq5BIOHVIdNEi1QAHVM89UHTFCde9ev63KmMRE1dq1VUH14YdVDxwInW/jRtWnnlItVcrlbd5c9bPPVJOTs9deIyJWrVql27dv12PHjvltStQ4duyYbt++XVetWnVSGpCoYd6rFhoyokfBgvDsszBvHtSqBXffDcWLQ7VqcM018OST7qt6wYKcIXOdkuJGUDdvDklJLiz0+utQuHDo/BUruvzr1zt11u3boUsXOPdct19SUvbaH20OHMjVI8oPHTrEmWeeiTcFSp5ERDjzzDMzXesxR2BEn7p1Yfp0mDABXnzRhVDWroWhQ+G221zX06JFXZvCzTfDP/8JY8fCypUu9JId/PWXU1l9+mm48UY3TqJdu8j2LVLEtYssWwZff+3aD/r0gcqVoV8/5yhyMzt3wv/7f86J168PH3wAuTS8kpedQCqnco7WRmD4x5Ejbl7kRYvcizf1b2ADbOHCzkHUr+8abdu0cSOcY7LoG0YVhg93L+64OHj3XddD6HSZM8e1I3zxhVvv0sW1IzRqdPplZxcpKfCf/8DAga432O23u9rdggVQrpxzfg88kDvaflRZOncudapXdzXVggXd/c6jjmHp0qXUqVMnzTZrIzByF0lJqrNnq37wgWqfPqrt2qlWqODi8KBapozqzTerDh+uunbtqR9nyxbVa65xZf7f/6muW5d155DKmjWqffuqFivmjtO4seo776ju2pX1x8pKfvxRtV69E9dmwQK3/dgx1Z9+Ur3ySpdWqJBqjx6qS5b4a296JCer3nOPLpk4UTUh4cQyd67qokWqK1a4e791q+qePa5tKwrtCLt27dK333470/t17NhRd2XyeVkS4n6QThuB7y/2zC7mCPIxmzapfvyx6p13qp511gnHUKuW6kMPqY4d6/6RI2HcONWyZVULFlR97TXVo0eja/uePaqvv656wQXO5oIFVbt2dS/caB87M6xcqdqpk7OxenXVr78O/1JcssQ5gUKFXP4rr3ROIic1xu7ff9zZL5kxwzX879rlPgLWrlVdvlx14ULXQSDQSSQmOue3bJlz5ps3u/1OoyPA6tWrtV69eidtT0lJOZ0zDElmHUFUQ0MicgXwBhALDFfVwUHptwFPeKv7gAdUdX56ZVpoyACcC1iyxDXo/vgjTJniGjNjY51Savv2cPnl0LSpCwGkkpTkwkAffAANG7puofXqZa/df/wBH34I//ufC7lUrepGaHfrBtWrZ58tgSQluYbv116DAgVgwAB3nQoVynjf7dtdSO3tt2HbNtfm07cv3HILxMdH3/Zw7NwJ117rujK//TZLL730eLjkke8fYd7/b+/sg6OqsgT+O8TwEQKSj8kY+XbZwh01BmsQERhhZhAiFOLWLOO4CFW6i1YtNThbIDCWikPVwKiLqLXq4C67+DWrOCqO4BI/+HCLMICYMBEcA4pliEIICkYICJz947xAE7pDvl53hz6/qlf9+r773j19+vY9fc+999wvS8/Mr2oBFCOP+rT6dlLE6lPHjme5JwsvKmTx2MUxxbn55ptZsWIFAwcOJD09nczMTPLz8yktLWX79u1MnDiRzz//nLq6OmbMmMG0adMA6NevH1u2bKG2tpaioiKGDx/Ohg0b6NmzJytWrKBLlMkMzXUNhWYIRCQN+BgYDVQCm4FfqOr2iDzXAjtU9SsRKQLmqeqQxp7rhsCJytGjUFJy2jBs2WI/3u7dYdQoMwz1g7i7d8Ps2TBvXmIbqro6WLHCjMJbb5m8o0bBbbdZyI6MjPBlOHkSnn3W1kx8+SVMmQILFtiMqOZSV2fGbdEiM9L5+TbIfMcdkJ3d9rI3RmUljBljEw6efx5+9rMzGseohqAx6g3Cd9/ZAfano2PHU380zmUIdu/ezfjx4ykvL2ft2rWMGzeO8vJy+gfG/8CBA2RnZ3PkyBEGDx7MunXryMnJOcMQDBgwgC1btlBYWMikSZOYMGECkydPPquspBkjAIYCqyPezwXmNpI/C9hzrue6a8hpEjU1qsuXm+uiX7/TbqT+/VXfey/R0p3NZ5+pzp+vesklJmf37ib7xo3huVo2bFAdPNjKGzLEymoLTp5UffNN1dGj7dkZGea6q6hom+efi+3bVXv3Nh2++25EchuNYxw7Zm7KsjJzI5WVmevoHG6jSNfQmjVrdOTIkWdcv//++7WgoEALCgq0e/fuWlJSoqqqffv21erqav300091wIABp/IvXLhQ58+fH7Ws5rqGwpw+2hOInDdXGaTF4nagnccjcJKG7GybBvr739sspIoKm8FTVgbDhydaurPp08emrlZUmJtr4kT7p37NNTZj6uGHYe/etilrzx649VabxltZCc88Y+6TIY12xpuOCIwda3tWbNtmM6aWLLG1JFOnWplhUVJi3++xY7BunfWw2pr0dOvtXHGFReHt1Mk+U1mZ9TYPH27SY7p27XrqfO3atbz99tuUlJRQVlbGoEGDoq4F6NSp06nztLQ0jh8/3uqPA3DBubO0mGjzsqL6oURkFGYIov5CRWQaMA2gT58+bSWfkyqIWMC4AQMSLcm56dDBpshed52Ft3jpJXMdzZpl7qzcXMjKMkMX67VhWlaWuTCOHLG1GwsW2PqMe+4xl1BmZnif54orLFT5b39r4w+PPgrLl9vnuftuW4PRVqxcabvn9ewJq1dbgMAwETmt38OHbaykpgb27zed5uVBjx6nxhK6devGNzEWGR48eJCsrCwyMjL4aMcONm7cePqZJ07YOpevvrIpvSEQpiGoBHpHvO8FVDXMJCIFwH8ARapaE+1BqroEWAI2RtD2ojpOEtK9u8Vrqo/ZtHw5VFVZg3DggPn0d+yw84MHG39W167WIH3zjY0/PPRQ+A1lJPn58OCDtu5g7lz4zW/g6afNQEyZ0vp1IcuWwe232wSAVausEY4nGRnQt68Zof37rQH/5BPrPeTlQW4uOTk5DBs2jMsvv5wuXbrw/bw8a+zr6hhbUMBThw5RMHAgA3v35prLLrOFiHl5Zgi++87W1IS07iHMweILsMHinwB7sMHiW1T1w4g8fYB3gSmquqEpz/XBYseJwokTNgOp3khEe62ttZXbP/5xoqU1V9SvfmUL7wYNsgHmkSOb/xxVM2qzZ9tK8FdegW7domaNNoAaGqpmnPftg0OHrAHPzrYB5ro6OxrubdGxo83Sijw6dbL0ZhqA5g4Wh9YjUNXjIjIdWI1NH12qqh+KyJ3B9aeA+4Ac4IlgWfTxWII6jtMIaWmQk2NHe+Daa82f/+KL1oiPGmXjIg8+aLGamsLJkxYa/JFHbKrqsmWJnQUWiYi5hXr0MJdcdbX1FMAa+MzMsxv8tLTEiRtWjyAsvEfgOOcZR45YY75ggU0Dnj7ddrvLyop9z7FjFsTwhRdsX4hHHjmneymuPYJoRK5FCJnm9gg86JzjOImlSxf49a9txtTUqRa5dcAAeOyx03P2I6mttei1L7xgxmPx4raLPRUmIkkb26gdaM9xnJTgootsAPmDD2zcYMYMmzr7pz+d/jddXW1jHO+8Y7Op5sxJ2sa1PeGGwHGc5OLKK22l9RtvWCM/YYKtDF+1CoYNsyi1r71mriGnTXBD4DhO8iEC48ZZWPLHH7dewrhxNuD69tu2P7bTZrghcBwneUlPt8HjnTttRtGGDe12b+ivv/6aJ554okX3Ll68mMNNXLHcEtwQOI6T/GRl2WrkSy9NtCQtJpkNQZgrix3HcZKTu+6y3dbaksJCm8EUgzlz5rBr1y4KCwsZPXo0eXl5vPTSSxw9epSbbrqJBx54gG+//ZZJkyZRWVnJiRMnuPfee9m7dy9VVVWMGjWK3Nxc1qxZ07Zy44bAcRwnLixcuJDy8nJKS0spLi7m5ZdfZtOmTagqEyZMYP369VRXV3PxxRezcuVKwGIQXXjhhSxatIg1a9aQm5sbimxuCBzHST0a+eceD4qLiykuLmbQoEEA1NbWUlFRwYgRI5g5cyazZ89m/PjxjBgxIi7yuCFwHMeJM6rK3LlzueOOO8669v7777Nq1Srmzp3L9ddfz3333Re6PD5Y7DiOEwciw1CPGTOGpUuXUltbC8CePXvYt28fVVVVZGRkMHnyZGbOnMnWrVvPujcMvEfgOI4TByLDUBcVFXHLLbcwdOhQADIzM3nuuefYuXMns2bNokOHDqSnp/Pkk08CMG3aNIqKisjPzw9lsNiDzjmOkxIkPOhcHPGgc47jOE6zcEPgOI6T4rghcBwnZWhvrvCW0JLP6IbAcZyUoHPnztTU1JzXxkBVqampoXPnzs26z2cNOY6TEvTq1YvKykqqq6sTLUqodO7cmV69ejXrHjcEjuOkBOnp6fTv3z/RYiQlobqGRGSsiPxVRHaKyJwo1y8VkRIROSoiM8OUxXEcx4lOaD0CEUkD/h0YDVQCm0XkdVXdHpHtAPBLYGJYcjiO4ziNE2aP4Gpgp6p+oqrHgP8BbozMoKr7VHUzEGWHasdxHCcehDlG0BP4POJ9JTCkJQ8SkWnAtOBtrYj8tYUy5QL7W3hvPEh2+SD5ZXT5WofL1zqSWb6+sS6EaQgkSlqL5m2p6hJgSevEARHZEmuJdTKQ7PJB8svo8rUOl691JLt8sQjTNVQJ9I543wuoCrE8x3EcpwWEaQg2A38rIv1FpCNwM/B6iOU5juM4LSA015CqHheR6cBqIA1YqqofisidwfWnROQiYAvQHTgpIncBP1DVQyGJ1Wr3Usgku3yQ/DK6fK3D5WsdyS5fVNpdGGrHcRynbfFYQ47jOCmOGwLHcZwU57w0BE0IbSEi8lhwfZuIXBVH2XqLyBoR2SEiH4rIjCh5RorIQREpDY7wd68+s/zdIvKXoOyztoNLsP4GRuilVEQOBWNLkXnirj8RWSoi+0SkPCItW0TeEpGK4DUrxr2N1tcQ5XtIRD4KvsNXRaRHjHsbrQ8hyjdPRPZEfI83xLg3Ufp7MUK23SJSGuPe0PXXalT1vDqwgeldwCVAR6AMG4COzHMD8Ca21uEa4M9xlC8fuCo47wZ8HEW+kcAbCdThbiC3kesJ01+U7/pLoG+i9Qf8CLgKKI9IexCYE5zPAX4X4zM0Wl9DlO964ILg/HfR5GtKfQhRvnnAzCbUgYTor8H1fwPuS5T+Wnucjz2Cc4a2CN4/o8ZGoIeI5MdDOFX9QlW3BuffADuwVdjtiYTprwE/AXap6mcJKPsMVHU9FjsrkhuBZcH5MqLH1GpKfQ1FPlUtVtXjwduN2FqfhBBDf00hYfqrR0QEmAT8oa3LjRfnoyGIFtqiYUPblDyhIyL9gEHAn6NcHioiZSLypohcFlfBbAV4sYi8H4T3aEhS6A9bmxLrx5dI/dXzfVX9AuwPAJAXJU+y6PI2rJcXjXPVhzCZHriulsZwrSWD/kYAe1W1Isb1ROqvSZyPhqApoS3aLPxFSxGRTOCPwF169rqJrZi740rgceC1eMoGDFPVq4Ai4F9E5EcNrieD/joCE4DlUS4nWn/NIRl0eQ9wHHg+RpZz1YeweBL4G6AQ+AJzvzQk4foDfkHjvYFE6a/JnI+GoCmhLRIa/kJE0jEj8LyqvtLwuqoeUtXa4HwVkC4iufGST1Wrgtd9wKtY9zuSZAgfUgRsVdW9DS8kWn8R7K13mQWv+6LkSXRdnAqMB/5RA4d2Q5pQH0JBVfeq6glVPQk8HaPcROvvAuDvgRdj5UmU/prD+WgImhLa4nVgSjD75RrgYH0XPmwCf+J/AjtUdVGMPBcF+RCRq7HvqSZO8nUVkW7159iAYnmDbAnTXwQx/4UlUn8NeB2YGpxPBVZEyZOwUCwiMhaYDUxQ1cMx8jSlPoQlX+S4000xyk10KJufAh+pamW0i4nUX7NI9Gh1GAc2q+VjbDbBPUHancCdwblgm+bsAv4C/DCOsg3Huq7bgNLguKGBfNOBD7EZEBuBa+Mo3yVBuWWBDEmlv6D8DKxhvzAiLaH6w4zSF9jeGpXA7UAO8A5QEbxmB3kvBlY1Vl/jJN9OzL9eXw+faihfrPoQJ/meDerXNqxxz08m/QXp/11f7yLyxl1/rT08xITjOE6Kcz66hhzHcZxm4IbAcRwnxXFD4DiOk+K4IXAcx0lx3BA4juOkOG4IHAcQkQVB1NKJYUWwbIIMa0Wk3W187rR/3BA4jjEEi/l0HfBegmVxnLjihsBJaYKY/NuAwUAJ8E/AkxJlDwMR+Z6I/FFENgfHsCB9nog8KyLviu098M9BugTPLw/i0f884ll3B2llIrIwoph/EJFNIvKxiIwI9cM7TkBom9c7TntAVWeJyHLgVuBfgbWqOixG9keBR1T1/0SkD7Aa+LvgWgG2N0NX4AMRWQkMxQKmXQnkAptFZH2QNhEYoqqHRSQ7oowLVPVqsU1Y7sdCGDhOqLghcBwLBV4KXApsbyTfT4EfBGGMALrXx5EBVqjqEeCIiKzBAosNB/6gqiewAHTrsJ7HdcB/aRDfR1Uj49zXByF8H+jX2g/mOE3BDYGTsohIIRYrphewH4thJMGWg0ODhj2SDtHSA8PQMFaLEj1EMkF6rNguR4PXE/jv04kTPkbgpCyqWqqqhQTbhQLvAmNUtTCKEQAoxgLaAacMST03ikhnEcnBtsrcDKwHfi4iaSLyPWy7w03Bc24TkYzgOZGuIceJO24InJQmaKC/Uot5f6mqNuYa+iXww2DHrO1YxNN6NgErsWin89Vi0L+KRc4sw4zM3ar6par+LxZNc0vQ+5jZ5h/McZqBRx91nFYiIvOAWlV9ONGyOE5L8B6B4zhOiuM9AsdxnBTHewSO4zgpjhsCx3GcFMcNgeM4TorjhsBxHCfFcUPgOI6T4vw/nWzyCSPaptkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_model_loss([idx_sorted[i] for i in range(3)],\"Worst cases\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Plot different architectures in a 2D space representating the different architectures with the minimum loss as the size**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_loss_map(results_list)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Summary"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this course, we tackled the predictive maintenance problem using different approaches. Mainly, we used a machine-learning-based approach called XGBoost to classify the defective hard drive samples. Next, we used recurrent neural networks to predict hard drive feature sequences and indicate whether the timeseries ends in a fail state or not. Finally, we used an anomaly detection approach to classify samples into defective/normal bins.\n",
    "One important factor affecting the outcome of all the models experimented in this course, is the number of data sequences. While we had the liberty of choosing among a dataset of 6M samples, the defective hard drives were significantly less. This challenge affected the outcome of the models discussed so far. To overcome similar issues in industrial applications, the reader is encouraged to investigate data augmentation techniques to increase the training set."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.10"
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 },
 "nbformat": 4,
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}