{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# **Amazon Lookout for Equipment** - Getting started\n",
    "*Part 4 - Model evaluation*\n",
    "\n",
    "## Initialization\n",
    "---\n",
    "This repository is structured as follow:\n",
    "\n",
    "```sh\n",
    ". lookout-equipment-demo\n",
    "|\n",
    "├── data/\n",
    "|   ├── interim                          # Temporary intermediate data\n",
    "|   ├── processed                        # Finalized datasets\n",
    "|   └── raw                              # Immutable original data\n",
    "|\n",
    "├── getting_started/\n",
    "|   ├── 1_data_preparation.ipynb\n",
    "|   ├── 2_dataset_creation.ipynb\n",
    "|   ├── 3_model_training.ipynb\n",
    "|   ├── 4_model_evaluation.ipynb               <<< THIS NOTEBOOK <<<\n",
    "|   ├── 5_inference_scheduling.ipynb\n",
    "|   ├── 6_visualization_with_quicksight.ipynb\n",
    "|   └── 7_cleanup.ipynb\n",
    "|\n",
    "└── utils/\n",
    "    └── lookout_equipment_utils.py\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Notebook configuration update"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
      "\u001b[0m"
     ]
    }
   ],
   "source": [
    "!pip install --quiet --upgrade tqdm lookoutequipment"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import boto3\n",
    "import config\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.ticker as mtick\n",
    "import numpy as np\n",
    "import os\n",
    "import pandas as pd\n",
    "import sys\n",
    "\n",
    "# SDK / toolbox for managing Lookout for Equipment API calls:\n",
    "import lookoutequipment as lookout"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### AWS Look & Feel definition for Matplotlib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from matplotlib import font_manager\n",
    "\n",
    "# Load style sheet:\n",
    "plt.style.use('../utils/aws_matplotlib_template.py')\n",
    "\n",
    "# Get colors from custom AWS palette:\n",
    "prop_cycle = plt.rcParams['axes.prop_cycle']\n",
    "colors = prop_cycle.by_key()['color']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "TMP_DATA       = os.path.join('..', 'data', 'interim', 'getting-started')\n",
    "PROCESSED_DATA = os.path.join('..', 'data', 'processed', 'getting-started')\n",
    "LABEL_DATA     = os.path.join(PROCESSED_DATA, 'label-data')\n",
    "TRAIN_DATA     = os.path.join(PROCESSED_DATA, 'training-data', 'centrifugal-pump')\n",
    "REGION_NAME    = boto3.session.Session().region_name\n",
    "MODEL_NAME     = config.MODEL_NAME\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  Training period | from 2019-01-01 00:00:00 to 2019-07-31 00:00:00\n",
      "Evaluation period | from 2019-08-01 00:00:00 to 2019-10-27 00:00:00\n"
     ]
    }
   ],
   "source": [
    "# Configuring time ranges:\n",
    "training_start   = pd.to_datetime('2019-01-01 00:00:00')\n",
    "training_end     = pd.to_datetime('2019-07-31 00:00:00')\n",
    "evaluation_start = pd.to_datetime('2019-08-01 00:00:00')\n",
    "evaluation_end   = pd.to_datetime('2019-10-27 00:00:00')\n",
    "\n",
    "print(f'  Training period | from {training_start} to {training_end}')\n",
    "print(f'Evaluation period | from {evaluation_start} to {evaluation_end}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Loading original datasets for visualization purpose"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Let's load all our original signals (they will be useful later on):\n",
    "all_tags_fname = os.path.join(TRAIN_DATA, 'sensors.csv')\n",
    "all_tags_df = pd.read_csv(all_tags_fname)\n",
    "all_tags_df['Timestamp'] = pd.to_datetime(all_tags_df['Timestamp'])\n",
    "all_tags_df = all_tags_df.set_index('Timestamp')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model evaluation\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The [**DescribeModel**](https://docs.aws.amazon.com/lookout-for-equipment/latest/ug/API_DescribeModel.html) API can be used to extract, among other things, the metrics associated to the trained model. Here are the different fields available when calling this API:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['ModelName',\n",
       " 'ModelArn',\n",
       " 'DatasetName',\n",
       " 'DatasetArn',\n",
       " 'Schema',\n",
       " 'LabelsInputConfiguration',\n",
       " 'TrainingDataStartTime',\n",
       " 'TrainingDataEndTime',\n",
       " 'EvaluationDataStartTime',\n",
       " 'EvaluationDataEndTime',\n",
       " 'RoleArn',\n",
       " 'Status',\n",
       " 'TrainingExecutionStartTime',\n",
       " 'TrainingExecutionEndTime',\n",
       " 'ModelMetrics',\n",
       " 'LastUpdatedTime',\n",
       " 'CreatedAt',\n",
       " 'ResponseMetadata']"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lookout_client = boto3.client('lookoutequipment')\n",
    "describe_model_response = lookout_client.describe_model(ModelName=MODEL_NAME)\n",
    "list(describe_model_response.keys())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `ModelMetrics` field above is a dictionnary that follows this format:\n",
    "\n",
    "```json\n",
    "{\n",
    "    'labeled_ranges': [\n",
    "        {'start': '2019-08-08T00:00:00.000000', 'end': '2019-08-09T00:00:00.000000'},\n",
    "        {'start': '2019-08-18T00:00:00.000000', 'end': '2019-08-19T00:00:00.000000'},\n",
    "        {'start': '2019-08-28T00:00:00.000000', 'end': '2019-08-29T00:00:00.000000'},\n",
    "        {'start': '2019-09-07T00:00:00.000000', 'end': '2019-09-08T00:00:00.000000'},\n",
    "        {'start': '2019-09-17T00:00:00.000000', 'end': '2019-09-18T00:00:00.000000'},\n",
    "        {'start': '2019-09-27T00:00:00.000000', 'end': '2019-09-28T00:00:00.000000'},\n",
    "        {'start': '2019-10-07T00:00:00.000000', 'end': '2019-10-08T00:00:00.000000'},\n",
    "        {'start': '2019-10-17T00:00:00.000000', 'end': '2019-10-18T00:00:00.000000'}\n",
    "    ],\n",
    "    'labeled_event_metrics': {\n",
    "        'num_labeled': 8,\n",
    "        'num_identified': 8,\n",
    "        'total_warning_time_in_seconds': 668040.0\n",
    "    },\n",
    "    'predicted_ranges': [\n",
    "        {\n",
    "            'start': '2019-08-08T00:42:00.000000',\n",
    "            'end': '2019-08-08T01:48:00.000000',\n",
    "            'diagnostics': [\n",
    "                {'name': 'centrifugal-pump\\\\Sensor0', 'value': 0.05218326564181105},\n",
    "                {'name': 'centrifugal-pump\\\\Sensor1', 'value': 0.023636079094576},\n",
    "                {'name': 'centrifugal-pump\\\\Sensor2', 'value': 0.03825258734479793},\n",
    "                {'name': 'centrifugal-pump\\\\Sensor3', 'value': 0.023349531399873558},\n",
    "                \n",
    "                ...\n",
    "                \n",
    "                {'name': 'centrifugal-pump\\\\Sensor20', 'value': 0.04989340342761552},\n",
    "                {'name': 'centrifugal-pump\\\\Sensor21', 'value': 0.033976174168938014},\n",
    "                {'name': 'centrifugal-pump\\\\Sensor22', 'value': 0.046622167459421035},\n",
    "                {'name': 'centrifugal-pump\\\\Sensor23', 'value': 0.044698573526762944}\n",
    "            ]\n",
    "        },\n",
    "        \n",
    "        ...\n",
    "        \n",
    "    ],\n",
    "    'unknown_event_metrics': {\n",
    "        'num_identified': 8,\n",
    "        'total_duration_in_seconds': 4200.0\n",
    "    }\n",
    "}\n",
    "```\n",
    "\n",
    "The `labeled_ranges` contains the label provided as an input while the `predicted_ranges` contains all the predicted ranges where Lookout for Equipment detected an anomaly. Each predicted range contains a `diagnostics` field with a percentage associated to each sensor available in the dataset. During the training, Lookout for Equipment learns the relationship between the sensors that denotes a normal behavior. When this normal relationship is broken, the service considers that it detected an an anomalous event. It then proceeds with calculating which sensors are indicating that the asset is no longer operating normally. You can read this diagnostic as a feature importance output of the model: the percentage associated to a given sensor corresponds to the magnitude of impact (*importance*) this sensor has with regards to a given anomaly.\n",
    "\n",
    "Let's use the following utility function get these results into two dataframes (labeled and predicted):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "LookoutDiagnostics = lookout.LookoutEquipmentAnalysis(model_name=MODEL_NAME, tags_df=all_tags_df)\n",
    "LookoutDiagnostics.set_time_periods(evaluation_start, evaluation_end, training_start, training_end)\n",
    "predicted_ranges = LookoutDiagnostics.get_predictions()\n",
    "labels_fname = os.path.join(LABEL_DATA, 'labels.csv')\n",
    "labeled_range = LookoutDiagnostics.get_labels(labels_fname)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Note:** the labeled range from the model Describe API, only provides any labelled data falling within the evaluation range. We use the original label data to get all of them."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's now display one of the original signal and map both the labeled and the predicted ranges on the same plot:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1728x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "custom_colors = {\n",
    "    'labels': colors[9],\n",
    "    'predictions': colors[5]\n",
    "}\n",
    "    \n",
    "TSViz = lookout.plot.TimeSeriesVisualization(\n",
    "    timeseries_df=all_tags_df,\n",
    "    data_format='tabular'\n",
    ")\n",
    "TSViz.add_signal(['Sensor0'])\n",
    "TSViz.add_labels(labeled_range)\n",
    "TSViz.add_predictions([predicted_ranges])\n",
    "TSViz.add_train_test_split(evaluation_start)\n",
    "TSViz.legend_format = {\n",
    "    'loc': 'upper right',\n",
    "    'framealpha': 0.4,\n",
    "    'ncol': 2\n",
    "}\n",
    "fig, axis = TSViz.plot(fig_width=24, colors=custom_colors)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Unpacking event details\n",
    "---\n",
    "### Single event overview\n",
    "Each detected event have some detailed diagnostics stored in JSON format. Let's unpack the event details for the first large event and plot a similar bar chart than what the console provides:\n",
    "\n",
    "![Event details](assets/model-diagnostics.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Let's get the details for one of the event:\n",
    "first_event_details = predicted_ranges.loc[1, 'diagnostics']\n",
    "first_event_details = pd.DataFrame(first_event_details).sort_values(by='value', ascending=False).reset_index(drop=True)\n",
    "first_event_details = first_event_details.sort_values(by='value')\n",
    "\n",
    "fig, ax = lookout.plot.plot_event_barh(first_event_details.iloc[0:, 0:2])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You might be curious about why Amazon Lookout for Equipment detected an anomalous event. Sometime, looking at a few of the time series is enough. But sometime, you need to dig deeper.\n",
    "\n",
    "The following function, aggregate the signal importance of every signals over the evaluation period and sum these contributions over time for each signal. Then, it takes the top 8 signals and plot two distributions: one with the values each signal takes during the normal periods (present in the evaluation range) and a second one with the values taken during all the anomalous events detected in the evaluation range. This will help you visualize any significant shift of values for the top contributing signals.\n",
    "\n",
    "You can also restrict these histograms over a specific range of time by setting the start and end arguments of the following function with datetime values:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x270 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "event_start = pd.to_datetime('2019-08-17 00:00:00')\n",
    "event_end = pd.to_datetime('2019-08-18 23:59:00')\n",
    "\n",
    "fig = TSViz.plot_histograms(freq='5min', start=event_start, end=event_end, top_n=4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "On these plots, you can see that the distributions of the sensor values when an event is detected are slightly wider than during the normal operation times."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Grouping sensors by component\n",
    "The above bar chart is already a great help to pinpoint what might be going wrong with your asset. Let's load the initial tags description file we prepared in the first notebook and match the sensors with our initial components:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>value</th>\n",
       "      <th>Component</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>centrifugal-pump\\Sensor22</td>\n",
       "      <td>0.054236</td>\n",
       "      <td>volute</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>centrifugal-pump\\Sensor20</td>\n",
       "      <td>0.054068</td>\n",
       "      <td>volute</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>centrifugal-pump\\Sensor18</td>\n",
       "      <td>0.052952</td>\n",
       "      <td>volute</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>centrifugal-pump\\Sensor21</td>\n",
       "      <td>0.050128</td>\n",
       "      <td>volute</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>centrifugal-pump\\Sensor19</td>\n",
       "      <td>0.047699</td>\n",
       "      <td>volute</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>centrifugal-pump\\Sensor23</td>\n",
       "      <td>0.047020</td>\n",
       "      <td>volute</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>centrifugal-pump\\Sensor24</td>\n",
       "      <td>0.036710</td>\n",
       "      <td>shaft</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>centrifugal-pump\\Sensor0</td>\n",
       "      <td>0.036293</td>\n",
       "      <td>pump</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>centrifugal-pump\\Sensor4</td>\n",
       "      <td>0.033996</td>\n",
       "      <td>pump</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>centrifugal-pump\\Sensor1</td>\n",
       "      <td>0.032556</td>\n",
       "      <td>pump</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         name     value Component\n",
       "29  centrifugal-pump\\Sensor22  0.054236    volute\n",
       "28  centrifugal-pump\\Sensor20  0.054068    volute\n",
       "27  centrifugal-pump\\Sensor18  0.052952    volute\n",
       "26  centrifugal-pump\\Sensor21  0.050128    volute\n",
       "25  centrifugal-pump\\Sensor19  0.047699    volute\n",
       "24  centrifugal-pump\\Sensor23  0.047020    volute\n",
       "23  centrifugal-pump\\Sensor24  0.036710     shaft\n",
       "22   centrifugal-pump\\Sensor0  0.036293      pump\n",
       "21   centrifugal-pump\\Sensor4  0.033996      pump\n",
       "20   centrifugal-pump\\Sensor1  0.032556      pump"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tags_description_fname = os.path.join(TMP_DATA, 'tags_description.csv')\n",
    "tags_description_df = pd.read_csv(tags_description_fname)\n",
    "first_event_details[['asset', 'sensor']] = first_event_details['name'].str.split('\\\\', expand=True)\n",
    "component_diagnostics = pd.merge(first_event_details, tags_description_df, how='inner', left_on='sensor', right_on='Tag')[['name', 'value', 'Component']]\n",
    "component_diagnostics.sort_values(by='value', ascending=False).head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If we group the contribution of all sensors by component we end up seeing that the volute component has a 30% contribution to this particular event, while the other components are ranging from 16 to 19%: **time to give the volute a visit?**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>value</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Component</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>impeller</th>\n",
       "      <td>0.161875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>motor</th>\n",
       "      <td>0.170943</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>shaft</th>\n",
       "      <td>0.173411</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pump</th>\n",
       "      <td>0.187667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>volute</th>\n",
       "      <td>0.306104</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              value\n",
       "Component          \n",
       "impeller   0.161875\n",
       "motor      0.170943\n",
       "shaft      0.173411\n",
       "pump       0.187667\n",
       "volute     0.306104"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "event_diagnostics = component_diagnostics.groupby(by='Component').sum().sort_values(by='value')\n",
    "event_diagnostics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# We can then plot a horizontal bar chart:\n",
    "y_pos = np.arange(event_diagnostics.shape[0])\n",
    "values = list(event_diagnostics['value'])\n",
    "\n",
    "fig = plt.figure(figsize=(12,5))\n",
    "ax = plt.subplot(1,1,1)\n",
    "ax.barh(y_pos, event_diagnostics['value'], align='center')\n",
    "ax.set_yticks(y_pos)\n",
    "ax.set_yticklabels(list(event_diagnostics.index))\n",
    "ax.xaxis.set_major_formatter(mtick.PercentFormatter(1.0))\n",
    "\n",
    "# Add the values in each bar:\n",
    "for i, v in enumerate(values):\n",
    "    ax.text(0.005, i, f'{v*100:.2f}%', color='#FFFFFF', fontweight='bold', verticalalignment='center')\n",
    "    \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conclusion\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this notebook, we use the model created in part 3 of this notebook series and performed a few visualization and diagnostics on the results obtained. You can now move forward to the next step to the **inference scheduling notebook** where we will start the model, feed it some new data and catch the results."
   ]
  }
 ],
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